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Agent skill

Analyze Agent Codebase

by murray17 in murray17/rovai-ai

A skill your agent uses to analyze an agent system's architecture or mechanisms from repository evidence, including follow-up questions and analysis documents.

MITAuto-check passedDevelopment

Install Analyze Agent Codebase

skills CLI
$ npx skills add murray17/rovai-ai --skill analyze-agent-codebase -a claude-code

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

GitHub CLI
$ gh skill install murray17/rovai-ai analyze-agent-codebase --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/murray17/rovai-ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyze-agent-codebase .claude/skills/analyze-agent-codebase && 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
analyze-agent-codebase
GitHub stars
138
Token cost
~1.1k tokens
SKILL.md length
495 words
Files
4 (incl. references)
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses to analyze an agent system's architecture or mechanisms from repository evidence, including follow-up questions and analysis documents.

  • Works in 6 steps: Freeze scope. Record repository root,… → Trace the runtime. Follow entry point ->… → Trace each question vertically. Use a… → …
  • Analyze an agent systems architecture
  • SKILL.md covers Boundaries, Choose the scope, Investigate and Deliver
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Analyze Agent Codebase is an agent skill from murray17/rovai-ai. Use to analyze an agent system's architecture or mechanisms from repository evidence, including follow-up questions and analysis documents. Exclude ordinary code review, implementation, fixes, and conceptual questions that need no repository evidence.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `agents/openai.yaml` and `references/dossier-structure.md`).

It sits in Development. The repository describes itself as: Desktop and web workspace for lasting teams of coding agents, with shared conversations, task delegation, and collaborative memory. The licence is MIT.

When your agent uses it

  • Analyze an agent systems architecture
  • Mechanisms from repository evidence
  • Including follow-up questions and analysis documents

Example prompts

  • “/analyze-agent-codebase”

Workflow steps

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

  1. Freeze scope. Record repository root, revision, requested questions, exclusions, output format, languages, build entry points, generated…
  2. Trace the runtime. Follow entry point -> configuration and dependency wiring -> agent/workflow construction -> execution loop -> model…
  3. Trace each question vertically. Use a real trigger: input -> authorization and validation -> state change -> effects -> result -> error…
  4. Record evidence as you read. Use the table below. A claim about subagents, for example, needs the creator, context transfer, isolation…
  5. Explain ownership. Identify control and state authority, sync/async connections, context/session/Memory/history lifecycles…
  6. Cross-check. Reverse-reference key symbols to verify production wiring. Inspect tests, schemas, flags, platforms, adapters, and alternate…

What it can do on your machine

Read from SKILL.md and the folder at commit ed90fa9. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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

Analyze Agent Codebase loads about 1.1k tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 495 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

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 murray17/rovai-ai at commit ed90fa9, republished under its MIT licence (© murray17). 495 words, ~1,081 tokens.

Download SKILL.mdSave it as .claude/skills/analyze-agent-codebase/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyze-agent-codebase
description
Use to analyze an agent system's architecture or mechanisms from repository evidence, including follow-up questions and analysis documents. Exclude ordinary code review, implementation, fixes, and conceptual questions that need no repository evidence.

Analyze Agent Codebases

Reconstruct behavior from real entry points, call chains, state transitions, and persistence. Documentation explains intent; code and tests establish implementation. Respond in the user's language unless asked otherwise.

Boundaries

  • Follow repository instructions, documentation routes, and read-only constraints.
  • Default to read-only. Write analysis documents only when requested; do not modify implementation.
  • Use source, dependency wiring, configuration, schemas, migrations, and tests as evidence.
  • Label important claims confirmed, inferred, or unknown, localizing these labels in the report. Explain inferences and gaps.
  • Cite paths, symbols, and relevant entry-to-effect call chains. Names such as agent, memory, plan, or tool do not prove capabilities.

Choose the scope

Use the smallest sufficient scope: a vertical slice for a mechanism question, an architecture report for several mechanisms, or a dossier when multiple documents are requested. For a dossier, read analysis axes and structure.

Check existing analyses for scope, evidence, and revision; update the appropriate document instead of creating duplicate overviews.

Investigate

  1. Freeze scope. Record repository root, revision, requested questions, exclusions, output format, languages, build entry points, generated directories, and initial worktree state. Distinguish production code from tests, fixtures, examples, generated code, vendors, and historical documents.
  2. Trace the runtime. Follow entry point -> configuration and dependency wiring -> agent/workflow construction -> execution loop -> model, tool, collaboration, and persistence effects -> events, recovery, and presentation. Follow registries through loaders, macros, decorators, or configuration until the actual implementation is connected.
  3. Trace each question vertically. Use a real trigger: input -> authorization and validation -> state change -> effects -> result -> error and recovery. Select only mechanisms present in the code.
  4. Record evidence as you read. Use the table below. A claim about subagents, for example, needs the creator, context transfer, isolation, and result path.
  5. Explain ownership. Identify control and state authority, sync/async connections, context/session/Memory/history lifecycles, tool/Skill/prompt/permission boundaries, and failure, retry, cancellation, idempotency, and recovery limits. Record documentation drift.
  6. Cross-check. Reverse-reference key symbols to verify production wiring. Inspect tests, schemas, flags, platforms, adapters, and alternate entries. Tests prove only covered behavior; mark unexecuted checks not_run. Compare authoritative documentation unless the user prohibits reading it.
ClaimStatusSource and call chainTest or runtime evidenceLimits or counterevidence
Falsifiable statementconfirmed / inferred / unknownpath:line + symboltest / fixture / tracegap or conflicting path
Show full SKILL.md (125 more words)Show less

Deliver

Lead with conclusions and the runtime picture. Include scope and revision, key flows, each requested mechanism and its evidence status, tradeoffs, constraints, documentation drift, valuable unknowns and verification steps, and source locations or dossier reading order. Quote only the minimum useful code.

For independent evidence domains, bounded collaboration may help when authorized and available. Specify the question, permitted scope, exclusions, evidence format, and stopping condition. The lead retains runtime topology, cross-domain flows, evidence spot checks, conflict resolution, and final conclusions. Avoid overlapping overviews; proceed alone without a suitable collaborator.

Before delivery, trace each major claim back to evidence, separate confirmed facts from inference and unknowns, cover all requested topics, and preserve one dossier entry point. Verify that changes are limited to authorized analysis artifacts.

© murray17, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/analyze-agent-codebase of murray17/rovai-ai.

  • SKILL.md
  • NOTICE
  • agents/openai.yaml
  • references/dossier-structure.md

Open the folder on GitHubat commit ed90fa9

Compare with similar skills

Analyze Agent Codebase 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.

Analyze Agent Codebase compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Analyze Agent Codebase this skillmurray17/rovai-ai138—~1.1kAutomated safety check: PassMIT
Trellis Session Insightmindfold-ai/Trellis15k4 repos~1.7kAutomated safety check: PassAGPL-3.0
Openspec Verify ChangeFission-AI/OpenSpec71k2 repos~4.6kAutomated safety check: PassMIT
Warp Factory Fileswarpdotdev/warp65k1 repos~2.5kAutomated safety check: PassAGPL-3.0
Migrate Core Code to Submodulestinyhumansai/openhuman42k—~2.6kAutomated safety check: PassGPL-3.0
Analyze Logsactivepieces/activepieces25k1 repos~1.6kAutomated safety check: PassMIT

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Categories

Questions about Analyze Agent Codebase

What does Analyze Agent Codebase do?

A skill your agent uses to analyze an agent system's architecture or mechanisms from repository evidence, including follow-up questions and analysis documents. Analyze Agent Codebase is an agent skill from murray17/rovai-ai. Use to analyze an agent system's architecture or mechanisms from repository evidence, including follow-up questions and analysis documents.

When should I use Analyze Agent Codebase?

Analyze Agent Codebase fits situations like: analyze an agent systems architecture; mechanisms from repository evidence; including follow-up questions and analysis documents.

How do I install Analyze Agent Codebase in Claude Code?

Run `npx skills add murray17/rovai-ai --skill analyze-agent-codebase -a claude-code`. Or copy the skill folder (skills/analyze-agent-codebase in murray17/rovai-ai) into .claude/skills/analyze-agent-codebase in your project. Claude Code loads it when a task matches its description.

How do I install Analyze Agent Codebase in Codex?

Run `npx skills add murray17/rovai-ai --skill analyze-agent-codebase -a codex`. Or copy the skill folder (skills/analyze-agent-codebase in murray17/rovai-ai) into .agents/skills/analyze-agent-codebase in your project. Codex loads it when a task matches its description.

Can I use Analyze Agent Codebase 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 murray17/rovai-ai --skill analyze-agent-codebase -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyze-agent-codebase, .gemini/skills/analyze-agent-codebase, .github/skills/analyze-agent-codebase and .opencode/skills/analyze-agent-codebase in your project.

What does Analyze Agent Codebase need to run?

SKILL.md names no scripts, command-line tools or credentials: Analyze Agent Codebase is instructions for the agent only.

Does Analyze Agent Codebase access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Analyze Agent Codebase 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 Analyze Agent Codebase use?

Analyze Agent Codebase is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyze Agent Codebase use?

About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.

What are the alternatives to Analyze Agent Codebase?

Skills that share tags, products or a category with Analyze Agent Codebase: Trellis Session Insight (mindfold-ai/Trellis, 15k stars), Openspec Verify Change (Fission-AI/OpenSpec, 71k stars), Warp Factory Files (warpdotdev/warp, 65k stars) and Migrate Core Code to Submodules (tinyhumansai/openhuman, 42k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyze Agent Codebase?

murray17 (a GitHub user) maintains it in murray17/rovai-ai, which has 138 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 8, 2026.

Source: murray17/rovai-ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.