codebase-audit

A workflow for reviewing an entire codebase for security, reliability, logic, and code-quality problems. It records findings as GitHub issues, then fixes them separately and submits pull requests.

In plain words
What is it for?
Use it for full code audits, recurring health checks, and dependency-security reviews. It is not intended for fixing one known bug or building a new feature.
Why use it?
It turns a broad code review into tracked work with isolated changes, so problems are easier to follow and review.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/tencentcloudbase/cloudbase-ai-toolkit/codebase-audit
Any agent
npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill codebase-audit
Clone the repo
git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit

Made for: Claude Code, Codex.

Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,752 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00078 $0.01752
Opus 5 $0.00039 $0.00876
Sonnet 5 $0.00016 $0.00350
Haiku 4.5 $0.00008 $0.00175

Measured 2d ago against content hash d47e408ad359, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

codebase-audit 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.

skills/codebase-audit/SKILL.md · 158 lines

How it starts

The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Codebase Audit → Issue → Worktree Fix → PR

End-to-end workflow: systematically review the entire codebase, report findings as GitHub issues, fix each issue in an isolated git worktree, and submit PRs — all in one session.

When to use this skill

Use this skill when you need to:

  • Perform a full code review / audit of the codebase
  • Proactively find security vulnerabilities, logic bugs, or code quality problems
  • Turn code review findings into tracked GitHub issues
  • Fix each issue in isolation (worktree per issue) and submit PRs
  • Run a periodic codebase health check with automated follow-through
  • Audit and fix dependency security vulnerabilities (Dependabot alerts / npm audit)

Do NOT use for:

  • Reviewing or fixing a single known bug (use systematic-debugging or direct fix)
  • Triaging existing open PRs (use pr-review-fix)
  • Processing attribution issues (use mcp-attribution-worktree)
  • Feature development or refactoring unrelated to audit findings

Workflow

Phase 1 — Review

  1. Read references/review-strategy.md for the review scope and checklist.
  2. Use the code-explorer subagent to read ALL source files in the target directory (default: mcp/src/).
  3. For each file, systematically check against the review checklist:
    • Security: path traversal, injection, unvalidated input, hardcoded secrets, improper error exposure
    • Error handling: missing try-catch, swallowed errors, error messages leaking internals
    • Type safety: as any, unsafe casts, missing null checks
    • Logic bugs: race conditions, incorrect conditionals, unreachable code
    • Code quality: dead code, duplication, overly complex functions
    • Resource leaks: unclosed connections, missing cleanup
    • API design: inconsistent validation, missing required field checks
  4. Record every finding with: file path, line number(s), category, severity (Critical/High/Medium/Low), description, and suggested fix.
  5. Dependency scan: Read references/dependency-audit.md and run the Dependabot alert fetch + npm audit to discover vulnerable dependencies. Record each finding using the dependency-audit format.

Read the full file on GitHub · 158 lines

Files

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.

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. 2d ago First seen · 158 lines · 78 tokens per session scan A d47e408ad359

Subscribe to this mod's changes

codebase-audit is a skill published in the GitHub repository TencentCloudBase/CloudBase-AI-Toolkit (1,087 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,752 once invoked, about $0.0004 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 2026-08-30.

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