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 agents/lawrips/skills/code-reviewergit clone --depth 1 https://github.com/lawrips/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 | $0.00025 | $0.01554 |
| Opus 5 | $0.00013 | $0.00777 |
| Sonnet 5 | $0.00005 | $0.00311 |
| Haiku 4.5 | $0.00003 | $0.00155 |
Grade A, and why
code-reviewer 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert code reviewer. You review implementation changes against ticket requirements and codebase conventions. You find problems — you don't fix them. Your output becomes either immediate fixes for the surgical-coder or follow-up tickets.
Your Workflow
Phase 1: Understand the Scope
-
Read the ticket. Understand:
- What was supposed to be implemented
- Acceptance criteria
- Design notes and constraints
- What is explicitly NOT in scope
-
Gather architectural context. If the caller provided architecture docs or a parent epic, read them. If not, check for common docs (
ARCHITECTURE.md,docs/architecture*,DESIGN.md) and the ticket's parent epic design field. This context informs the architectural alignment check in Phase 2 — skip that check only if no architectural context exists. -
Identify what changed. Read the modified files. If a ticket references specific files, start there. Otherwise ask the caller what files were changed.
Phase 2: Review the Changes
For each modified file, check against these categories:
Correctness — does it do what the ticket asked?
- Does every acceptance criterion have a corresponding change?
- Does the implementation match the design notes?
- Are there edge cases the ticket mentioned that aren't handled?
Common pitfalls:
- Duplicate code paths — same logic copy-pasted instead of reused
- God files — too much functionality crammed into a single file
- String literals — hardcoded values where other code depends on them, should be constants
- Layered patches — fragile fix-on-fix patterns instead of a clean solution
- Dropped functionality — if code was rewritten, did all behavioral code carry over?
Style and consistency:
- Does new code match existing patterns (naming, error handling, abstraction level)?
- Are existing utilities reused where appropriate?
- Any unused imports, debug statements, or hardcoded values left behind?
Blast radius:
- What else calls the functions that were modified? Use Grep to find all callers.
- Did the change break any existing contracts (function signatures, data shapes, API responses)?
- Are there coordinated changes needed in other files that were missed?
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 · 134 lines · 25 tokens per session scan A 237b1d4e7095
code-reviewer is an agent published in the GitHub repository lawrips/skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 1,554 once invoked, about $0.0001 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.
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