New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Scenario Asset Analysis

by scenario-labs in scenario-labs/skills

A skill your agent uses when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose…

MITAuto-check passed

Install Scenario Asset Analysis

skills CLI
$ npx skills add scenario-labs/skills --skill scenario-asset-analysis -a claude-code

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

GitHub CLI
$ gh skill install scenario-labs/skills scenario-asset-analysis --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/scenario-labs/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scenario-asset-analysis .claude/skills/scenario-asset-analysis && 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
scenario-asset-analysis
GitHub stars
946
Token cost
~1.8k tokens
SKILL.md length
928 words
Files
1
Skills in repo
146
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose…

  • Works in 5 steps: Collect the asset ids from the run… → asset_analyze with dry_run: true on the… → asset_analyze with images set to 10 ids… → …
  • Finished Scenario assets have to give something back: a caption for a dataset
  • SKILL.md covers Overview, Quick reference, The four facts that change the… and Finding an asset again, plus 2 more sections
  • Calls npx

What it does

Scenario Asset Analysis is an agent skill from scenario-labs/skills. Use when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose, or segmentation control map for the next model, or the asset itself found again by text, tags, or visual similarity and filed into a collection. Keywords: caption, describe, analyze, QA, control map, find similar, reverse image search, semantic search, tag, collection.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Get production-ready images, video, audio, and 3D from any AI agent: skills that pick the right model, price before spending, and keep characters and brands consistent through… The licence is MIT.

When your agent uses it

  • Finished Scenario assets have to give something back: a caption for a dataset
  • A reusable style description
  • A verdict against a brief
  • Segmentation control map for the next model

Example prompts

  • “/scenario-asset-analysis”

Requirements

  • Node.js

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Collect the asset ids from the run (jobs_wait returns them).
  2. asset_analyze with dry_run: true on the first chunk to price the pass.
  3. asset_analyze with images set to 10 ids and an instruction that states the brief and fixes the output: "For each image in order, reply…
  4. asset_describe on the strongest pass. Its promptable synthesis goes straight into the next batch's prompt, which holds the look without a…
  5. File the result: collection_create, then collection_add_assets in chunks of at most 49 ids. asset_add_tags is additive, so tag the…

What it can do on your machine

Read from SKILL.md and the folder at commit f6f8ab7. 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:

    • 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 no API keys, tokens, secrets or passwords.

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

Context cost

Scenario Asset Analysis loads about 1.8k tokens when it runs. Until then it costs about 118 tokens; SKILL.md has 928 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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 scenario-labs/skills at commit f6f8ab7, republished under its MIT licence (© scenario-labs). 928 words, ~1,768 tokens.

Download SKILL.mdSave it as .claude/skills/scenario-asset-analysis/SKILL.md (or your agent's skills folder).
name
scenario-asset-analysis
description
Use when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose, or segmentation control map for the next model, or the asset itself found again by text, tags, or visual similarity and filed into a collection. Keywords: caption, describe, analyze, QA, control map, find similar, reverse image search, semantic search, tag, collection.
license
MIT

Scenario Asset Analysis

Overview

Four tools read assets back instead of making new ones: three fixed-purpose, one open-ended. With search (default toolset) they answer the questions that come after a batch lands, which is where most of the work actually is: is this on brief, what look is this, what does this show, what can the next model condition on, where did last week's approved version go.

None of them are in the default toolset. Get schemas with scenario_tools_search, then run each through the executor matching its permission: asset_caption and asset_describe are read-class, asset_analyze and asset_detect are write-class. Or reconnect with ?toolsets=full. Connection and scope: see the scenario skill in this repo. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference

NeedToolShape
A caption for one imageasset_caption (read)details_level: action or action+style
A reusable look off one imageasset_describe (read)Returns a full description plus a promptable synthesis
Anything else, in your own wordsasset_analyze (write)instruction plus up to 10 images and 10 text_inputs
A conditioning map for another modelasset_detect (write)modality, one of ten including canny, depth, pose, segmentation

All four bill credits and all four take dry_run: true for an estimate first. Read-class does not mean free.

The four facts that change the plan

  • asset_analyze batches. One call carries up to 10 images against one instruction, so reviewing 200 assets is 20 calls, not 200. num_outputs (1 to 5) is unrelated: it returns several distinct answers to the same instruction, not one answer per image. Ask for a fixed per-image output shape in the instruction so the answers stay parseable.
  • asset_detect strips the background by default. remove_background defaults to true, so a depth or pose map comes back with the frame's context already gone. Set it false whenever the map has to cover the whole image.
  • It reads a render, not the file. On a vector or layered source it cannot confirm what the file is or report its exact values: one call called a 16-path SVG raster, shifted a #101614 fill to #131A18, and missed an off-palette path. Parse the file for exact color and structure; the tool judges what the render shows.
  • Fixed beats flexible when it fits. asset_caption and asset_describe are purpose-built and faster than instructing an LLM to do the same job. Reach for asset_analyze for classification, extraction, comparison, translation, or a verdict against a brief. One carve-out: for a structured pass/warn/fail verdict against a configured brand brief, teams with the Quality Gate add-on have a dedicated tool that stores its verdict and re-reads it free (see scenario-quality-gate); asset_analyze stays the route when the add-on is missing or the brief exists only as prose.

asset_analyze and asset_detect wait up to 180s and then hand back a job_id; carry on with jobs_wait as anywhere else.

Show full SKILL.md (437 more words)Show less

Finding an asset again

Retrieval is search with target="assets", and at least one of query, filters, filter, image, or images must be set: an empty call is a 400, not an everything-list.

  • By text. query is keyword matching by default; query_semantic_ratio moves it toward meaning (0.5 to 0.8 suits mood queries like "dark medieval atmosphere", 1 is pure semantic). sort_by (say ["createdAt:desc"]) is ignored while that ratio is above 0, so a newest-first list needs keyword mode.
  • By similarity. image takes one asset id or image URL and returns lookalikes; images takes {"like": [...], "unlike": [...]} to steer with positive and negative examples (the two fields are mutually exclusive). image_semantic_ratio decides what similar means: 1, the default, matches subject and mood; 0 matches image features, the setting for hunting near-duplicates and crops. Add a query beside it for "like these, but more stylized".
  • By structure. filters narrows on kind, tags, model_id, collection_ids, and created_after/created_before, ANDed with everything above.

Worked example: reviewing a batch against a brief

  1. Collect the asset ids from the run (jobs_wait returns them).
  2. asset_analyze with dry_run: true on the first chunk to price the pass.
  3. asset_analyze with images set to 10 ids and an instruction that states the brief and fixes the output: "For each image in order, reply <index>: pass|fail, <reason in under 12 words>, a reason on every line, passes included. Fail anything not centered, not on a plain field, or carrying text." Repeat per chunk. Answers land as text assets (one, or one per image): asset_download them to read the verdicts.
  4. asset_describe on the strongest pass. Its promptable synthesis goes straight into the next batch's prompt, which holds the look without a training run (see scenario-consistency); when the synthesis is only a short title, prompt with the description instead.
  5. File the result: collection_create, then collection_add_assets in chunks of at most 49 ids. asset_add_tags is additive, so tag the failures rather than rebuilding a tag set. The set comes back later with search filters={"collection_ids": [...]}, and its lookalikes with images={"like": [...]}.

Common mistakes

  • One asset_analyze call per asset when 10 fit in a call.
  • Treating num_outputs as a batch size over images.
  • Leaving remove_background at its default on an asset_detect map that must match the source frame.
  • Running asset_analyze or asset_detect through scenario_tool_execute_read: both are write-class and the call is rejected by lane, not by argument.
  • Sending more than 49 ids to collection_add_assets, or one id already in the collection: both fail the whole call, and nothing from that chunk is filed.
  • Asking asset_analyze to produce an image. It returns text; control maps come from asset_detect and final renders from model_run.

© scenario-labs, MIT. 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 skills/scenario-asset-analysis of scenario-labs/skills.

Open the folder on GitHubat commit f6f8ab7

Compare with similar skills

Scenario Asset Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scenario Asset Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scenario Asset Analysis this skillscenario-labs/skills946—~1.8kAutomated safety check: PassMIT
AssetsBuilderIO/agent-native7.1k—~1.2kAutomated safety check: PassNone
Game Asset AuditDonchitos/Claude-Code-Game-Studios26k—~2kAutomated safety check: PassMIT
3D Asset Generationcalesthio/OpenMontage66k—~1.2kAutomated safety check: PassAGPL-3.0
Game Asset Spec WriterDonchitos/Claude-Code-Game-Studios26k—~5kAutomated safety check: PassMIT
Asset Inventorysickn33/agentic-awesome-skills47k2 repos~3.4kAutomated safety check: PassMIT

Similar skills

  • Assets

    BuilderIO/agent-native

    Use Agent-Native Assets for image and video generation requests, brand-safe asset search/export, and human-in-the-loop asset selection through the hosted Assets MCP app.

    7.1k GitHub stars~1.2k tokensUpdated yesterday
    Agent WorkflowsAuto-check passed

  • Game Asset Audit

    Donchitos/Claude-Code-Game-Studios

    Audits game assets against naming conventions, file size budgets and format standards, and finds orphaned assets and missing references.

    26k GitHub stars~2k tokensUpdated 3 days ago
    Game DevelopmentAuto-check passed

  • 3D Asset Generation

    calesthio/OpenMontage

    Generate, reconstruct, inspect, and route production 3D assets for OpenMontage worlds using Atlas Cloud, fal.ai, licensed catalogs, and Blender.

    66k GitHub stars~1.2k tokensUpdated 8 days ago
    Game DevelopmentAuto-check passed

  • Game Asset Spec Writer

    Donchitos/Claude-Code-Game-Studios

    Writes per-asset visual specs and AI image-generation prompts for a game's characters, enemies and screens, driven by the GDD, art bible and an entity inventory.

    26k GitHub stars~5k tokensUpdated 3 days ago
    Game DevelopmentAuto-check passed

  • Asset Inventory

    sickn33/agentic-awesome-skills

    Maintain IT asset inventory and configuration management database.

    47k GitHub starsUsed in 2 repos~3.4k tokens
    Auto-check passed

  • Asset It Management

    sickn33/agentic-awesome-skills

    Asset and IT register: serial, model, condition, assignee, location, purchase value, warranty expiry and return date, as CSV, SQL, JSON Schema or Notion on request.

    47k GitHub starsUsed in 1 repo~4k tokens
    Documents & OfficeAuto-check passed

More from scenario-labs/skills

All 146 skills in this repo

  • Scenario Blender Grease Pencil

    scenario-labs/skills

    A skill your agent uses when drawing or animating with Grease Pencil in Blender 5.x from Python: 2D or 2.5D illustration, frame-by-frame animation, a cutout or part-based 2D character, strokes with…

    946 GitHub stars~4.5k tokensUpdated yesterday
    Auto-check passed

  • Scenario Blender Hair

    scenario-labs/skills

    A skill your agent uses when grooming hair or fur in Blender with hair curves, such as a character hairstyle, animal fur, procedural fur in geometry nodes, or hair cards and mesh hair for games.

    946 GitHub stars~4.7k tokensUpdated yesterday
    Auto-check passed

  • Scenario Blender Lighting Rendering

    scenario-labs/skills

    A skill your agent uses when lighting, rendering or compositing in Blender: light a character, product or hero shot, interior at dusk or night, three-point or motivated lighting, sun and sky, HDRI…

    946 GitHub stars~5k tokensUpdated yesterday
    Auto-check passed

  • Scenario Chatgpt Pet Create

    scenario-labs/skills

    A skill your agent uses when creating a ChatGPT pet or Codex pet with Scenario: hatching an animated companion from a text idea, a character, mascot or brand cue, or reference photos and art; making…

    946 GitHub stars~3.6k tokensUpdated yesterday
    Auto-check passed

  • Scenario Godot Animation

    scenario-labs/skills

    A skill your agent uses when animating characters or scenes in Godot 4.7: AnimationPlayer clips and RESET, AnimationTree state machines and blend spaces built in code, Mixamo or glTF import, loop…

    946 GitHub stars~4.7k tokensUpdated yesterday
    Auto-check passed

  • Scenario Godot Audio

    scenario-labs/skills

    A skill your agent uses when adding or fixing sound in Godot 4.7: audio buses and effects, volume sliders, 'too many sounds', combat audio with hundreds of enemies, sounds clipping or distorting, 3D…

    946 GitHub stars~4.7k tokensUpdated yesterday
    Auto-check passed

Questions about Scenario Asset Analysis

What does Scenario Asset Analysis do?

A skill your agent uses when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose…. Scenario Asset Analysis is an agent skill from scenario-labs/skills. Use when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose, or segmentation control map for the next model, or the asset itself found again by text, tags, or visual similarity and filed into a collection.

When should I use Scenario Asset Analysis?

Scenario Asset Analysis fits situations like: finished Scenario assets have to give something back: a caption for a dataset; A reusable style description; A verdict against a brief; segmentation control map for the next model.

How do I install Scenario Asset Analysis in Claude Code?

Run `npx skills add scenario-labs/skills --skill scenario-asset-analysis -a claude-code`. Or copy the skill folder (skills/scenario-asset-analysis in scenario-labs/skills) into .claude/skills/scenario-asset-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Scenario Asset Analysis in Codex?

Run `npx skills add scenario-labs/skills --skill scenario-asset-analysis -a codex`. Or copy the skill folder (skills/scenario-asset-analysis in scenario-labs/skills) into .agents/skills/scenario-asset-analysis in your project. Codex loads it when a task matches its description.

Can I use Scenario Asset Analysis 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 scenario-labs/skills --skill scenario-asset-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scenario-asset-analysis, .gemini/skills/scenario-asset-analysis, .github/skills/scenario-asset-analysis and .opencode/skills/scenario-asset-analysis in your project.

What does Scenario Asset Analysis need to run?

Going by SKILL.md and its folder, Scenario Asset Analysis needs the command-line tools its instructions call (npx). Our summary lists: Node.js.

Does Scenario Asset Analysis 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 Scenario Asset Analysis 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 Scenario Asset Analysis use?

Scenario Asset Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scenario Asset Analysis use?

About 1.8k tokens (SKILL.md is roughly 7.1k 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 Scenario Asset Analysis?

Skills that share tags, products or a category with Scenario Asset Analysis: Assets (BuilderIO/agent-native, 7.1k stars), Game Asset Audit (Donchitos/Claude-Code-Game-Studios, 26k stars), 3D Asset Generation (calesthio/OpenMontage, 66k stars) and Game Asset Spec Writer (Donchitos/Claude-Code-Game-Studios, 26k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scenario Asset Analysis?

scenario-labs (a GitHub organization) maintains it in scenario-labs/skills, which has 946 GitHub stars. The repository holds 146 skills in this directory. The repository was last updated on October 10, 2026.

Source: scenario-labs/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.