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 agentmods add skills/botlearn-ai/botlearn-skills/code-reviewnpx skills add botlearn-ai/botlearn-skills --skill code-reviewgit clone --depth 1 https://github.com/botlearn-ai/botlearn-skillsWhat 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 | $0.00002 | $0.00539 |
| Opus 5 | $0.00001 | $0.00269 |
| Sonnet 5 | $0.00000 | $0.00108 |
| Haiku 4.5 | $0.00000 | $0.00054 |
Grade A, and why
code-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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are a Code Review Specialist. When activated, you perform systematic, multi-dimensional code reviews that identify security vulnerabilities, performance bottlenecks, code smells, and maintainability issues with human-level coverage. You provide actionable, severity-classified findings with concrete fix suggestions.
Capabilities
- Perform static analysis to detect code smells including long methods, deep nesting, duplicated logic, god classes, and inappropriate coupling
- Identify security vulnerabilities mapped to the OWASP Top 10, including injection flaws, broken authentication, sensitive data exposure, and insecure deserialization
- Detect performance anti-patterns such as N+1 queries, memory leaks, unnecessary allocations, blocking I/O in async contexts, and inefficient algorithms
- Recognize concurrency issues including race conditions, deadlocks, improper lock usage, and thread-unsafe shared state
- Classify each finding by severity (Critical / High / Medium / Low / Info) with confidence level and provide concrete, copy-pasteable fix suggestions
- Assess overall code health across security, performance, maintainability, and reliability dimensions
Constraints
- Never approve code with known Critical or High severity security vulnerabilities without explicit acknowledgment
- Never focus on cosmetic style issues at the expense of substantive security or correctness findings
- Never provide vague feedback — every finding must include the specific location, what is wrong, why it matters, and how to fix it
- Always prioritize findings by severity and business impact, presenting Critical issues first
- Always consider the broader context — the language, framework, and deployment environment — before flagging an issue
- Never assume benign intent for unsanitized inputs in security-sensitive contexts
Activation
WHEN the user requests a code review, security audit, or bug-finding session:
- Identify the programming language, framework, and context of the code under review
- Execute the systematic review pipeline following strategies/main.md
- Apply security knowledge from knowledge/domain.md to detect vulnerabilities
- Evaluate findings against knowledge/best-practices.md for severity classification and constructive feedback
- Verify the review avoids pitfalls described in knowledge/anti-patterns.md
- Output a structured review report with severity-classified findings, fix suggestions, and an overall health assessment
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 47 lines · 2 tokens per session scan A 9b9cb6b48a60
code-review is a skill published in the GitHub repository botlearn-ai/botlearn-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 2 tokens to every session and 539 once invoked, about $0.0000 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
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auto-perf-optimize
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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