analyze-agent-codebase

analyze-agent-codebase is a skill for Codex from murray17/rovai-ai. It costs 47 tokens per session (813 once invoked), scanned A, original, MIT.

A method for examining a coding-agent or multi-agent codebase by tracing its real execution path from entry point to tools, state, storage, and results. It relies on source code, configuration, schemas, migrations, and tests rather than assumptions from names.

In plain words
What is it for?
Mapping agent architecture, following a specific mechanism through the code, and documenting confirmed facts, inferences, evidence, and unknowns.
Why use it?
It helps explain how the system actually works when documentation is incomplete or misleading.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions subagents.

Good fit Mapping agent architecture, following a specific mechanism through the code, and documenting confirmed facts, inferences, evidence, and unknowns.

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Install with agentmods
npx agentmods add skills/murray17/rovai-ai/analyze-agent-codebase
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add murray17/rovai-ai --skill analyze-agent-codebase
Clone the repo
git clone --depth 1 https://github.com/murray17/rovai-ai

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyze-agent-codebase

README.md
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Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/murray17/rovai-ai/analyze-agent-codebase"><img src="https://agentmods.dev/badge/skills/murray17/rovai-ai/analyze-agent-codebase.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 813 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00047 $0.00813
Opus 5.5 $0.00019 $0.00325
Sonnet 5.5 $0.00009 $0.00163
Haiku 4.5 $0.00005 $0.00081

Measured 20d ago against content hash 7ae81b15888b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

analyze-agent-codebase scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 20d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/analyze-agent-codebase/SKILL.md · 44 lines

How it starts

The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 44 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 20d ago Changed · -82 lines · -52 tokens per session 7ae81b15888b
  2. 1mo ago First seen · 126 lines · 99 tokens per session scan A f4eb72dee133

Subscribe to this mod's changes

analyze-agent-codebase is a skill published in the GitHub repository murray17/rovai-ai (201 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 813 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.