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| mcp.json | ||
| README.md | ||
| SKILL.md | ||
gitnexus-lfg — plan → gate → work → review
Thin pipeline orchestrator over three existing skills: gitnexus-plan
produces the plan (asking up front how deep to go), the user chooses at a
blocking gate to proceed or stop (an explicit deepen request is still
honored), gitnexus-work executes it as verified atomic commits, and
gitnexus-review reviews the result (the open PR if one exists, else the
branch diff against the default branch). One bounded fix cycle for review
findings, then a final report. It never pushes or opens a PR on its own.
Invocation
| CLI | How to invoke |
|---|---|
| Claude Code | /gitnexus-lfg <task description> or /gitnexus-lfg docs/plans/<plan>.md |
| Codex CLI | Ask: "run the gitnexus pipeline on " (Codex reads AGENTS.md), or install the skill user-level (below) |
Codex (user-level install)
cp -r .claude/skills/gitnexus-lfg ~/.agents/skills/gitnexus-lfg
Optionally, for an explicit slash command, create
~/.codex/prompts/gitnexus-lfg.md:
---
description: GitNexus pipeline — plan (depth asked up front), user gate, work, PR review
argument-hint: <task description or plan path>
---
Use the gitnexus-lfg skill for: $ARGUMENTS
Read `~/.agents/skills/gitnexus-lfg/SKILL.md` (prefer the repo copy at
`.claude/skills/gitnexus-lfg/SKILL.md` when present) and follow its lanes in
order, invoking the real gitnexus-plan / gitnexus-work / gitnexus-review
skills for each lane. Stop at the plan gate for the user's choice.
The three lanes
| Lane | Skill | Gate |
|---|---|---|
| Plan | gitnexus-plan (.claude/skills/gitnexus-plan/) |
Depth asked up front; blocking gate: proceed / stop |
| Work | gitnexus-work (.claude/skills/gitnexus-work/) |
Structural drift routes back to the plan gate |
| Review | gitnexus-review (.claude/skills/gitnexus-review/) |
One fix cycle max, then report |
Threshold governance (maintainers)
The Lane 1 planning boundary (~35 turns) is a promoted benchmark policy from
the GitNexus repository's eval/workflow_bench/ paired candidate loop.
Re-evaluate it offline whenever the named model or tool harness changes, and
at least every 90 days; update the SKILL.md threshold only after the
deterministic promotion gate shows no quality regression. Reading agents
never self-edit it from a live task.