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/gopherguides/gopher-ai/go-code-reviewnpx skills add gopherguides/gopher-ai --skill go-code-reviewgit clone --depth 1 https://github.com/gopherguides/gopher-aiWrote 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/gopherguides/gopher-ai/go-code-review)<a href="https://agentmods.dev/skills/gopherguides/gopher-ai/go-code-review"><img src="https://agentmods.dev/badge/skills/gopherguides/gopher-ai/go-code-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 | $0.00076 | $0.01306 |
| Opus 5 | $0.00038 | $0.00653 |
| Sonnet 5 | $0.00015 | $0.00261 |
| Haiku 4.5 | $0.00008 | $0.00131 |
Grade A, and why
go-code-review scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -H "Authorization: Bearer $GOPHER_GUIDES_API_KEY" \ How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Go Code Review
Automated PR code review for Go projects. Provides first-pass review with inline comments, quality scoring, and breaking change detection.
What It Does
- Analyzes PR diff for code quality issues
- Generates inline comments on specific lines
- Produces a quality score (0-100)
- Flags breaking API changes
- Summarizes findings with actionable next steps
Steps
API Integration (Optional)
If GOPHER_GUIDES_API_KEY is set, verify it:
curl -s -H "Authorization: Bearer $GOPHER_GUIDES_API_KEY" \
https://gopherguides.com/api/gopher-ai/me
If not set, local analysis tools (go vet, staticcheck, golangci-lint) still provide comprehensive analysis. Set the key for enhanced API-powered insights. Get your key at gopherguides.com.
1. Get the Diff
# For a PR
gh pr diff {number}
# For uncommitted changes
git diff
# For staged changes
git diff --cached
# For changes against main
git diff main...HEAD
2. Static Analysis on Changed Files
# Get list of changed Go files
CHANGED=$(git diff --name-only main...HEAD | grep '\.go$')
# Run vet on changed packages
echo "$CHANGED" | xargs -I{} dirname {} | sort -u | xargs go vet
# Run staticcheck on changed packages
echo "$CHANGED" | xargs -I{} dirname {} | sort -u | xargs staticcheck
# Run tests on affected packages
echo "$CHANGED" | xargs -I{} dirname {} | sort -u | xargs go test -race -count=1
3. Review Checklist
For each changed file, check:
Correctness
- Error handling on all fallible operations
- No nil pointer dereferences
- Proper resource cleanup (defer Close)
- Context propagation in concurrent code
- No data races (channels/mutexes used correctly)
Readability
- Clear naming following Go conventions
- Functions are focused (single responsibility)
- Comments explain "why", not "what"
- No magic numbers/strings
Maintainability
- Tests added/updated for changes
- No dead code introduced
- Dependencies justified
- Backward compatibility preserved (or breaking change documented)
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.
- 4d ago First seen · 185 lines · 76 tokens per session scan A 07a1564c439c
go-code-review is a skill published in the GitHub repository gopherguides/gopher-ai (21 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 1,306 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
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
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…