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 agentmods add skills/cintia09/codenook/module-analysisnpx skills add cintia09/CodeNook --skill module-analysisgit clone --depth 1 https://github.com/cintia09/CodeNookWhat 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 | $0.00002 | $0.00342 |
| Opus 5 | $0.00001 | $0.00171 |
| Sonnet 5 | $0.00000 | $0.00068 |
| Haiku 4.5 | $0.00000 | $0.00034 |
Grade A, and why
module-analysis 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 2d 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.
What it actually says
Module Analysis Skill Template
Use this template to author a domain-specific analysis skill for a module, service, component, or subsystem.
When to author one
Create a per-module analysis skill when a module has:
- log-prefix conventions or signature patterns;
- a non-trivial state machine;
- recurring known-issue fingerprints;
- module-specific review or verification criteria.
Directory layout
Place each new skill in one of:
.codenook/memory/skills/<module>-analysis/SKILL.mdfor deployment-specific or team-specific skills;<plugin>/skills/<module>-analysis/SKILL.mdonly when the skill is generic enough to ship with a plugin.
Frontmatter contract
---
id: "<slug>"
name: "<slug>"
title: "<Human title>"
summary: "<one-paragraph purpose>"
tags:
- "skill"
- "<module>"
- "analysis"
---
Do not add unsupported frontmatter fields.
Recommended body sections
- Module overview.
- Log patterns.
- State machine / normal flow.
- Error handling and retry semantics.
- Known issues and fingerprints.
- Review checklist.
- Verification checks.
Every claim should be traceable to code, logs, documentation, or a completed case record.
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.
- 2d ago First seen · 65 lines · 2 tokens per session scan A 9f4afe76765d
module-analysis is a skill published in the GitHub repository cintia09/CodeNook (5 stars, last pushed 3mo ago), licensed MIT. It adds 2 tokens to every session and 342 once invoked, about $0.0000 per session on Opus 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 2026-08-31.
Other skills, from other repositories
skill-template
技能创建模板。复制此目录并修改 SKILL.md 即可创建新技能。请将所有 替换为实际内容。.
skill-creator
引导用户创建和编辑符合规范的 SKILL.md 技能包。支持 DesireCore 完整格式 （frontmatter 元数据 + L0/L1/L2 分层内容 + 脚本/参考/资产）和 Claude Code 基础格式。Use when 用户要求创建新技能、更新已有技能、或将经验封装为可复用 的技能包。.
Package Audit
Produces a rigorous, forward-looking technical audit with severity ratings and improvement directions.
Project Analyzer
Produces a concise, structured analysis report of a codebase: dependencies, workflow, and context.
Performance Optimizer
Systems-level performance engineer: identifies hotspots, provides optimized code with resource justifications.
repomix
Pack and analyze codebases into AI-friendly single files using Repomix. Use when the user wants to explore repositories, analyze code structure, find patterns, check token counts, or prepare codebase context for AI analysis. Supports both local directories and remote GitHub repositories.