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/everyinc/compound-engineering-plugin/ce-retunenpx skills add EveryInc/compound-engineering-plugin --skill ce-retunegit clone --depth 1 https://github.com/EveryInc/compound-engineering-pluginWhat 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 | $0.00068 | $0.01007 |
| Opus 5 | $0.00034 | $0.00504 |
| Sonnet 5 | $0.00014 | $0.00201 |
| Haiku 4.5 | $0.00007 | $0.00101 |
Grade A, and why
ce-retune 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 3d 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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 41 lines · 68 tokens per session scan A 5d6637f5556b
ce-retune is a skill published in the GitHub repository EveryInc/compound-engineering-plugin (24,760 stars, last pushed today), licensed MIT. It adds 68 tokens to every session and 1,007 once invoked, about $0.0003 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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