Borrowing it
Nothing to install: this file belongs to Bita71/Agent-Workflow-Kit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Bita71/Agent-Workflow-Kit/master/.agents/skills/skill-review-correctness/SKILL.mdgit clone --depth 1 https://github.com/Bita71/Agent-Workflow-KitWrote 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/bita71/agent-workflow-kit/skill-review-correctness)<a href="https://agentmods.dev/skills/bita71/agent-workflow-kit/skill-review-correctness"><img src="https://agentmods.dev/badge/skills/bita71/agent-workflow-kit/skill-review-correctness/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/bita71/agent-workflow-kit/skill-review-correctness"><img src="https://agentmods.dev/badge/skills/bita71/agent-workflow-kit/skill-review-correctness.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 | $0.00022 | $0.00111 |
| Opus 5.5 | $0.00009 | $0.00044 |
| Sonnet 5.5 | $0.00004 | $0.00022 |
| Haiku 4.5 | $0.00002 | $0.00011 |
Grade A, and why
skill-review-correctness 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 2mo 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
Source of truth: docs/ai/skills/review-correctness.md and
docs/ai/agents/review-correctness.md.
Read them and follow them exactly. Review only the changed files and related code
paths. Do not edit code — return the report only. If an artifact is requested,
write it to docs/ai/reviews/YYYY-MM-DD-short-task.review.md.
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.
- 2mo ago First seen · 12 lines · 22 tokens per session scan A a49b5adff960
skill-review-correctness is a skill published in the GitHub repository Bita71/Agent-Workflow-Kit (1 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 111 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
readiness
Use when the user wants to analyze, audit, or assess their codebase for AI agent readiness. Also use for "readiness report", "how ready is my project", "analyze my codebase", "audit my repo", "check my setup", or "what should I improve".
agentic-code-review
Use when reviewing a diff, pull request, branch, or AI-generated code for correctness, security, regression, test, performance, and maintainability risks before merge.
repo-health-check
Use when auditing a repo before changes to find the smallest safe PR, quality risks, stale docs, missing tests, ignored-file gaps, or agent setup issues. Best first skill for unfamiliar codebases.
pr-reviewing
Review a PR across multiple dimensions — description, code changes, code scan, unit tests — using parallel subagents. Supports GitHub, GitLab, and enterprise platforms.
code-review
Use after completing implementation steps and before merging — reviews against plan and HOTL contracts.
requesting-code-review
Use at executor review checkpoints to dispatch the code-reviewer agent with structured context — git range, workflow contracts, and verification evidence.