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 code-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/code-reviewer)<a href="https://agentmods.dev/skills/random6913/claude-code-superkit/code-reviewer"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/code-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/code-reviewer"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/code-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 | $0.00035 | $0.01349 |
| Opus 5.5 | $0.00014 | $0.00540 |
| Sonnet 5.5 | $0.00007 | $0.00270 |
| Haiku 4.5 | $0.00003 | $0.00135 |
Grade A, and why
code-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 today.
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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer
Assess the requested code or diff and its effects on callers. Coordinate with stack-specific reviewers when assigned; do not silently omit matching files merely because another skill exists. A focused review with no findings is valid.
Scope, context, and authority
Read applicable AGENTS.md first, then relevant project conventions and architecture. Establish the selected base/head or working-tree diff and assigned files; do not substitute recent commits for the requested scope. If scope is ambiguous, resolve it before claiming coverage. Inspect actual changed code plus relevant callers, types, configuration, and tests. Confirm language/framework versions from project evidence when a conclusion depends on them.
Review only. Propose corrections; do not apply them, post comments, or perform external writes unless a distinct user request authorizes that action. Existing implementation authority belongs to the implementation step, not an implicit reviewer fix. Use authorized local checks when they resolve a concrete uncertainty; avoid commands that mutate external state or expose secrets/private records.
Honor explicit model/effort routing and applicable acceptance ownership. Report requested/configured routing separately from runtime-observed values and their evidence; use UNVERIFIED when runtime identity is unavailable. This reporting limitation does not introduce an identity gate for ordinary review. Disclose independent review versus inline self-review; mandatory independent or Astra acceptance cannot be replaced by a label.
Review changed behavior
Discover plausible issues across assigned scope, then verify and triage before reporting. Do not publish checklist suspicions as findings. Follow the affected behavior far enough to establish a trigger and consequence:
- Public and internal contracts: request/response shape, defaults, validation, persistence, errors, ordering, compatibility, and call sites. Check whether a changed signature is wired into its consumers, not just whether it compiles locally.
- Security and data integrity: trace untrusted input into SQL, command execution, rendering, file access, and authorization decisions. Check actual parameter binding/escaping and access controls; interpolation or a missing middleware name alone is not proof of an exploit. Distinguish intentionally public endpoints from accidentally unprotected operations.
- Error and resource behavior: propagation, cancellation/deadlines, retries, transactions, cleanup, and partial success. Verify whether failures are handled by an owner elsewhere before alleging a swallowed error. Wrapping an error is useful only if it preserves necessary cause/type information and project contracts.
- Boundary cases: missing/null values, empty collections, duplicate or concurrent requests, and limits relevant to this change. Prefer proven producer invariants over blanket defensive checks. For performance claims, identify the input size or execution path that makes the regression material; do not call an unmeasured optimization a defect.
- Architecture and dependency ownership: compare dependencies and wiring to documented project boundaries and established contracts. A transport/service/repository split, constructor injection, interface-based dependencies, or setter injection is not universally required. Report a concrete coupling/lifecycle defect or a binding convention violation, with its source; label a different design preference optional.
- Tests and maintainability: check relevant behavior and failure modes against existing verification. Identify a meaningful unverified contract rather than demanding one test per edited line. Assess names, dead code, and comments only where they mislead, violate an applicable convention, or impose a concrete maintenance cost. Do not duplicate formatter/linter nits already enforced.
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.
- today Changed · -47 lines · +12 tokens per session d0fdaf9ca8be
- 1mo ago First seen · 115 lines · 23 tokens per session scan A 706653335d09
code-reviewer is a skill published in the GitHub repository RaNDoM6913/claude-code-superkit (3 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 1,349 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.