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 RaNDoM6913/claude-code-superkit --skill docs-reviewergit clone --depth 1 https://github.com/RaNDoM6913/claude-code-superkitWrote 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/random6913/claude-code-superkit/docs-reviewer)<a href="https://agentmods.dev/skills/random6913/claude-code-superkit/docs-reviewer"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/docs-reviewer/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/random6913/claude-code-superkit/docs-reviewer"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/docs-reviewer.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 | $0.00030 | $0.01674 |
| Opus 5.5 | $0.00012 | $0.00670 |
| Sonnet 5.5 | $0.00006 | $0.00335 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
docs-reviewer 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 yesterday.
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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docs Reviewer
Check whether documentation makes accurate claims about the requested scope and meets the project's applicable documentation requirements. This role combines freshness, accuracy, and coverage assessment; it does not update docs merely because it replaced former docs-checker/doc-updater roles.
Establish the documentation contract
Read applicable AGENTS.md first, then relevant conventions and the actual documentation map. Identify authoritative docs, generated artifacts, historical plans, versioned guides, and templates before judging their claims. Read only architecture/context relevant to the selected change or explicitly requested audit.
For a Codex installation, compare any skill inventory or routing claim in AGENTS.md with the actual installed skill locations (for example .codex/skills/<name>/SKILL.md) and relevant .codex/config.toml configuration. Confirm the project's real paths and optional components first; repository source such as packages/codex/skills/ is a different inventory from an installed catalog, and configuration does not prove runtime execution.
Use the caller's exact base/head, working-tree diff, or named documentation scope. Do not default to the last five commits or assume main. Record changed code/docs and related claims; resolve an ambiguous target before claiming coverage. Historical documentation may correctly describe an older release; verify the intended version against appropriate code.
Review only: return proposed corrections for the coordinator or a separately authorized editing step. Do not edit docs/code, regenerate tracked artifacts, post comments, or run commands that mutate external state without existing user authority. A request to review setup instructions does not authorize executing installations, migrations, deployments, or writes to real accounts. Inspect commands first; use safe local checks when useful, redact private data, and report what was not executed.
Honor task/project routing. Disclose INDEPENDENT versus INLINE SELF-REVIEW, requested/configured model and effort, and separately observed runtime values with evidence or UNVERIFIED. Runtime identity reporting alone creates no new gate; retain any explicitly required independent/Astra acceptance.
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.
- yesterday Changed · -80 lines · +4 tokens per session bcdeeaee3ed3
- 1mo ago First seen · 167 lines · 26 tokens per session scan A 2e0217d3b440
docs-reviewer is a skill published in the GitHub repository RaNDoM6913/claude-code-superkit (3 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 1,674 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 2026-08-31.
Other skills, from other repositories
Self-Review
Spawn a reviewer subagent with 4-6 specific verification questions before declaring non-trivial code work done. Triggers when changes are >30 LOC, multi-file, hot-path, prompt-affecting, or genuinely uncertain. Bad asks produce bad answers — this skill forces structured verification questions, not "is this good?".
find-weakness
Hypatia's targeted weakness-finding skill. Given a specific claim, architecture, or decision, finds the single strongest objection and the hidden assumption most likely to be wrong. Sharper and faster than critique-plan.
ai-dev-os-check
Checks code changes against AI Dev OS guidelines and automatically fixes violations. Based on benchmark Test 011: check+fix achieves +9.9 improvement vs report-only (+0.8). Supports git diff (default), branch comparison, and staged changes. Use --dry-run for report-only mode (no fixes applied).
ai-dev-os-scan
Scans ALL source files in the project against AI Dev OS guidelines. Unlike /ai-dev-os-check (which only checks git diff), this performs a full project-wide compliance scan. Use for initial audits, periodic full reviews, or after introducing new guidelines.
ai-dev-os-review
Performs a comprehensive self-review before creating a PR. Combines guideline compliance checking (L3) with design-level review (L2) and philosophical alignment (L1). Unlike /ai-dev-os-check which only checks rules, this also evaluates architecture decisions and code design quality.
ai-dev-os-extract
Extracts rules from the gap between AI-generated code and ideal code. Used after code review to add new rules to AI Dev OS guidelines. A Rule Harvesting approach to rule discovery.