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.
npx skills add ProsusAI/prism --skill analyze-agent-codebasegit clone --depth 1 https://github.com/ProsusAI/prismWrote 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.
[](https://agentmods.dev/skills/prosusai/prism/analyze-agent-codebase)<a href="https://agentmods.dev/skills/prosusai/prism/analyze-agent-codebase"><img src="https://agentmods.dev/badge/skills/prosusai/prism/analyze-agent-codebase/github.svg" alt="Measured on agentmods" height="20"></a>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.
<a href="https://agentmods.dev/skills/prosusai/prism/analyze-agent-codebase"><img src="https://agentmods.dev/badge/skills/prosusai/prism/analyze-agent-codebase.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.01475 |
| Opus 5.5 | $0.00013 | $0.00590 |
| Sonnet 5.5 | $0.00006 | $0.00295 |
| Haiku 4.5 | $0.00003 | $0.00147 |
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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Agent Codebase Skill
When to use this skill
Use this skill when the user wants to perform a comprehensive architectural analysis of an agentic AI codebase.
Instructions
Analyze the target codebase in four phases.
Each cluster has its own questions file in the skill directory (e.g. questions_cluster_a.md).
Global Token Rules
- Do NOT launch parallel subagents.
- Run clusters sequentially.
- Do NOT read the entire repository per cluster.
- Read ONLY the assigned cluster questions file.
- Read ONLY files relevant to the cluster questions.
- Index-driven lookup only — when a section says "Find" or "Find and read", do not search the filesystem. Use the Key File Registry in
index.md(already in context from Cluster A) to identify candidate files, then read only those. If a topic has no matching registry entry, write:Not indexed — verify Phase 1 captured all relevant files.
Global Output Rules
- Iventories: always use tables (tools, models, entities, tests)
- Findings with evidence: always use bullet points in format
- [finding] — evidence: path/to/file.ext:Lxx - Comparisons or yes/no pattern checks: always use a table with columns: Pattern | Implemented | Evidence
- Evidence format:
path/to/file.ext:Lxx-Lyy - NO prose or narrative explanations (except Section 14: Decision Extraction and Classification).
- NO restating questions.
- NO long evidence excerpts.
- Do NOT duplicate findings across sections.
- Write each cluster file immediately after completion.
Per-section Output Format:
- Provide Finding: what is implemented in the codebase.
- Provide Evidence: exact file path and line number (file.py:Lxx) with a direct code quote.
- If nothing is implemented, write exactly: No implementation.
- If partially implemented, write: Partial — [what exists] / [what's missing].
- Separate implemented-and-active, implemented-but-disabled, and not-implemented items.
- Do not speculate. Only use code-level evidence.
Output all results to {CODEBASE_ROOT}/_analysis/ directory inside the target codebase root.
What ships with it
7 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.
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.
- 2mo ago First seen · 145 lines · 32 tokens per session scan A 7cd103f49ae5
analyze-agent-codebase is a skill published in the GitHub repository ProsusAI/prism (20 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,475 once invoked, about $0.0001 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.
analyze-agent-codebase compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| analyze-agent-codebaseProsusAI/prism | Skill | 20 | 4mo ago | Apache-2.0 | A | 32 | Claude Code, Codex |
| autoreviewopenclaw/clawhub | Skill | 9.5k | yesterday | MIT | A | 17 | Claude Code, Codex |
| agent-teams-simplify-and-hardenLeoYeAI/openclaw-master-skills | Skill | 2.2k | 6d ago | MIT | A | 115 | Claude Code, Codex |
| omh-code-reviewrlaope/oh-my-hermes | Skill | 5.2k | yesterday | MIT | A | 48 | Claude Code, Codex |
| revdiff-planumputun/revdiff | Skill | 945 | yesterday | MIT | A | 84 | Claude Code |
| explore-unknownsdzhng/skills | Skill | 1.4k | 2d ago | MIT | A | 162 | Claude Code, Codex |
| re0-mergeLilMGenius/paperthin | Skill | 1.4k | 4d ago | MIT | A | 70 | Claude Code |