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-optimizenpx skills add EveryInc/compound-engineering-plugin --skill ce-optimizegit 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 | $0.00019 | $0.01469 |
| Opus 5 | $0.00010 | $0.00734 |
| Sonnet 5 | $0.00004 | $0.00294 |
| Haiku 4.5 | $0.00002 | $0.00147 |
Grade A, and why
ce-optimize 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iterative Optimization Loop
references/usage-guide.md covers hard metrics versus a judge, first-run defaults, and the expensive-benchmark shape (multiple required targets plus a measurement ladder).
Done when: a stopping criterion fired, every declared required target is met or another stop fired first, the final state is written and verified on disk, and the user has been given the post-completion options. If the run instead stopped at a gate it could not clear, say what blocked it.
Interaction Method
Use the host's blocking question tool already in the current tool list (match by capability, not by a host-specific name). Presence in the current tool list is proof the tool exists; never call a user-facing question tool to discover whether it exists. If a matching tool is listed but unloaded, use the host's tool-discovery primitive to load that capability — do not search for another host's tool name. Fall back to numbered options on the host's chat surface only when no such tool is in the list or a real question call errors. Never skip the question silently.
Artifact Root
Resolve <root> the first time you compose a path under it. Reading learnings under <root>/solutions/ counts as composing one. Give any subagent the resolved path, not the config.
Resolve the CE artifact root <root> before composing any artifact path.
- Read
docs_rootfrom<repo-root>/.compound-engineering/config.yamlonly (<repo-root>=git rev-parse --show-toplevel). Do not read it fromconfig.local.yaml. Unset -><root>isdocs, exactly as before. - Validate a set value: a repo-relative directory whose real, symlink-resolved path stays inside the repo and is neither the repo root nor under
.git/. Otherwise stop with an error namingdocs_rootand the value -- never fall back todocs. - Use
<root>as the sole artifact location: create it if absent, compose each path as<root>/<subdir>with this skill's own subdirectory, and never also readdocs.
What ships with it
19 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.
- references/agents/learnings-researcher.md 16 KB
- references/agents/repo-research-analyst.md 15 KB
- references/example-expensive-benchmark-spec.yaml 2.1 KB
- references/example-hard-spec.yaml 1.3 KB
- references/example-judge-spec.yaml 1.8 KB
- references/experiment-log-schema.yaml 11 KB
- references/experiment-prompt-template.md 4.0 KB
- references/judge-prompt-template.md 4.9 KB
- references/loop.md 19 KB
- references/measurement.md 8.8 KB
- references/optimize-spec-schema.yaml 20 KB
- references/persistence.md 4.5 KB
- references/spec.md 7.0 KB
- references/usage-guide.md 5.8 KB
- references/wrap-up.md 3.5 KB
- scripts/decide.mjs 17 KB runs code
- scripts/experiment-worktree.sh 8.8 KB runs code
- scripts/measure.sh 3.9 KB runs code
- scripts/parallel-probe.sh 5.0 KB runs code
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 · 54 lines · 19 tokens per session scan A e4733c2cec3b
ce-optimize is a skill published in the GitHub repository EveryInc/compound-engineering-plugin (24,760 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,469 once invoked, about $0.0001 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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