Q Branch
Installs four MIT-licensed gadgets that make coding agents cheaper and more reliable, using hooks instead of longer prompts.
Free
Works with the AI Agents you already use
Q Branch
Example session with this skill installed
Set up groundhog in this repo and show me what my agents keep getting wrong.
- Read your context and instructions
- Compiled the q branch
Summary
Installed groundhog, mined this repo's session history, and turned the most-repeated mistake into an enforced lesson. Output below is from a real run on a repo with ~1,200 sessions of history.
1. Install
claude plugin marketplace add grexican/qbranch
claude plugin install groundhog@qbranch
2. Mine the history
$ groundhog scan
1187 sessions, 81291 shell calls: 21 lesson candidates, 50 sequences, 32 friction shapes, 100 repeats.
Next: groundhog report
3. Read what it found
$ groundhog report
Lessons: the same error, then the same workaround (accept with: groundhog accept L<n>)
L1 x14 in 14 sessions pnpm -s db:seed --env => pnpm db:seed --env dev
Sequences run by hand again and again (draft a runbook with: groundhog draft S<n>)
S1 71 sessions dotnet build P -v > dotnet test P --no-build
Friction: repeated commands that fail a lot
F1 97% of 39 fail npm run verify
That first line is the same lesson re-learned in
14 separate sessions: the agent runs the command one way, it fails, and it then works the other way. The sequence in 71 sessions is a verify script nobody wrote.
4. Accept the lesson
$ groundhog accept L1
L1 added (deny-once): when /pnpm\s+-s\s+db:seed\s+--env/
-> this form fails here; what worked: pnpm db:seed --env dev
It lands in groundhog.lessons.json, which you commit, so the whole team's agents get it.
What happens next session
The lesson is injected at session start. If the agent reaches for the old command anyway, a PreToolUse hook blocks it
once, with the fix, then lets it through on a retry, so a false positive can never deadlock the run.
Next steps
groundhog draft S1turns that 71-session sequence into a runbook skill.- Review before accepting: mined lessons go stale. On one machine the top mined lesson was "use
python3", but by then bothpythonandpython3were broken and onlypy -3worked. groundhog statslater tells you whether an accepted lesson actually stopped recurring.
Connects securely to your tools. The creator never sees your data.
About this skill
Q Branch is four open-source gadgets for coding agents. This skill installs the ones you choose and explains how to drive them. Everything is MIT-licensed and public: https://github.com/grexican/qbranch
They share one idea: guarantee steps with hooks instead of longer prompts. A rule in CLAUDE.md or AGENTS.md costs tokens in every session and still gets skipped. A hook costs nothing until the moment it applies, and then it fires every time.
The gadgets
- headjack — Skills arrive when a task needs them. The library is indexed and searched per prompt instead of preloaded. This matters because large libraries lose their skill descriptions from the listing (measured on Claude Code 2.1.285: roughly 8 skills keep a description on Haiku, 105 on Sonnet), so a plain-language request can no longer be matched to a skill the agent technically has.
- middleout — One plan over your MCP servers: chained calls, one-to-many fan-out, automatic pagination, and only the fields you select come back.
- moneyball — Measures every subagent run from the transcripts (model, cost, and whether it actually worked), then graduates a kind of task to a cheaper model once the evidence supports it. It judges cost per completed task, so a cheap model that needs retries is not counted as cheap.
- groundhog — Mines transcripts for the lessons agents keep re-learning, keeps accepted ones in a committed ledger, and enforces them with a hook that blocks the known-bad command once, with the fix.
Honest limits
- groundhog needs session history to mine. A fresh machine has nothing to learn from yet.
- moneyball needs at least 10 runs of a task class before recommending anything, and its automatic model routing applies to Claude models; for other vendors it reports advice.
- headjack helps most with a large library. With a handful of skills, your agent's built-in preloading is fine, and the honest answer is to keep using it.
- middleout earns its place on per-item fan-out over chatty APIs. For a one-off call, a raw tool call is simpler. A plan runs under a single approval, so a fan-out of 100 items makes 100 calls; it is exactly as safe as the tools the plan names.
Requirements
Node 22.13+, or the native binary (Windows, macOS, Linux). Works with Claude Code, Cursor, Codex, Gemini CLI, Copilot and Windsurf. Where an agent cannot do something, that feature turns off rather than half-working, and each gadget's README lists exactly what works where.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
Free forever
No account required to browse
Trust & safety
Security scanned
Verified clean 1 day ago
- Free to download with an account
Requires