How to design the numbered prompt pipeline that drives Hunt phases in Cavekit.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Prompt Pipeline

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill prompt-pipeline -a claude-code

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins prompt-pipeline --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/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/JuliusBrussee/blueprint/skills/prompt-pipeline .claude/skills/prompt-pipeline && 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/

Facts

Skill name
prompt-pipeline
GitHub stars
1.3k
Token cost
~4.7k tokens
SKILL.md length
1,330 words
Files
1
Skills in repo
716
Repo updated
First seen
Licence
Apache-2.0

At a glance

How to design the numbered prompt pipeline that drives Hunt phases in Cavekit.

  • Works in 9 steps: Greenfield Pattern (3-Prompt Pipeline) → Rewrite Pattern (6-9 Prompt Pipeline) → Shared Principles Across All Pipelines → …
  • Phrases: prompt pipeline
  • SKILL.md covers 1. Greenfield Pattern…, 2. Rewrite Pattern (6-9 Prompt…, 3. Shared Principles Across… and 4. Prompt Engineering Best…, plus 6 more sections
  • Calls git

What it does

Prompt Pipeline is an agent skill from hashgraph-online/awesome-codex-plugins. How to design the numbered prompt pipeline that drives Hunt phases in Cavekit. Covers greenfield 3-prompt patterns, rewrite 6-9 prompt patterns, shared principles, prompt engineering best practices, task templates, and time guards. Trigger phrases: "prompt pipeline", "design prompts for SDD", "create Hunt prompts", "pipeline prompts", "how many prompts do I need"

Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Phrases: prompt pipeline
  • Design prompts for SDD
  • Create Hunt prompts
  • Pipeline prompts

Example prompts

  • “prompt pipeline”
  • “design prompts for SDD”
  • “create Hunt prompts”
  • “/prompt-pipeline”

Workflow steps

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

  1. Greenfield Pattern (3-Prompt Pipeline)
  2. Rewrite Pattern (6-9 Prompt Pipeline)
  3. Shared Principles Across All Pipelines
  4. Prompt Engineering Best Practices
  5. Task Template Standardization
  6. Time Guards
  7. Prompt File Naming Convention
  8. Designing Your Pipeline
  9. Iteration Loop Integration

What it can do on your machine

Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, 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 no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prompt Pipeline loads about 4.7k tokens when it runs. Until then it costs about 95 tokens; SKILL.md has 1,330 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~4.7k

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 passed

The automated check found no risky patterns in SKILL.md.

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 hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its Apache-2.0 licence (© hashgraph-online). 1,330 words, ~4,660 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-pipeline/SKILL.md (or your agent's skills folder).
name
prompt-pipeline
description
How to design the numbered prompt pipeline that drives Hunt phases in Cavekit. Covers greenfield 3-prompt patterns, rewrite 6-9 prompt patterns, shared principles, prompt engineering best practices, task templates, and time guards. Trigger phrases: "prompt pipeline", "design prompts for SDD", "create Hunt prompts", "pipeline prompts", "how many prompts do I need"

Prompt Pipeline Design

The prompt pipeline is the engine of SDD. Each numbered prompt drives one phase of the Hunt lifecycle (Spec, Plan, Implement, Iterate, Monitor). Prompts are structured markdown files that instruct an AI agent to perform a specific phase, with detailed information delegated to specs, plans, and reference materials.

Core principle: Prompts should be as lightweight and systemic as possible. They define the process, not the content -- specs and plans hold the content.


1. Greenfield Pattern (3-Prompt Pipeline)

For new projects starting from reference materials (PRDs, language specs, design docs, research).

Pipeline Flow:
  refs/ ──> [001] ──> specs/ ──> [002] ──> plans/ ──> [003] ──> src/ + tests/
                                   ^                     |
                                   |                     |
                                   +── impl/ <───────────+
                                   (bidirectional flow)
Prompt FileLifecycle StageReads FromWrites ToDescription
001-generate-specs-from-refs.mdSpeccontext/refs/context/kits/Reads reference materials, decomposes into domain-specific specs with cross-references and testable acceptance criteria
002-generate-plans-from-specs.mdPlancontext/kits/ + context/impl/context/plans/Reads specs plus implementation progress, creates framework-specific plans with feature dependencies, test strategies, and acceptance criteria
003-generate-impl-from-plans.mdImplementcontext/plans/ + context/kits/src/, tests/, context/impl/Implements the highest-priority unblocked work from plans, runs tests, updates implementation tracking
Key behaviors
  • Prompt 001 runs once or a few times to stabilize specs. It reads context/refs/ and produces context/kits/.
  • Prompt 002 reads specs and any existing implementation tracking (context/impl/). It produces plans that sequence the work.
  • Prompt 003 reads plans and specs, implements code, runs validation gates, and updates context/impl/ with progress.
  • Prompts 002 and 003 modify each other's files. This bidirectional flow is expected and healthy -- it is how the system self-corrects.
Example prompt 001 structure
markdown
# 001: Generate Specs from Reference Materials

## Runtime Inputs
- Framework: {FRAMEWORK}
- Build command: {BUILD_COMMAND}
- Test command: {TEST_COMMAND}

## Context
Read all files in `context/refs/`. These are the source of truth.

## Task
Decompose the reference materials into domain-specific specifications:
1. Create `context/kits/cavekit-overview.md` as the index file
2. Create one `context/kits/spec-{domain}.md` per domain
3. Each spec must include: Scope, Requirements with Acceptance Criteria, Dependencies, Out of Scope, Cross-References

## Exit Criteria
- [ ] All domains from reference materials have corresponding spec files
- [ ] Every requirement has at least one testable acceptance criterion
- [ ] cavekit-overview.md indexes all spec files with one-line summaries
- [ ] Cross-references link related specs

## Completion Signal
<all-tasks-complete>

2. Rewrite Pattern (6-9 Prompt Pipeline)

For projects that start from existing code that must be reverse-engineered into specs before building a new implementation.

Pipeline Flow:
  old-code ──> [001] ──> reference/ ──> [002] ──> specs/ ──> [003] ──> validated specs
                                                                            |
       +────────────────────────────────────────────────────────────────────+
       |
       v
  specs/ ──> [004] ──> plans/ ──> [005] ──> src/ + tests/ ──> [006] ──> updated specs
                                                                            |
       +────────────────────────────────────────────────────────────────────+
       |
       v
  (loop back to 002 for refinement)
Prompt FileLifecycle StageReads FromWrites To
001-generate-refs-from-code.mdPre-SpecOld application sourceshared-context/reference/ (API docs, data models, UI components)
002-generate-specs.mdSpecFeature scope + reference materialsshared-context/kits/ (implementation-agnostic specs)
003-validate-specs.mdSpec QAReference + specsValidation report (specs match old behavior)
004-create-plans.mdPlanSpecs + framework researchcontext/plans/ (framework-specific plans)
005-implement.mdImplementPlans + specssrc/ + tests/ + context/impl/
006-backpropagate.mdIterateWorking prototypeUpdated specs (back-propagates to 002)
Rewrite-specific considerations
  • Prompt 001 only runs once -- it extracts reference documentation from the old codebase.
  • Prompt 003 is a validation pass -- it does not produce code, only a report on spec accuracy.
  • Prompt 006 creates a feedback loop: prototype learnings flow back into specs, which then flow forward through 004 and 005 again.
  • The rewrite pipeline supports multi-repo strategies: shared specs can drive implementations in multiple frameworks simultaneously (e.g., evaluating framework A vs framework B using the same specs).

3. Shared Principles Across All Pipelines

These principles apply regardless of whether the pipeline is greenfield, rewrite, or hybrid.

PrincipleDetail
One prompt per Hunt phaseEach prompt maps to exactly one phase. Do not combine phases.
Explicit input/output directoriesEvery prompt declares what it reads and what it writes. No implicit side effects.
Git-based continuityAgents read git history (git log, git diff, git status) between iterations to understand what was done before.
Explicit done-conditions with termination markersEvery prompt concludes with a verifiable checklist of conditions and a distinct output token that the iteration loop uses to detect completion.
Bidirectional spec/plan updatesPlan prompts read impl tracking; implement prompts update plans. This cross-pollination is healthy.
Test generation on changed filesAfter modifying source files, run test generation to maintain coverage.
Phase gates between promptsBefore moving to the next prompt, verify: build passes, tests pass, acceptance criteria met.
The bidirectional flow in detail
Prompt 002 (Plans):
  READS:  context/kits/     (what to build)
  READS:  context/impl/      (what has been built, what failed)
  WRITES: context/plans/     (how to build it)

Prompt 003 (Implement):
  READS:  context/plans/     (how to build it)
  READS:  context/kits/     (acceptance criteria)
  WRITES: src/, tests/       (the code)
  WRITES: context/impl/      (progress tracking)
  WRITES: context/plans/     (updates to plans based on implementation reality)

This means running prompt 002 again after prompt 003 will incorporate implementation learnings into plans. Running prompt 003 again after prompt 002 will implement updated plans. This is exactly the convergence loop at work.


4. Prompt Engineering Best Practices

4.1 Runtime Inputs

Use runtime variables so prompts work across any project without modification:

markdown
## Runtime Inputs
- Framework: {FRAMEWORK}           # e.g., "React + Vite", "Tauri + Svelte"
- Build command: {BUILD_COMMAND}   # e.g., "npm run build", "cargo build"
- Test command: {TEST_COMMAND}     # e.g., "npm test", "pytest"
- Lint command: {LINT_COMMAND}     # e.g., "npm run lint", "cargo clippy"
- Source dir: {SRC_DIR}            # e.g., "src/", "lib/"
- Test dir: {TEST_DIR}            # e.g., "tests/", "__tests__/"
4.2 Agent Team Structure (ASCII Trees)

When prompts use agent teams, define the hierarchy explicitly as an ASCII tree:

Agent Team Structure:
  Lead (delegate mode -- never writes code directly)
  +-- Teammate A: domain-auth
  |   Owns: src/auth/*, context/impl/impl-auth.md
  |   Dispatch: Agent tool
  +-- Teammate B: domain-data
  |   Owns: src/data/*, context/impl/impl-data.md
  |   Dispatch: Agent tool
  +-- Teammate C: domain-ui
      Owns: src/ui/*, context/impl/impl-ui.md
      Dispatch: Agent tool

Why: Agents need to understand their role and what they own. Dispatch subagents via the Agent tool. After merging a subagent's branch, the caller must clean up: git branch -D <branch>.

4.3 Batching Rules
  • Max 3 concurrent teammates per batch. Prevents resource exhaustion and race conditions.
  • Batch phases: Spawn batch 1 (3 teammates) -> wait for completion -> shutdown -> spawn batch 2.
  • Max 3 sub-agents per teammate. Sub-agents handle discrete subtasks (reading docs, running tests) to preserve the teammate's context window.
Execution Timeline:
  Batch 1: [Teammate A] [Teammate B] [Teammate C]
           ─────────────────────────────────────────> complete, shutdown
  Batch 2: [Teammate D] [Teammate E] [Teammate F]
           ─────────────────────────────────────────> complete, shutdown
4.4 File Ownership Tables

Assign each shared file to exactly one teammate to eliminate merge conflicts:

markdown
## File Ownership
| File/Pattern | Owner |
|-------------|-------|
| `src/auth/**` | domain-auth |
| `src/data/**` | domain-data |
| `src/ui/**` | domain-ui |
| `src/shared/types.ts` | domain-data |
| `context/impl/impl-auth.md` | domain-auth |

Rule: If two teammates need to modify the same file, assign ownership to one and have the other request changes through the lead.

4.5 Exit Criteria and Completion Signals

Every prompt must end with explicit exit criteria and a completion signal:

markdown
## Exit Criteria
- [ ] All T- tasks are DONE or documented as BLOCKED
- [ ] {BUILD_COMMAND} passes with zero errors
- [ ] {TEST_COMMAND} passes with zero failures
- [ ] All modified source files have corresponding test coverage
- [ ] context/impl/ updated with current status

## Completion Signal
When ALL exit criteria are met, output exactly:
<all-tasks-complete>

This signal is used by the iteration loop to detect when to stop.
4.6 Spawn Templates

Teammates are fresh processes with no inherited history. Every spawn must include full context:

markdown
## Spawn Template for Teammate

You are implementing {DOMAIN} for the {PROJECT_NAME} project.

### Your Role
- You own: {FILE_PATTERNS}
- Dispatched via the Agent tool
- Your impl tracking: context/impl/impl-{DOMAIN}.md

### Context to Read First
1. context/kits/spec-{DOMAIN}.md (WHAT to build)
2. context/plans/plan-{DOMAIN}.md (HOW to build it)
3. context/impl/impl-{DOMAIN}.md (what has been done)
4. git log --oneline -20 (recent history)

### Task
{TASK_DESCRIPTION}

### Exit Criteria
- [ ] {CRITERIA}

### Halting Conditions
- Do NOT push to remote unless explicitly asked
- Do NOT modify files outside your ownership
- If blocked for more than 20 minutes, document the blocker and stop
4.7 Halting Conditions

Explicit halting conditions prevent irreversible or wasteful actions:

markdown
## Halting Conditions
- Do NOT push to remote unless explicitly asked
- Do NOT modify files outside your file ownership table
- Do NOT delete test files or skip failing tests
- If a task takes more than 20 minutes, document findings and move on
- If you encounter a circular dependency, document it and stop
- Commit frequently to preserve progress
4.8 Sub-Agent Delegation

Teammates should delegate discrete subtasks to sub-agents to preserve their own context window:

markdown
## When to Use Sub-Agents
- Reading large documentation files
- Running and parsing test output
- Generating boilerplate code
- Researching framework APIs
- Performing file-by-file migrations

## Sub-Agent Rules
- Max 3 concurrent sub-agents per teammate
- Each sub-agent gets a focused, self-contained task
- Sub-agent results are summarized back to the teammate
- Sub-agents do NOT inherit the teammate's conversation history

5. Task Template Standardization

Use standardized task templates for consistent tracking across prompts:

Show full SKILL.md (547 more words)Show less
Task ID format
markdown
### T-{DOMAIN}-{NUMBER}: {Task Title}
- **Status:** TODO | IN_PROGRESS | DONE | BLOCKED
- **blockedBy:** T-{OTHER_DOMAIN}-{NUMBER} (if applicable)
- **Files:** {list of files to create or modify}
- **Acceptance criteria:**
  - [ ] {criterion 1}
  - [ ] {criterion 2}
Dependency tracking with blockedBy
markdown
### T-AUTH-001: Implement login flow
- **Status:** TODO
- **blockedBy:** T-DATA-001 (needs user model)

### T-DATA-001: Create user data model
- **Status:** IN_PROGRESS
- **blockedBy:** none
Conditional and dynamic tasks
markdown
### T-UI-005: Implement dark mode [CONDITIONAL]
- **Skip if:** {FRAMEWORK} does not support CSS variables
- **Status:** TODO

### T-PERF-001: Optimize hot paths [DYNAMIC]
- **Created when:** Performance gate identifies bottlenecks
- **Status:** not yet created

[CONDITIONAL] tasks include a skip condition -- if the condition is met, the task is skipped without failure.

[DYNAMIC] tasks are placeholders created at runtime when a specific trigger occurs. They do not exist in the initial plan.


6. Time Guards

Per-task time budgets prevent agents from spending too long on any single task:

CategoryBudgetExamples
Mechanical10 minutesFile creation, boilerplate, simple refactors
Investigation20 minutesDebugging, researching APIs, understanding existing code
Category budget20 minutesTotal time for all tasks in one category before escalating
Time guard rules
  1. Set expectations in the prompt:

    markdown
    ## Time Guards
    - Mechanical tasks (file creation, boilerplate): 10 min max
    - Investigation tasks (debugging, research): 20 min max
    - If you hit a time guard, document your findings and move to the next task
    - Do NOT silently retry -- document the blocker
  2. Hard stops: When a time guard is hit, the agent must:

    • Document what was attempted
    • Document what was learned
    • Document the blocker or open question
    • Move to the next unblocked task
  3. Escalation: If an agent hits time guards on multiple related tasks, this signals a systemic issue (fuzzy spec, missing dependency, architectural problem). Document it as a pattern, not individual failures.


7. Prompt File Naming Convention

context/prompts/
+-- 000-generate-specs-from-code.md    # Brownfield only (bootstrap, runs once)
+-- 001-generate-specs-from-refs.md    # Greenfield spec generation
+-- 002-generate-plans-from-specs.md   # Plan generation
+-- 003-generate-impl-from-plans.md    # Implementation
+-- 004-validate-specs.md              # Spec validation (rewrite pipelines)
+-- 005-backpropagate.md               # Back-propagation (rewrite pipelines)
Naming rules
  • Three-digit prefix for ordering (000, 001, 002...)
  • Verb-noun format describing the transformation (generate-specs-from-refs)
  • Lower prompt numbers are upstream (closer to specs)
  • Higher prompt numbers are downstream (closer to code)
  • 000 is reserved for brownfield bootstrap (runs once, not in the main loop)

8. Designing Your Pipeline

Step-by-step process
  1. Identify your project type: Greenfield (start from refs) or Rewrite (start from old code)?
  2. Start with the minimum pipeline: Greenfield = 3 prompts. Rewrite = 6 prompts.
  3. Write prompt 001 first: This is always the spec generation step.
  4. Define your runtime variables: What framework, build command, test command?
  5. Set exit criteria for each prompt: What must be true before moving to the next phase?
  6. Add agent teams if needed: For large projects, add team structure and file ownership.
  7. Run the pipeline with the iteration loop: Start with a small number of iterations (3-5) and increase as needed.
  8. Watch for convergence: Exponentially decreasing changes = convergence. Flat or oscillating changes = fix your specs.
When to add more prompts
  • If a single prompt is trying to do too much (reading AND writing specs, for example), split it.
  • If you see a phase producing inconsistent results, add a validation prompt between phases.
  • If back-propagation is frequent, add an explicit back-propagation prompt (006 in the rewrite pattern).

9. Iteration Loop Integration

Prompts are designed to run inside an iteration loop that repeats them until convergence:

bash
# Greenfield: Run implementation prompt with iteration loop
# -n 10: max 10 iterations
# -t 1h: 1 hour timeout per iteration
iteration-loop context/prompts/003-generate-impl-from-plans.md -n 10 -t 1h

# Leader-follower pattern: staggered pipeline
# Terminal 1: Specs (leader)
iteration-loop context/prompts/001-generate-specs-from-refs.md -n 5 -t 2h

# Terminal 2: Plans (follower, 1h delay)
iteration-loop context/prompts/002-generate-plans-from-specs.md -n 5 -t 2h -d 1h

# Terminal 3: Implementation (follower, 2h delay)
iteration-loop context/prompts/003-generate-impl-from-plans.md -n 10 -t 1h -d 2h

The iteration loop handles: iteration counting, timeouts, nudging idle agents, detecting completion signals, and graceful stop on convergence.


Cross-References

  • Prompt engineering details: See references/prompt-engineering.md for the complete reference on runtime inputs, spawn templates, task templates, time guards, and file ownership.
  • Agent team patterns: See references/agent-team-patterns.md for coordination patterns, batching, agent isolation, and merge protocol.
  • Convergence monitoring: See ck:convergence-monitoring skill for detecting when the iteration loop should stop.
  • Revision: See ck:revision skill for how prompt 006 traces bugs back to specs.
  • Context architecture: See ck:context-architecture skill for the directory structure that prompts read from and write to.

© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/JuliusBrussee/blueprint/skills/prompt-pipeline of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 3e1456a

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Questions about Prompt Pipeline

What does Prompt Pipeline do?

How to design the numbered prompt pipeline that drives Hunt phases in Cavekit. Prompt Pipeline is an agent skill from hashgraph-online/awesome-codex-plugins. How to design the numbered prompt pipeline that drives Hunt phases in Cavekit.

When should I use Prompt Pipeline?

Prompt Pipeline fits situations like: phrases: prompt pipeline; design prompts for SDD; create Hunt prompts; pipeline prompts.

How do I install Prompt Pipeline in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill prompt-pipeline -a claude-code`. Or copy the skill folder (plugins/JuliusBrussee/blueprint/skills/prompt-pipeline in hashgraph-online/awesome-codex-plugins) into .claude/skills/prompt-pipeline in your project. Claude Code loads it when a task matches its description.

How do I install Prompt Pipeline in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill prompt-pipeline -a codex`. Or copy the skill folder (plugins/JuliusBrussee/blueprint/skills/prompt-pipeline in hashgraph-online/awesome-codex-plugins) into .agents/skills/prompt-pipeline in your project. Codex loads it when a task matches its description.

Can I use Prompt Pipeline 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 hashgraph-online/awesome-codex-plugins --skill prompt-pipeline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-pipeline, .gemini/skills/prompt-pipeline, .github/skills/prompt-pipeline and .opencode/skills/prompt-pipeline in your project.

What does Prompt Pipeline need to run?

Going by SKILL.md and its folder, Prompt Pipeline needs the command-line tools its instructions call (git).

Does Prompt Pipeline access the network?

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

Is Prompt Pipeline safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prompt Pipeline use?

Prompt Pipeline is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prompt Pipeline use?

About 4.7k tokens (SKILL.md is roughly 19k 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 Prompt Pipeline?

Skills that share tags, products or a category with Prompt Pipeline: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 618 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt Pipeline?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.