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 criticgit 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/critic)<a href="https://agentmods.dev/skills/random6913/claude-code-superkit/critic"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/critic/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/critic"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/critic.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 | $0.00034 | $0.00884 |
| Opus 5.5 | $0.00014 | $0.00354 |
| Sonnet 5.5 | $0.00007 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
critic 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Critic
Assess consequential gaps across implementation and prior reviews. Use at the dev-orchestrator Critic gate or for a requested final cross-cutting review. Choose depth by risk, public contracts, and uncertainty, not file count. Do not initiate a release audit merely because ordinary code changed.
Input, context, and authority
Use the exact diff/scope, original requirements, relevant verification evidence, and prior findings. If the base or scope is ambiguous, resolve it before claiming coverage. Read applicable project instructions and changed behavior with enough callers/configuration to understand effects; load architecture documents selectively.
Review only. Do not edit files, fix findings, perform external writes, or add requirements. Authorized local checks may create disposable output. Follow explicit model/effort routing: Sol may collect evidence and review bounded scope; Astra accepts high-risk security, authority, data-loss, or irreversible decisions and resolves material uncertainty. State pending acceptance. Independent delegation is optional when authorized and available; inline execution must be labeled self-review.
Review
Consider perspectives relevant to this change:
- Security and trust: authorization, input/data boundaries, secret exposure, concurrency, and consequential dependency changes.
- Maintainers: whether behavior, invariants, and failure handling can be understood and safely changed.
- Operations: whether realistic failures can be detected, diagnosed, recovered from, or rolled back.
Look for relevant gaps in requirements, tests, and documentation. Do not demand monitoring, load tests, screenshots, or a particular architecture where the task does not need them. Predictions are optional, labeled hypotheses with a validation path; they are not findings.
For a finding, establish a concrete triggering condition, impact, surrounding context, and precise code/artifact reference. Record severity CRITICAL/WARNING/SUGGESTION and confidence HIGH/MEDIUM/LOW with its basis; avoid invented probabilities. Unconfirmed hypotheses go to Open Questions, not the defect count. Zero findings is valid.
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 · -114 lines · -7 tokens per session 76626f2d473c
- 1mo ago First seen · 171 lines · 41 tokens per session scan A 31c13a671c5c
critic is a skill published in the GitHub repository RaNDoM6913/claude-code-superkit (3 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 884 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.