Borrowing it
Nothing to install: this file belongs to zahardev/aicontext. 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/zahardev/aicontext/main/.claude/skills/deep-review/SKILL.mdgit clone --depth 1 https://github.com/zahardev/aicontextWrote 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/zahardev/aicontext/deep-review)<a href="https://agentmods.dev/skills/zahardev/aicontext/deep-review"><img src="https://agentmods.dev/badge/skills/zahardev/aicontext/deep-review.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00148 |
| Opus 5 | $0.00019 | $0.00074 |
| Sonnet 5 | $0.00008 | $0.00030 |
| Haiku 4.5 | $0.00004 | $0.00015 |
Grade A, and why
deep-review 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 8d 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
Determine scope
Follow .aicontext/prompts/detect-review-scope.md to determine scope and count changed lines.
Run review
- Small scope (~200 changed lines or fewer, or IDE selection): follow
.aicontext/prompts/deep-review.mddirectly - Large scope (more than ~200 changed lines, or
all): launchrevieweragent with the prompt path.aicontext/prompts/deep-review.mdand the scope description
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.
- 8d ago First seen · 14 lines · 39 tokens per session scan A b2e36443e3b3
deep-review is a skill published in the GitHub repository zahardev/aicontext (2 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 148 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
audit-agents-skills
Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
design-patterns
Detect, suggest, and evaluate GoF design patterns in TypeScript/JavaScript codebases. Use when refactoring code, applying singleton/factory/observer/strategy patterns, reviewing pattern quality, or finding stack-native alternatives for React, Angular, NestJS, and Vue.
pr-triage
4-phase PR backlog management with audit, deep code review, validated comments, and optional worktree setup. Use when triaging pull requests, catching up on pending code reviews, or managing a backlog of open PRs. Args: 'all' to review all, PR numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit…
eval-skills
Audit all skills in the current project for frontmatter completeness, effort level appropriateness, allowed-tools scoping, and content quality. Produces a scored report with effort-level recommendations for each skill. Use when onboarding to a new project, reviewing skill quality before shipping, or adding effort…
review-pr
Perform a comprehensive code review of a pull request.
sonarqube
Analyze SonarCloud quality issues for a specific PR.