
ce-retune
Retune a skill corpus for a new model, measurement-first: mine the run archive for a baseline, establish a noise floor, audit the corpus adversarially, then cut in measured passes until a pre-registered bar clears. Requires a benchmark harness that can A/B two builds of the corpus; refuses without one.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/everyinc/compound-engineering-plugin --skill ce-retune- 01
Install
Run the command in your project. The CLI asks which agents to install for.
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Describe the task
Ask your agent for something this skill covers. It loads ce-retune automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Retune a Corpus for a New Model
A corpus that degrades on a new model is a measurement problem before it is a writing problem: rewriting what looks wrong produces a plausible fix list and no way to know whether any item mattered.
Outcome: a corpus whose measured behavior on the target model clears a bar registered before any change, with the regression classes removed and each removal attributable.
Done: the bar is cleared, or the run reports the specific claim it could not support. A green test suite is not done: it proves nothing broke, not that behavior improved.
Non-goal: word reduction. Leanness and performance are separate programs that share a corpus; only one of them is the result here. Report completion, not word count.
Phase 0: the measurement gate — check this first
This skill cannot run without a way to observe behavior. Check for all three, and name whichever is missing:
- A run archive or a harness that produces one — per-run logs carrying the tool-call trace, a terminal marker, token counts, and the final message.
- A build selector — the harness can point a run at a specific source checkout of the corpus (a
--plugin-dir-style override, a configurable skills path, an env var), so two builds are comparable under one runner. - A repeatable task the corpus actually executes end to end.
If any is missing, stop and say so, naming what to build. Do not fall back to a static audit and present it as retuning: an audit can say what looks cuttable and never whether cutting helped. An audit-only pass is a legitimate thing to want; it is a different request.
State the target model and the harness you found before continuing.
The phases
They run in order, and each names the reference it cannot start without. Read references/workflow-shapes.md before dispatching any phase: the wrong orchestration shape is the common failure. Fan out by disjoint file ownership, never by item. Items cross files, and agents that share a file lose each other's edits.
Before assessing whether the registered bar is met or interpreting its results, read references/noise-floor.md.
- Mine the archive before spending a run —
references/baseline-mining.md. Historical runs are a free baseline, usually larger than any experiment affordable now. - Establish the noise floor —
references/noise-floor.md. Run the harness against two identical copies of the corpus, same commit on both sides; whatever difference appears is the floor every later claim must clear. Register the bar now, in writing, before any change exists. A bar chosen after seeing results is not a bar. - Audit the corpus adversarially —
references/corpus-audit.md. One agent per skill proposes cuts; a second per skill does the opposite and defends the existing prose. The two passes require independent contexts. If the host exposes no way to run them as separate agents, report that as a blocker and stop the audit — do not argue both sides in one context and present the result as an audit. - Cut in surgical passes, one problem per agent —
references/cut-passes.md, andreferences/halt-taxonomy.mdwhen the symptom is stalling, halting, or a run that ends while naming work it did not do. Two rules bound every pass, whatever class it is cutting. Never edit a test to make a suite green: a removed string a test pins is a finding to report, not a test to weaken. And not every stop is the enemy. Some workflows exist to stop and ask; that is the product. Sort every stop by who is actually on the other side before touching it.references/halt-taxonomy.mdcarries the screens that decide, so read them before cutting any stop. - Measure, then let the failure choose the next fix —
references/cut-passes.mdagain for what each failure site means and for auditing the phases the instrument never enters. Loop 4 and 5 until the registered bar clears. Then stop; a bar cleared is done. Also report what stayed unmeasured: a cleared bar never implies coverage it does not have. - Ship —
references/cut-passes.mdcarries what the commits and the write-up must preserve.
FAQ
What does the ce-retune skill do?
Retune a skill corpus for a new model, measurement-first: mine the run archive for a baseline, establish a noise floor, audit the corpus adversarially, then cut in measured passes until a pre-registered bar clears. Requires a benchmark harness that can A/B two builds of the corpus; refuses without one.
How do I install ce-retune?
Run npx skills add https://github.com/everyinc/compound-engineering-plugin --skill ce-retune in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ce-retune/ (personal) or .claude/skills/ce-retune/ (project).
Which AI agents can use ce-retune?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ce-retune safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ce-retune?
It is published by everyinc in everyinc/compound-engineering-plugin. It has 626 recorded installs.
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