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/gcwing/bitfun/gstack-reviewnpx skills add GCWing/BitFun --skill gstack-reviewgit clone --depth 1 https://github.com/GCWing/BitFunWhat 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 | $0.00074 | $0.10454 |
| Opus 5 | $0.00037 | $0.05227 |
| Sonnet 5 | $0.00015 | $0.02091 |
| Haiku 4.5 | $0.00007 | $0.01045 |
Grade A, and why
pre-landing-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 — 859 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Landing PR Review
You are running the specialized pre-landing workflow. Analyze the current branch's diff against the base branch for structural issues that tests don't catch. Do not present this skill as the product /review command.
BitFun Team Mode Dispatch
When this skill is invoked by BitFun Team Mode, this skill supplies the pre-landing review lens. Use existing Task sub-agents for independent diff review tracks, then consolidate findings in the main Team session.
- Do not assume a Staff Engineer sub-agent exists. Choose only from the Task tool's available agents.
- Use at most one built-in
CodeReviewsub-agent for an independent pass, and put the exact correctness, performance, security, or architecture question in its prompt. Broader dynamic lens selection belongs to the unified/reviewpath. - Prefer a matching custom review sub-agent when the user configured one. Use
Exploreonly for broad read-only investigation when no review sub-agent fits. - Keep Task work read-only. Ask for tight findings with file paths, line references if possible, severity, confidence, and why tests might miss it.
- The main Team orchestrator owns final severity ordering, AUTO-FIX vs ASK classification, and any code changes.
Step 1: Check branch
- Run
git branch --show-currentto get the current branch. - If on the base branch, output: "Nothing to review — you're on the base branch or have no changes against it." and stop.
- Run
git fetch origin <base> --quiet && git diff origin/<base> --statto check if there's a diff. If no diff, output the same message and stop.
Step 1.5: Scope Drift Detection
Before reviewing code quality, check: did they build what was requested — nothing more, nothing less?
- Read
TODOS.md(if it exists). Read PR description (gh pr view --json body --jq .body 2>/dev/null || true). Read commit messages (git log origin/<base>..HEAD --oneline). If no PR exists: rely on commit messages and TODOS.md for stated intent — this is the common case since /review runs before /ship creates the PR. - Identify the stated intent — what was this branch supposed to accomplish?
- Run
git diff origin/<base>...HEAD --statand compare the files changed against the stated intent.
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 · 859 lines · 74 tokens per session scan A 5466a4879991
pre-landing-review is a skill published in the GitHub repository GCWing/BitFun (1,871 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 10,454 once invoked, about $0.0004 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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