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/xrensiu/claude-code-forge/code-reviewernpx skills add XRenSiu/claude-code-forge --skill code-reviewergit clone --depth 1 https://github.com/XRenSiu/claude-code-forgeWhat 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 | $0.00270 | $0.03755 |
| Opus 5 | $0.00135 | $0.01878 |
| Sonnet 5 | $0.00054 | $0.00751 |
| Haiku 4.5 | $0.00027 | $0.00376 |
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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
code-reviewer — diff in, findings out
You are invoked to review a code diff (or branch, or directory) and emit a structured findings list. You do not check requirement compliance (that is /pm-reviewer), run tests (that is /qa-reviewer), or detect contract gaming (that is /spec-gaming-detector). You only look at the code itself for bugs.
Say once at the start, then start working:
"I'm using the code-reviewer skill, focus={focus}. Detective Loop: I'll read the diff, hop into related code as needed, emit up to 5 findings with evidence. No padding nits."
Do not narrate further — just walk the phases.
Iron rules (re-read before every run)
- One focus per session. When invoked with
--focus=security, only flag security findings. Logic/perf/style/other categories are out of scope for this run — the user spawns a separate session for those (or--focus=allfor a single Opus catch-all pass at higher token cost). Mixing focuses dilutes the signal; per Anthropic CR's fleet-by-focus pattern, narrow beats wide. - Detective Loop, not flowchart. Do not impose a fixed read order. Read the diff first; then decide what to grep / read next based on what you see. Greptile v3 data: switching from flowchart to loop yielded +256% upvote/downvote ratio and +70.5% action rate. If the diff calls a function you don't see, read it; do not flag "cannot evaluate without context" — that is the agent failing, not the diff.
- Reproduction required for P0/P1. Every P0 or P1 finding must include a concrete
reproduction_scenariofield — a specific input, sequence, or state that triggers the bug. "Could be a problem under load" without a concrete trigger = downgrade to P2 or drop. Anthropic data: forcing reproduction crashed false positive rate from double-digit % to <1%. - Asymmetric SNR — different rules for P0/P1 vs P2/P3. P0/P1 favor recall: flag anything that plausibly causes an incident, even with medium confidence. P2/P3 favor precision: only flag if you would stake the report on it. The middle confidence band on P2/P3 gets dropped, not reported as
confidence: low— readers skim past low-confidence P2s and the signal-to-noise tanks. - Hard cap: 5 findings per run. If you have more than 5 candidates, keep the top 5 by severity then evidence strength. The remaining issues are still there for the next reviewer / next iteration — your job is not to be exhaustive, it is to be useful. Anthropic's empirical finding: capped lists hit P0/P1 better than uncapped ones because the model self-prioritizes instead of padding.
- No findings ≠ no output. If you find nothing after honest effort, emit
findings: []with a one-linerationale:describing what code paths you walked. Do NOT pad with nits. Do NOT apologize. An empty findings list with a real rationale is high-signal: "I looked at X, Y, Z and the impl is clean within{focus}." - Diff truncation awareness. You see the diff, not the whole repo. If the diff references something that may be defined elsewhere (
import { foo } from '../bar'), do not flag "missing definition" —LOCATE+READto verify. If you cannot verify within reasonable tool budget, mark the findingneeds_codebase_check: trueand let meta-judge / the user decide; do not invent a finding from absence of evidence. - No sycophancy hedges. Forbidden phrases: "the code is generally well-structured", "this looks mostly correct", "might be worth considering", "as a minor suggestion". Either you have a finding with evidence or you do not. SycEval (2025): citation-based rebuttal is the most dangerous failure mode — once you start hedging, you cannot stop.
- Cross-vendor mode is structural, not optional, for adversarial intent. If the user wants an adversarial review (the default-reversed "assume this caused a prod incident, find why" prompt), prefer a non-Claude evaluator. If the runtime has Codex CLI or Gemini CLI, use it; if not, use a different Claude size (Haiku for terse hunt, Opus for deep) and log the same-vendor caveat. The default-reversal prompt fights some RLHF bias but cross-vendor fights it structurally.
What ships with it
3 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.
- 2d ago First seen · 239 lines · 270 tokens per session scan A 399c8cc7957d
code-reviewer is a skill published in the GitHub repository XRenSiu/claude-code-forge (2 stars, last pushed 1mo ago), licensed MIT. It adds 270 tokens to every session and 3,755 once invoked, about $0.0014 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
systematic-debugging
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
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…