Agent skill

Pp Openrouter Image

by mvanhorn in mvanhorn/printing-press-library

Every image model on OpenRouter, one key: generate, rank, estimate, and batch with a local cost ledger.

Apache-2.0Auto-check: notesAI & LLM Engineering

Install Pp Openrouter Image

skills CLI

$ npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install mvanhorn/printing-press-library pp-openrouter-image --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .claude/skills && cp -r skills-src/library/ai/openrouter-image .claude/skills/pp-openrouter-image && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "pp-openrouter-image" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/openrouter-image into .claude/skills/pp-openrouter-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-openrouter-image", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/openrouter-image

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install mvanhorn/printing-press-library pp-openrouter-image --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .agents/skills && cp -r skills-src/library/ai/openrouter-image .agents/skills/pp-openrouter-image && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "pp-openrouter-image" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/openrouter-image into .agents/skills/pp-openrouter-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-openrouter-image", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install mvanhorn/printing-press-library pp-openrouter-image --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/library/ai/openrouter-image .cursor/skills/pp-openrouter-image && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "pp-openrouter-image" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/openrouter-image into .cursor/skills/pp-openrouter-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-openrouter-image", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/mvanhorn/printing-press-library.git --path library/ai/openrouter-image

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install mvanhorn/printing-press-library pp-openrouter-image --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/library/ai/openrouter-image .gemini/skills/pp-openrouter-image && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "pp-openrouter-image" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/openrouter-image into .gemini/skills/pp-openrouter-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-openrouter-image", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install mvanhorn/printing-press-library pp-openrouter-image

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .github/skills && cp -r skills-src/library/ai/openrouter-image .github/skills/pp-openrouter-image && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "pp-openrouter-image" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/openrouter-image into .github/skills/pp-openrouter-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-openrouter-image", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install mvanhorn/printing-press-library pp-openrouter-image --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/library/ai/openrouter-image .opencode/skills/pp-openrouter-image && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "pp-openrouter-image" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/openrouter-image into .opencode/skills/pp-openrouter-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-openrouter-image", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: pp-openrouter-image
GitHub stars: 2.1k
Token cost: ~11k tokens
SKILL.md length: 4,287 words
Files: 338
Skills in repo: 506
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Every image model on OpenRouter, one key: generate, rank, estimate, and batch with a local cost ledger.

Works in 6 steps: recall before any discovery → decision tree → always read warnings → …
Phrases: generate an image
SKILL.md covers Prerequisites: Install the CLI, When to Use This CLI, Anti-triggers and Unique Capabilities, plus 7 more sections
Calls go, claude and npx; needs OPENROUTER_API_KEY

What it does

Pp Openrouter Image is an agent skill from mvanhorn/printing-press-library. Every image model on OpenRouter, one key: generate, rank, estimate, and batch with a local cost ledger. Trigger phrases: generate an image, create a picture, which image model is cheapest, estimate the cost of this image, regenerate that image, batch of images, use openrouter-image, run openrouter-image.

Its SKILL.md is about 11k tokens, which your agent loads only when the skill is triggered. The skill folder holds 342 other files (for example `.golangci.yml`, `.goreleaser.yaml` and `.manuscripts/20260803-205427-851f4b39/proofs/2026-08-03-205427-fix-openrouter-image-pp-cli-build-log.md`).

It sits in AI & LLM Engineering, covering Model routing and gateways. It works with OpenRouter. The repository describes itself as: Official library of CLIs generated by the CLI Printing Press. Endorsed, tested, and community-contributed. The licence is Apache-2.0.

When your agent uses it

Phrases: generate an image
Create a picture
Which image model is cheapest
Estimate the cost of this image

Example prompts

“/pp-openrouter-image”

Requirements

Node.js
Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

6 steps, taken from the step headings in SKILL.md.

recall before any discovery
decision tree
always read warnings
teach & after finalizing your response - always
playbooks - optional flags, automatic synthesis
playbook amend & when your debug response identifies a correction

What it can do on your machine

Read from SKILL.md and the folder at commit 76de244. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Read
- Bash
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- go
- claude
- npx
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- OPENROUTER_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pp Openrouter Image loads about 11k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 4,287 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~85
When it runs · the whole SKILL.md, loaded when a task matches: ~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
```
allowed-tools: Read, Bash
```

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mvanhorn/printing-press-library at commit 76de244, republished under its Apache-2.0 licence (© mvanhorn). 4,287 words, ~10,949 tokens.

Download SKILL.mdSave it as .claude/skills/pp-openrouter-image/SKILL.md (or your agent's skills folder). This skill also uses 337 other files; get the full folder from GitHub.

name: pp-openrouter-image
description: Every image model on OpenRouter, one key: generate, rank, estimate, and batch with a local cost ledger. Trigger phrases: `generate an image`, `create a picture`, `which image model is cheapest`, `estimate the cost of this image`, `regenerate that image`, `batch of images`, `use openrouter-image`, `run openrouter-image`.
allowed-tools: Read, Bash
author: neal-kyle
license: Apache-2.0
argument-hint: <command> [args] | install cli|mcp

OpenRouter — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the openrouter-image-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:

Install via the Printing Press installer. It defaults binaries to $HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:
bash
```
npx -y @mvanhorn/printing-press-library install openrouter-image --cli-only
```
Verify: openrouter-image-pp-cli --version
Ensure the reported install directory is on $PATH for the agent/runtime that will invoke this skill.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.5 or newer). This installs into $GOPATH/bin (default $HOME/go/bin), so add that directory to $PATH instead:

bash

go install github.com/mvanhorn/printing-press-library/library/ai/openrouter-image/cmd/openrouter-image-pp-cli@latest

If --version reports "command not found" after install, the runtime cannot see the binary directory on $PATH. Do not proceed with skill commands until verification succeeds.

OpenRouter's Image API fronts 40+ image models from every major lab. This CLI adds what the API alone lacks: offline model ranking by capability and budget, pre-spend cost estimates, deterministic re-generation from a local history ledger, budget-gated batch runs, and a weekly spend digest. Model selection is always explicit — every generation names its model.

When to Use This CLI

Use this CLI when an AI agent or human needs to generate images on demand through OpenRouter with explicit model selection, wants to pick the cheapest capable provider before spending, run budgeted batches from a CSV, reproduce past generations exactly, or track image-generation spend over time. It is the tool for scheduled image pipelines, prompt iteration across models, and cost-aware image production.

Anti-triggers

Do not use this CLI for:

Do not use this CLI for chat completions or text generation — use the existing openrouter-pp-cli (cost attribution) or a chat CLI
Do not use this CLI for video generation — OpenRouter's video API is a different surface
Do not use this CLI for embedding, reranking, or speech endpoints
Do not use this CLI as a general HTTP client for arbitrary OpenRouter endpoints

Unique Capabilities

These capabilities aren't available in any other tool for this API.

Local state that compounds

models rank — Rank every image model+provider combo cheapest-first under your capability and budget constraints.

Pick the cheapest provider that meets your image constraints without paging through catalog JSON.
bash
```
openrouter-image-pp-cli models rank --image-to-image --resolution 4K --max-cost 0.10 --limit 5 --json
```
cost-estimate — Estimate USD cost of a generation before spending credits, computed offline from synced per-endpoint pricing.

Agents can check the price of a planned image before spending credits.
bash
```
openrouter-image-pp-cli cost-estimate --model openai/gpt-image-1 --resolution 2K --quality high --n 4
```
regenerate — Re-run a past generation with its exact stored parameters (model, seed, resolution, quality, references).

Reproduce or tweak a past image without re-typing the full flag set.
bash
```
openrouter-image-pp-cli regenerate gen-1234567890 --output winner.png
```
usage digest — Period-over-period spend and volume summary: images generated, USD spent, top models, cost per image vs the prior window.

Budget owners get a machine-readable weekly cost report from the local ledger.
bash
```
openrouter-image-pp-cli usage digest --since 7d --agent
```

Agent-native plumbing

batch — Run many generations from a CSV with a hard USD budget: estimate first, abort before any spend if over, then execute and log each cost.

Cron pipelines can fire a batch with a hard spend cap and get typed exit codes instead of burning the whole balance.
bash
```
openrouter-image-pp-cli batch --spec batch.csv --budget 2.00 --dry-run
```

Reachability mitigation

models diff — See newly added, retired, and price-changed image models between syncs so pinned pipelines never break silently.

Catch a retired model before the next scheduled batch 404s.
bash
```
openrouter-image-pp-cli models diff --since 7d --json
```

Command Reference

activity — Manage activity

openrouter-image-pp-cli activity — Returns user activity data grouped by endpoint for the last 30 (completed) UTC days.

audio — Manage audio

openrouter-image-pp-cli audio create-speech — Synthesizes audio from the input text. Returns a raw audio bytestream in the requested format (e.g. mp3, pcm, wav).
openrouter-image-pp-cli audio create-transcriptions — Transcribes audio into text.

benchmarks — Benchmarks endpoints

openrouter-image-pp-cli benchmarks — Unified benchmark endpoint that aggregates scores from multiple benchmark sources (Artificial Analysis, Design Arena

byok — BYOK endpoints

openrouter-image-pp-cli byok create-byokkey — Create a new bring-your-own-key (BYOK) provider credential.
openrouter-image-pp-cli byok delete-byokkey — Delete (soft-delete) a bring-your-own-key (BYOK) provider credential by its id.
openrouter-image-pp-cli byok get-byokkey — Get a single bring-your-own-key (BYOK) provider credential by its id.
openrouter-image-pp-cli byok list-byokkeys — List the bring-your-own-key (BYOK) provider credentials for the authenticated entity's default workspace.
openrouter-image-pp-cli byok update-byokkey — Update an existing bring-your-own-key (BYOK) provider credential by its id.

chat — Chat completion endpoints

openrouter-image-pp-cli chat — Sends a request for a model response for the given chat conversation. Supports both streaming and non-streaming modes.

classifications — Task classification market-share endpoints

openrouter-image-pp-cli classifications — Returns the market-share breakdown of OpenRouter traffic by task classification (e.g.

credits — Credit management endpoints

openrouter-image-pp-cli credits create-coinbase-charge — Deprecated.
openrouter-image-pp-cli credits get — Get total credits purchased and used for the authenticated user.

datasets — Datasets endpoints

openrouter-image-pp-cli datasets get-app-rankings — Returns the top public apps on OpenRouter ranked by token usage inside the requested date window
openrouter-image-pp-cli datasets get-rankings-daily — Returns the top 50 public models per day by total token usage on OpenRouter

embeddings — Text embedding endpoints

openrouter-image-pp-cli embeddings create — Submits an embedding request to the embeddings router
openrouter-image-pp-cli embeddings list-models — Returns a list of all available embeddings models and their properties

endpoints — Endpoint information

openrouter-image-pp-cli endpoints — Preview the impact of ZDR on the available endpoints

files — Files endpoints

openrouter-image-pp-cli files delete — Deletes a file owned by the requesting workspace. Deletion is irreversible.
openrouter-image-pp-cli files get-metadata — Retrieves metadata for a single file owned by the requesting workspace.
openrouter-image-pp-cli files list — Lists files belonging to the workspace of the authenticating API key.
openrouter-image-pp-cli files upload — Uploads a file to be referenced in future API calls.

generation — Generation history endpoints

openrouter-image-pp-cli generation get — Get request & usage metadata for a generation
openrouter-image-pp-cli generation list-content — Get stored prompt and completion content for a generation
openrouter-image-pp-cli generation submit-feedback — Submit structured feedback on a generation the authenticated user made.

guardrails — Guardrails endpoints

openrouter-image-pp-cli guardrails create — Create a new guardrail for the authenticated user.
openrouter-image-pp-cli guardrails delete — Delete an existing guardrail. Management key required.
openrouter-image-pp-cli guardrails get — Get a single guardrail by ID. Management key required.
openrouter-image-pp-cli guardrails list — List all guardrails for the authenticated user.
openrouter-image-pp-cli guardrails list-key-assignments — List all API key guardrail assignments for the authenticated user.
openrouter-image-pp-cli guardrails list-member-assignments — List all organization member guardrail assignments for the authenticated user.
openrouter-image-pp-cli guardrails update — Update an existing guardrail. Collection fields use replace semantics: send the full desired set on every update.

images — Images endpoints

openrouter-image-pp-cli images create — Generates an image from a text prompt via the image generation router
openrouter-image-pp-cli images list-model-endpoints — Returns the full per-endpoint records for an image model: each endpoint's definitive supported parameters, pricing
openrouter-image-pp-cli images list-models — Lists every image generation model with its top-level supported-parameter superset and a URL to its full per-endpoint

key — Manage key

openrouter-image-pp-cli key — Get information on the API key associated with the current authentication session

keys — Manage keys

openrouter-image-pp-cli keys create — Create a new API key for the authenticated user. The plaintext key is returned only in this response.
openrouter-image-pp-cli keys delete — Delete an existing API key. Management key required.
openrouter-image-pp-cli keys get — Get a single API key by hash. Management key required.
openrouter-image-pp-cli keys list — List all API keys for the authenticated user. Management key required.
openrouter-image-pp-cli keys update — Update an existing API key. Management key required.

messages — Manage messages

openrouter-image-pp-cli messages — Creates a message using the Anthropic Messages API format. Supports text, images, PDFs, tools, and extended thinking.

model — Model information endpoints

openrouter-image-pp-cli model <author> <slug> — Returns full details for a single model identified by its author and slug (e.g. openai/gpt-4).

models — Model information endpoints

openrouter-image-pp-cli models get — List all models and their properties
openrouter-image-pp-cli models list-count — Get total count of available models
openrouter-image-pp-cli models list-user — List models filtered by user provider preferences, [privacy settings](https://openrouter.

observability — Observability endpoints

openrouter-image-pp-cli observability create-destination — Create a new observability destination. A maximum of 5 destinations per type is allowed.
openrouter-image-pp-cli observability delete-destination — Delete an existing observability destination. This performs a soft delete.
openrouter-image-pp-cli observability get-destination — Fetch a single observability destination by its UUID.
openrouter-image-pp-cli observability list-destinations — List the observability destinations configured for the authenticated entity's default workspace.
openrouter-image-pp-cli observability update-destination — Update an existing observability destination. Only the fields provided in the request body are updated.

openrouter-analytics — Manage openrouter analytics

openrouter-image-pp-cli openrouter-analytics get-meta — Returns the available metrics, dimensions, filter operators, and granularities for the analytics query endpoint.
openrouter-image-pp-cli openrouter-analytics query — Execute an analytics query with specified metrics, dimensions, filters, and time range.

openrouter-auth — Manage openrouter auth

openrouter-image-pp-cli openrouter-auth create-keys-code — Create an authorization code for the PKCE flow to generate a user-controlled API key
openrouter-image-pp-cli openrouter-auth exchange-code-for-apikey — Exchange an authorization code from the PKCE flow for a user-controlled API key

organization — Organization endpoints

openrouter-image-pp-cli organization — List all members of the organization associated with the authenticated management key.

presets — Presets endpoints

openrouter-image-pp-cli presets get — Retrieves a preset by its slug with its currently designated version inline.
openrouter-image-pp-cli presets list — Lists all presets for the authenticated user, ordered by most recently updated first.

providers — Provider information endpoints

openrouter-image-pp-cli providers — List all providers

rerank — Rerank endpoints

openrouter-image-pp-cli rerank — Submits a rerank request to the rerank router

responses — OpenAI-compatible Responses API endpoints

openrouter-image-pp-cli responses create — Creates a streaming or non-streaming response using OpenResponses API format
openrouter-image-pp-cli responses create-compact — Rewrites a conversation into a smaller context window, returning the canonical next context window

scim — SCIM endpoints

openrouter-image-pp-cli scim create-group-mapping — Create a SCIM group-to-workspace role mapping.
openrouter-image-pp-cli scim delete-group-mapping — Delete a SCIM group-to-workspace mapping. Management key required.
openrouter-image-pp-cli scim get-group-mapping — Get a SCIM group-to-workspace mapping. Management key required.
openrouter-image-pp-cli scim list-group-mappings — List SCIM group-to-workspace mappings for the organization.
openrouter-image-pp-cli scim list-groups — List SCIM groups for the organization. Management key required.
openrouter-image-pp-cli scim update-group-mapping — Update a SCIM group mapping role. Management key required.

videos — Manage videos

openrouter-image-pp-cli videos create — Submits a video generation request and returns a polling URL to check status
openrouter-image-pp-cli videos get — Returns job status and content URLs when completed
openrouter-image-pp-cli videos list-models — Returns a list of all available video generation models and their properties

workspaces — Workspaces endpoints

openrouter-image-pp-cli workspaces create — Create a new workspace for the authenticated user.
openrouter-image-pp-cli workspaces delete — Delete an existing workspace. The default workspace cannot be deleted.
openrouter-image-pp-cli workspaces get — Get a single workspace by ID or slug. Management key required.
openrouter-image-pp-cli workspaces list — List all workspaces for the authenticated user.
openrouter-image-pp-cli workspaces update — Update an existing workspace by ID or slug. Management key required.

Freshness Contract

This printed CLI owns bounded freshness only for registered store-backed read command paths. In --data-source auto mode, those paths check sync_state and may run a bounded refresh before reading local data. --data-source local never refreshes. --data-source live reads the API and does not mutate the local store. Set OPENROUTER_IMAGE_NO_AUTO_REFRESH=1 to skip the freshness hook without changing source selection.

Covered paths:

openrouter-image-pp-cli activity
openrouter-image-pp-cli activity get
openrouter-image-pp-cli activity list
openrouter-image-pp-cli activity search
openrouter-image-pp-cli benchmarks
openrouter-image-pp-cli benchmarks get
openrouter-image-pp-cli benchmarks list
openrouter-image-pp-cli benchmarks search
openrouter-image-pp-cli byok
openrouter-image-pp-cli byok get
openrouter-image-pp-cli byok list
openrouter-image-pp-cli byok search
openrouter-image-pp-cli datasets
openrouter-image-pp-cli datasets get
openrouter-image-pp-cli datasets list
openrouter-image-pp-cli datasets search
openrouter-image-pp-cli datasets-rankings-daily
openrouter-image-pp-cli datasets-rankings-daily get
openrouter-image-pp-cli datasets-rankings-daily list
openrouter-image-pp-cli datasets-rankings-daily search
openrouter-image-pp-cli embeddings
openrouter-image-pp-cli embeddings get
openrouter-image-pp-cli embeddings list
openrouter-image-pp-cli embeddings search
openrouter-image-pp-cli endpoints
openrouter-image-pp-cli endpoints get
openrouter-image-pp-cli endpoints list
openrouter-image-pp-cli endpoints search
openrouter-image-pp-cli files
openrouter-image-pp-cli files get
openrouter-image-pp-cli files list
openrouter-image-pp-cli files search
openrouter-image-pp-cli generation
openrouter-image-pp-cli generation get
openrouter-image-pp-cli generation list
openrouter-image-pp-cli generation search
openrouter-image-pp-cli guardrails
openrouter-image-pp-cli guardrails get
openrouter-image-pp-cli guardrails list
openrouter-image-pp-cli guardrails search
openrouter-image-pp-cli guardrails-assignments-keys
openrouter-image-pp-cli guardrails-assignments-keys get
openrouter-image-pp-cli guardrails-assignments-keys list
openrouter-image-pp-cli guardrails-assignments-keys search
openrouter-image-pp-cli guardrails-assignments-members
openrouter-image-pp-cli guardrails-assignments-members get
openrouter-image-pp-cli guardrails-assignments-members list
openrouter-image-pp-cli guardrails-assignments-members search
openrouter-image-pp-cli images
openrouter-image-pp-cli images get
openrouter-image-pp-cli images list
openrouter-image-pp-cli images search
openrouter-image-pp-cli keys
openrouter-image-pp-cli keys get
openrouter-image-pp-cli keys list
openrouter-image-pp-cli keys search
openrouter-image-pp-cli models
openrouter-image-pp-cli models get
openrouter-image-pp-cli models list
openrouter-image-pp-cli models search
openrouter-image-pp-cli models-count
openrouter-image-pp-cli models-count get
openrouter-image-pp-cli models-count list
openrouter-image-pp-cli models-count search
openrouter-image-pp-cli models-user
openrouter-image-pp-cli models-user get
openrouter-image-pp-cli models-user list
openrouter-image-pp-cli models-user search
openrouter-image-pp-cli observability
openrouter-image-pp-cli observability get
openrouter-image-pp-cli observability list
openrouter-image-pp-cli observability search
openrouter-image-pp-cli organization
openrouter-image-pp-cli organization get
openrouter-image-pp-cli organization list
openrouter-image-pp-cli organization search
openrouter-image-pp-cli presets
openrouter-image-pp-cli presets get
openrouter-image-pp-cli presets list
openrouter-image-pp-cli presets search
openrouter-image-pp-cli providers
openrouter-image-pp-cli providers get
openrouter-image-pp-cli providers list
openrouter-image-pp-cli providers search
openrouter-image-pp-cli scim
openrouter-image-pp-cli scim get
openrouter-image-pp-cli scim list
openrouter-image-pp-cli scim search
openrouter-image-pp-cli scim-group-mappings
openrouter-image-pp-cli scim-group-mappings get
openrouter-image-pp-cli scim-group-mappings list
openrouter-image-pp-cli scim-group-mappings search
openrouter-image-pp-cli videos
openrouter-image-pp-cli videos get
openrouter-image-pp-cli videos list
openrouter-image-pp-cli videos search
openrouter-image-pp-cli workspaces
openrouter-image-pp-cli workspaces get
openrouter-image-pp-cli workspaces list
openrouter-image-pp-cli workspaces search

When JSON output uses the generated provenance envelope, freshness metadata appears at meta.freshness. Treat it as current-cache freshness for the covered command path, not a guarantee of complete historical backfill or API-specific enrichment.

Finding the right command

When you know what you want to do but not which command does it, ask the CLI directly:

bash

openrouter-image-pp-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match — fall back to --help or use a narrower query.

Recipes

Cheapest image-to-image model under budget

bash

openrouter-image-pp-cli models rank --image-to-image --max-cost 0.10 --limit 3 --json

Finds capable providers under a per-image budget, cheapest first

Budget-gated batch from CSV

bash

openrouter-image-pp-cli batch --spec batch.csv --budget 5.00 --dry-run

Dry-run estimates every row and aborts if the total exceeds the budget before any spend

Reproduce last week's winner

bash

openrouter-image-pp-cli regenerate gen-1234567890 --output winner-v2.png

Replays the exact stored model, seed, resolution, and quality of a past generation

Pre-flight cost check for an agent

bash

openrouter-image-pp-cli cost-estimate --model bytedance-seed/seedream-4.5 --resolution 2K --n 4 --json

Agents can gate generation on the quoted price before spending credits

Narrow generation output for agents

bash

openrouter-image-pp-cli generate --model google/gemini-2.5-flash-image --prompt 'a red panda astronaut floating in space, studio lighting' --json --agent --select data.0.media_type,usage.cost

Deeply nested generation responses collapse to the fields an agent needs

Spot a retiring model before cron breaks

bash

openrouter-image-pp-cli models diff --since 7d --json

Surfaces retired and price-changed models between syncs so pinned pipelines fail loudly, not silently

Auth Setup

Set OPENROUTER_API_KEY in your environment. The key is read per command; nothing is persisted to disk. Run openrouter-image-pp-cli doctor to verify setup.

Run openrouter-image-pp-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color --yes.

Pipeable — JSON on stdout, errors on stderr
Filterable — --select keeps a subset of fields. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:
bash
```
openrouter-image-pp-cli benchmarks --agent --select id,name,status
```
Previewable — --dry-run shows the request without sending
Offline-friendly — sync/search commands can use the local SQLite store when available
Non-interactive — never prompts, every input is a flag
Explicit retries — use --idempotent only when an already-existing create should count as success, and use --ignore-missing only when a missing delete target should count as success

Response envelope

Commands that read from the local store or the API wrap output in a provenance envelope:

json

{
  "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."},
  "results": <data>
}

Parse .results for data and .meta.source to know whether it's live or local. A human-readable N results (live) summary is printed to stderr only when stdout is a terminal AND no machine-format flag (--json, --csv, --compact, --quiet, --plain, --select) is set — piped/agent consumers and explicit-format runs get pure JSON on stdout.

Paths and state

Agents should treat the CLI's path resolver as part of the runtime contract:

Use --home <dir> for one invocation, or set OPENROUTER_IMAGE_HOME=<dir> to relocate all four path kinds under one root.
Use per-kind env vars only when a specific kind must diverge: OPENROUTER_IMAGE_CONFIG_DIR, OPENROUTER_IMAGE_DATA_DIR, OPENROUTER_IMAGE_STATE_DIR, OPENROUTER_IMAGE_CACHE_DIR.
Resolution order is per-kind env var, --home, OPENROUTER_IMAGE_HOME, XDG (XDG_CONFIG_HOME, XDG_DATA_HOME, XDG_STATE_HOME, XDG_CACHE_HOME), then platform defaults.
config contains settings like config.toml and profiles. data contains credentials.toml, data.db, cookies, and auth sidecars. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.
Stored secrets live in credentials.toml under the data dir. Existing legacy config.toml secrets are read for compatibility and leave config.toml on the first auth write.
Run openrouter-image-pp-cli doctor --fail-on warn to surface path and credential-location warnings. agent-context exposes a schema v4 paths block for agents that need the resolved dirs.

For MCP, pass relocation through the MCP host config. The MCP binary does not inherit CLI flags:

json

{
  "mcpServers": {
    "openrouter-image": {
      "command": "openrouter-image-pp-mcp",
      "env": {
        "OPENROUTER_IMAGE_HOME": "/srv/openrouter-image"
      }
    }
  }
}

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use OPENROUTER_IMAGE_HOME or per-kind vars as durable fleet levers, and use --home only for a single invocation. Relocation is not reversible by unsetting env vars; move files manually before clearing OPENROUTER_IMAGE_HOME, or doctor will not find credentials left under the former root.

Show full SKILL.md (1,686 more words)Show less

Automatic learning

This CLI ships a self-capturing learning loop. The CLI does its own bookkeeping: every invocation is journaled locally, a failed flag followed by a corrected retry auto-derives a flag_alias candidate, and a teach on a query family without a playbook auto-synthesizes a playbook_candidate from the session's journal. Your job is judgment only: recall first, act on surfaced candidates, teach the final answer, playbook amend when you observe a correction. You never record failures by hand.

Step 1: `recall` before any discovery

Before list/search/drill commands on a new user question, run:

bash

openrouter-image-pp-cli recall "<user's question>" --agent

The response envelope:

json

{
  "query": "...",
  "normalized": "<normalized form>",
  "query_entities": ["..."],
  "found": true | false,
  "match_score": 0.0,
  "results": [
    { "resource_id": "...", "resource_type": "...", "venue": "...",
      "confidence": 2, "entity_match": "exact|partial|unknown",
      "source": "taught|preseed|pattern", "warnings": ["..."] }
  ],
  "mismatches": [ /* only when --debug-mismatches */ ],
  "warnings": [ /* top-level */ ],
  "candidates": [
    { "id": 12, "class": "flag_alias | playbook_candidate",
      "summary": "...", "sightings": 3, "last_seen": "...",
      "rationale": "...",
      "next_action": ["<trial command>", "openrouter-image-pp-cli learnings confirm 12"] }
  ],
  "playbook": {
    "query_family": "...",
    "playbook": {
      "steps": [ { "cmd": "<command with {slot} substitution>", "purpose": "..." } ],
      "entity_slots": ["$ENTITY"],
      "expected_tool_calls": 3
    },
    "slots_resolved": { "$ENTITY": { "token": "<live token>", "canonical": "<canonical>" } },
    "notes": "<workarounds + gotchas for this query family>"
  },
  "notes": "<duplicate surface for non-playbook callers>"
}

Empty-store short-circuit: if the store has no learnings, playbooks, or candidates yet (recall finds nothing and learnings list and learnings candidates are both empty), skip recall for the rest of this session instead of taxing every query; resume recall-first once something has been taught.

Step 2: decision tree

Read candidates, playbook, notes, results[0], and warnings in that order:

if Candidates present (warnings include "candidates_present"):
    -> candidates are try-then-confirm, never facts. Follow each candidate's
       two-step next_action verbatim: run the trial command first, then run
       `learnings confirm <id>` only after the trial verified the behavior.
       Reject a wrong candidate with `learnings reject <id>`.
    -> NEVER re-teach something recall surfaced as a candidate; confirm or
       reject that candidate instead of teaching a duplicate.
    -> candidates ride alongside playbooks and resource hits, not instead of
       them; continue with the branches below after acting on them.

if Playbook present:
    -> READ Playbook.notes verbatim FIRST (workarounds + gotchas the CLI surface doesn't expose)
    -> replay Playbook.steps in order, substituting Playbook.slots_resolved entries
       for the entity slot tokens. If a step's slot is unresolved, fall back to
       discovery for that step only.
    -> the Playbook's expected_tool_calls is a budget; if you find yourself running
       materially more, record the divergence via `openrouter-image-pp-cli playbook amend`
       at end-of-session.

elif Notes present (no Playbook):
    -> read Notes verbatim before any discovery step; they carry known gotchas
       for this query family even when no structured choreography exists yet.

elif Found AND Results[0].EntityMatch == "exact" AND Results[0].Confidence >= 2:
    -> skip discovery; fetch live data for Results[*].ResourceID in parallel

elif Found AND Results[0].EntityMatch == "partial":
    -> candidate hint, NOT a hit; read the resource title to validate before trusting

elif (any row in Mismatches[] when --debug-mismatches was passed):
    -> treat as cold start; the stored learning is for a different entity
       (different canonical resolved from query_entities)

else:  // Found == false, no playbook, no notes
    -> cold start; run discovery normally; teach the answer afterward (Step 4).
       If the family has no playbook yet, that teach auto-synthesizes a
       playbook candidate from this session's journal - you do not need to
       record one by hand.

Playbook and Notes are orthogonal to the per-resource path. A recall response can carry both a Playbook AND a Results[] hit - use both: the Playbook tells you which choreography to run; the resource hits short-circuit specific steps. Default to skipping mismatches; pass --debug-mismatches only when investigating cold-start surprises.

Candidate judgment details: learnings confirm <id> prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. learnings reject <id> tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; openrouter-image-pp-cli learnings candidates lists the full open set.

Graceful degradation: if learnings confirm is an unknown command, you are driving an older binary - ignore the candidates guidance and follow the rest of the protocol.

Step 3: always read `warnings`

low_confidence: row exists at confidence<2. Treat as a hint, not a skip-discovery hit.
resource_not_in_store: the local store doesn't have the resource the learning points at. The match validator couldn't classify entities — direct-fetch and re-evaluate.
cross_alias_match (per-result): the row was taught under a different alias and matched the live query's canonical via entity_lookups (e.g., a "USA" teach satisfying a "United States" recall). Trust the resource_id.
similar_shape_different_entity:<canonical> (top-level): a structurally matching row exists but its canonical entity differs from the live query's. Treated as cold start; the warning carries the conflicting canonical as a hint, but the row is NOT promoted into Results.
ambiguous_alias (top-level): a single query entity resolved to multiple canonicals (e.g., "Cards" → Arizona Cardinals + St. Louis Cardinals). Surface the ambiguity from context before committing to a resource.
candidates_present (top-level): the envelope carries a candidates section. Handle it via the candidates branch in Step 2 before anything else.
lookup_refresh_available (top-level): an entity in the query has no lookup row yet, but synced data could provide one. Run openrouter-image-pp-cli sync to refresh entity lookups.
Top-level no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.

Step 4: `teach &` after finalizing your response - always

Teaching is unconditional. After resolving a query the store could not answer, background-teach the final resource mapping - no call-count threshold, no judging whether it was "worth" learning. The teach is the anchor of the loop: it triggers playbook synthesis for a family without a playbook, and same-referent phrasings fold into one family so near-duplicate teaches do not fragment the store. Fire it after assembling your user-facing response but BEFORE emitting it, with a shell & so the call returns immediately:

bash

openrouter-image-pp-cli teach --query "<user's question>" --resource-type <type> --resource <id1> --resource <id2>
# (append shell `&` to background it)

Silent on success. Errors only land in teach.log under the resolved state dir. Teach the most specific resource - if the user asked a broad question and you walked through parent records to find the specific answer, teach the leaf id, not the parent. The CLI uses seeded entity_lookups for cross-alias resolution at recall time, so a teach under one alias (e.g., "Niners") satisfies future queries under another alias (e.g., "49ers", "San Francisco") automatically.

PII rule: teach the structural question with identifiers stripped - never include names, emails, phone numbers, account ids, or other personal identifiers in taught queries or notes. The CLI scans teach queries for obvious email/phone shapes and warns, but does not block; strip before teaching rather than relying on the warning.

Step 5: playbooks - optional flags, automatic synthesis

You do not need to decide whether a session "deserves" a playbook: a teach on a family without one auto-synthesizes a playbook_candidate from the session's journal, and the next session judges it via confirm/reject. Attach explicit playbook flags only when you already hold choreography worth recording verbatim - workarounds the CLI didn't surface (silently-dropped flags, undocumented params, pagination tricks, payload gotchas). Prefer the integrated one-call form - record the resource learning and the playbook in the same teach invocation:

bash

# Common case: record both the resource learning AND the playbook in one call.
openrouter-image-pp-cli teach \
  --query "<user's question>" \
  --resource <id> \
  --playbook-file ~/playbooks/<shape>.json \
  --playbook-notes-file ~/playbooks/<shape>-notes.md
# (append shell `&` to background it)

# Alternate: playbook-only (no resource to record alongside).
openrouter-image-pp-cli teach-playbook \
  --query "<user's question>" \
  --playbook-file ~/playbooks/<shape>.json \
  --notes-file ~/playbooks/<shape>-notes.md

Playbook files are JSON with steps, entity_slots, expected_tool_calls. Notes files are markdown carrying the gotchas verbatim. File-free callers (MCP-only agents) pass the same content inline: --playbook-json and --playbook-notes on the integrated teach form, --playbook-json and --notes on teach-playbook. On the integrated teach form, the playbook flags are optional - omit them entirely for a resource-only teach. On the standalone teach-playbook form, at least one of the playbook and notes flags must be set; both empty is rejected. Playbooks are keyed on the structural query family (entities stripped) so a recipe taught from one entity-shaped query applies to every other query of the same shape, with slots_resolved binding the live query's canonical at recall time.

When you DO find a playbook on a future recall, treat it as ground truth: replay the steps with slots_resolved substitutions, skip the discovery that the choreography already documents, and read notes before any step.

Step 6: `playbook amend &` when your debug response identifies a correction

If your debug-protocol response identifies a concrete correction the notes or playbook should know — a workaround, an undocumented endpoint shape, a stale field name, observed schema drift, an empty-payload fallback — fire playbook amend BEFORE emitting your user-facing response. Same fire-and-forget posture as teach.

bash

openrouter-image-pp-cli playbook amend \
  --query "<exact recall query string>" \
  --add-note "<your concrete correction>"
# (append shell `&` to background it)

What counts as worth amending: a behavior you OBSERVED this session that future-you would benefit from knowing. Examples worth amending:

A workaround for a CLI surface that silently drops or misorders a flag.
An undocumented endpoint shape (response wrapped in {meta, results}, payload nested two levels deeper than the docs claim).
Observed schema drift (a field renamed, an index that shifted between seasons, a category label that the API now returns lower-cased).

What does NOT belong in notes:

The year-specific or entity-specific answer to the user's question. That's the response, not a learning.
Per-team / per-athlete / per-row data the playbook already retrieves at runtime.
Statements that paraphrase what the existing notes already say.

The amend command appends to the family's existing notes with a timestamped marker ([amend YYYY-MM-DDTHH:MMZ]: <text>). Multiple amends accumulate; the audit trail is visible. If no playbook exists yet for the family, amend creates a notes-only one (so cold-start corrections still land).

PII discipline for amend notes

playbook amend notes are designed to potentially flow upstream as shared knowledge in future versions of the Printing Press. Keep them clean of user-identifying content so the upstream-contribution path stays open without retroactive scrubbing:

Do NOT embed paths to user filesystems, personal API keys or tokens, user email addresses, user GitHub handles, or specific query histories tied to a single user.
Acceptable: endpoint shapes, undocumented field names, API gotchas, observed schema drift, workarounds for CLI surfaces, generalizable pagination or retry tactics.

If a correction is only meaningful with user-specific context, it belongs in a personal note, not in the playbook amend.

Measuring the loop

openrouter-image-pp-cli learnings stats reports recall hit rate, teach-to-reuse, playbook resolution rate, and candidate confirm/reject counts from the local learn_events table. Rates are null until they have a denominator; everything stays on this machine. Use it to check whether the loop is earning its keep for this CLI.

Disabling learning

--no-learn on a single command short-circuits both recall and the teach write path. Use for deterministic agent flows or tests that must not be affected by accumulated learnings.
OPENROUTER_IMAGE_NO_LEARN=true in the environment globally disables the pipeline.

Agent Feedback

When you (or the agent) notice something off about this CLI, record it:

openrouter-image-pp-cli feedback "the --since flag is inclusive but docs say exclusive"
openrouter-image-pp-cli feedback --stdin < notes.txt
openrouter-image-pp-cli feedback list --json --limit 10

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless OPENROUTER_IMAGE_FEEDBACK_ENDPOINT is set AND either --send is passed or OPENROUTER_IMAGE_FEEDBACK_AUTO_SEND=true. Default behavior is local-only.

Write what surprised you, not a bug report. Short, specific, one line: that is the part that compounds.

Output Delivery

Every command accepts --deliver <sink>. The output goes to the named sink in addition to (or instead of) stdout, so agents can route command results without hand-piping. Three sinks are supported:

Sink	Effect
`stdout`	Default; write to stdout only
`file:<path>`	Atomically write output to `<path>` (tmp + rename)
`webhook:<url>`	POST the output body to the URL (`application/json` or `application/x-ndjson` when `--compact`)

Unknown schemes are refused with a structured error naming the supported set. Webhook failures return non-zero and log the URL + HTTP status on stderr.

Named Profiles

A profile is a saved set of flag values, reused across invocations. Use it when a scheduled or recurring agent reuses the same saved flags while providing different input each run.

openrouter-image-pp-cli profile save briefing --json
openrouter-image-pp-cli --profile briefing benchmarks
openrouter-image-pp-cli profile list --json
openrouter-image-pp-cli profile show briefing
openrouter-image-pp-cli profile delete briefing --yes

Explicit flags always win over profile values; profile values win over defaults. agent-context lists all available profiles under available_profiles so introspecting agents discover them at runtime.

Exit Codes

Code	Meaning
0	Success
2	Usage error (wrong arguments)
3	Resource not found
4	Authentication required
5	API error (upstream issue)
7	Rate limited (wait and retry)
10	Config error

Argument Parsing

Parse $ARGUMENTS:

Empty, help, or --help → show openrouter-image-pp-cli --help output
Starts with install → ends with mcp → MCP installation; otherwise → see Prerequisites above
Anything else → Direct Use (execute as CLI command with --agent)

MCP Server Installation

Install the MCP server:

bash

go install github.com/mvanhorn/printing-press-library/library/ai/openrouter-image/cmd/openrouter-image-pp-mcp@latest

bash

claude mcp add openrouter-image-pp-mcp -- openrouter-image-pp-mcp

Verify: claude mcp list

Direct Use

Check if installed: which openrouter-image-pp-cli If not found, offer to install (see Prerequisites at the top of this skill).
Match the user query to the best command from the Unique Capabilities and Command Reference above.

Execute with the --agent flag:

bash

openrouter-image-pp-cli <command> [subcommand] [args] --agent

If ambiguous, drill into subcommand help: openrouter-image-pp-cli <command> --help.

Files

SKILL.md and 337 other files in library/ai/openrouter-image of mvanhorn/printing-press-library.

SKILL.md
.golangci.yml
.goreleaser.yaml
.manuscripts/20260803-205427-851f4b39/proofs/2026-08-03-205427-fix-openrouter-image-pp-cli-build-log.md
.manuscripts/20260803-205427-851f4b39/proofs/2026-08-03-205427-fix-openrouter-image-pp-cli-polish.md
.manuscripts/20260803-205427-851f4b39/proofs/2026-08-03-205427-fix-openrouter-image-pp-cli-shipcheck.md
.manuscripts/20260803-205427-851f4b39/proofs/phase5-acceptance.json
.manuscripts/20260803-205427-851f4b39/proofs/publish-live-gate.json
.manuscripts/20260803-205427-851f4b39/research.json
.manuscripts/20260803-205427-851f4b39/research/2026-08-03-205427-feat-openrouter-image-pp-cli-absorb-manifest.md
.manuscripts/20260803-205427-851f4b39/research/2026-08-03-205427-feat-openrouter-image-pp-cli-brief.md
.manuscripts/20260803-205427-851f4b39/research/2026-08-03-205427-novel-features-brainstorm.md
.printing-press-patches/.gitkeep
.printing-press-patches/batch-rollback-reports-committed-count.json
.printing-press-patches/ledger-time-and-reference-arrays.json
.printing-press-pii-polish.json
… and 322 more

Open the folder on GitHubat commit 76de244

Compare with similar skills

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Pp Openrouter Image compared with similar skills
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Freetoken Botslimin112/min-skill	412	—	~1.3k	Automated safety check: Pass	None	yesterday
Add Modelget-convex/convex-evals	130	—	~1.5k	Automated safety check: Notes	Apache-2.0	today
To Imgrtadewald/skills	185	—	~673	Automated safety check: Notes	None	11 days ago
Openrouterdavidondrej/skills	4.1k	—	~1.3k	Automated safety check: Pass	MIT	yesterday
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Questions about Pp Openrouter Image

What does Pp Openrouter Image do?

Every image model on OpenRouter, one key: generate, rank, estimate, and batch with a local cost ledger. Pp Openrouter Image is an agent skill from mvanhorn/printing-press-library. Every image model on OpenRouter, one key: generate, rank, estimate, and batch with a local cost ledger.

When should I use Pp Openrouter Image?

Pp Openrouter Image fits situations like: phrases: generate an image; create a picture; which image model is cheapest; estimate the cost of this image.

How do I install Pp Openrouter Image in Claude Code?

Run `npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a claude-code`. Or copy the skill folder (library/ai/openrouter-image in mvanhorn/printing-press-library) into .claude/skills/pp-openrouter-image in your project. Claude Code loads it when a task matches its description.

How do I install Pp Openrouter Image in Codex?

Run `npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a codex`. Or copy the skill folder (library/ai/openrouter-image in mvanhorn/printing-press-library) into .agents/skills/pp-openrouter-image in your project. Codex loads it when a task matches its description.

Can I use Pp Openrouter Image in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mvanhorn/printing-press-library --skill pp-openrouter-image -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pp-openrouter-image, .gemini/skills/pp-openrouter-image, .github/skills/pp-openrouter-image and .opencode/skills/pp-openrouter-image in your project.

What does Pp Openrouter Image need to run?

Going by SKILL.md and its folder, Pp Openrouter Image needs the command-line tools its instructions call (go, claude and npx) and credentials named OPENROUTER_API_KEY. Our summary lists: Node.js. Its frontmatter pre-approves these tools: Read, Bash.

Does Pp Openrouter Image access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Pp Openrouter Image safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Pp Openrouter Image use?

Pp Openrouter Image is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pp Openrouter Image use?

About 11k tokens (SKILL.md is roughly 44k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Pp Openrouter Image?

Skills that share tags, products or a category with Pp Openrouter Image: Freetoken Bots (limin112/min-skill, 412 stars), Add Model (get-convex/convex-evals, 130 stars), To Img (rtadewald/skills, 185 stars) and Openrouter (davidondrej/skills, 4.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pp Openrouter Image?

mvanhorn (a GitHub user) maintains it in mvanhorn/printing-press-library, which has 2,056 GitHub stars. The repository holds 506 skills in this directory. The repository was last updated on October 9, 2026.

Source: mvanhorn/printing-press-library on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Pp Openrouter Image

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

OpenRouter — Printing Press CLI

Prerequisites: Install the CLI

When to Use This CLI

Anti-triggers

Unique Capabilities

Local state that compounds

Agent-native plumbing

Reachability mitigation

Command Reference

Freshness Contract

Finding the right command

Recipes

Cheapest image-to-image model under budget

Budget-gated batch from CSV

Reproduce last week's winner

Pre-flight cost check for an agent

Narrow generation output for agents

Spot a retiring model before cron breaks

Auth Setup

Agent Mode

Response envelope

Paths and state

Automatic learning

Step 1: recall before any discovery

Step 2: decision tree

Step 3: always read warnings

Step 4: teach & after finalizing your response - always

Step 5: playbooks - optional flags, automatic synthesis

Step 6: playbook amend & when your debug response identifies a correction

PII discipline for amend notes

Measuring the loop

Disabling learning

Agent Feedback

Output Delivery

Named Profiles

Exit Codes

Argument Parsing

MCP Server Installation

Direct Use

Files

Compare with similar skills

Similar skills

More from mvanhorn/printing-press-library

Related

Questions about Pp Openrouter Image

Step 1: `recall` before any discovery

Step 3: always read `warnings`

Step 4: `teach &` after finalizing your response - always

Step 6: `playbook amend &` when your debug response identifies a correction