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/21pounder/terminalagent/code-reviewnpx skills add 21pounder/terminalAgent --skill code-reviewgit clone --depth 1 https://github.com/21pounder/terminalAgentWhat 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 | $0.00036 | $0.00650 |
| Opus 5 | $0.00018 | $0.00325 |
| Sonnet 5 | $0.00007 | $0.00130 |
| Haiku 4.5 | $0.00004 | $0.00065 |
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 2d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Perform thorough code review analyzing quality, potential bugs, security issues, and suggesting improvements.
Parameters
{
"type": "object",
"properties": {
"target": {
"type": "string",
"description": "File path, directory, or glob pattern to review"
},
"focus": {
"type": "string",
"enum": ["general", "security", "performance", "maintainability"],
"description": "Primary focus area",
"default": "general"
}
},
"required": ["target"]
}
When to Use
- User asks to "review" or "check" code
- User wants to find bugs or issues
- User asks about code quality
- User wants security analysis
- User asks for improvement suggestions
Methodology
Phase 1: Context Gathering
- Read the target files
- Understand the codebase structure
- Identify the programming language and framework
- Check for related tests and documentation
Phase 2: Analysis
- Logic Review: Check for bugs and edge cases
- Security Scan: Look for vulnerabilities (injection, auth issues, etc.)
- Performance Check: Identify bottlenecks and inefficiencies
- Style Review: Check consistency and best practices
Phase 3: Prioritization
- Categorize issues by severity (Critical, High, Medium, Low)
- Focus on actionable feedback
- Provide concrete examples
Phase 4: Output
Provide structured review with:
- Summary of findings
- Issues list with severity and line numbers
- Specific improvement suggestions
- Code examples where helpful
Guidelines
- Be constructive, not just critical
- Provide specific line references
- Explain WHY something is an issue
- Suggest concrete fixes, not just problems
- Acknowledge good patterns when found
- Consider the project's existing style
Examples
Example 1: File Review
User Input: "Review src/auth.ts for security issues"
Expected Behavior:
- Read the file and understand authentication flow
- Check for common security issues (SQL injection, XSS, weak crypto)
- Verify input validation and sanitization
- Check for proper error handling
- Provide prioritized list of findings with fixes
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.
- 2d ago First seen · 99 lines · 36 tokens per session scan A 1d46523c7020
code-review is a skill published in the GitHub repository 21pounder/terminalAgent (120 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 36 tokens to every session and 650 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 2026-08-30.
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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…