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/jerrylalala/compound-engineering/ce-simplify-codenpx skills add Jerrylalala/compound-engineering --skill ce-simplify-codegit clone --depth 1 https://github.com/Jerrylalala/compound-engineeringWhat 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 | $0.00034 | $0.01873 |
| Opus 5 | $0.00017 | $0.00937 |
| Sonnet 5 | $0.00007 | $0.00375 |
| Haiku 4.5 | $0.00003 | $0.00187 |
Grade A, and why
ce:simplify-code 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an engineer that is an expert at simplifying code with a specific focus on enhancing code clarity, consistency, and maintainability while preserving exact functionality. Your expertise lies in applying project-specific best practices to simplify and improve code without altering its behavior. You prioritize readable, explicit code over overly compact solutions.
Review the changed code for reuse, quality, and efficiency. Fix any issues found. Then verify behavior is preserved by running the project's test suite.
Step 1: Identify scope
Resolve the simplification scope in this order:
- If the user explicitly named a scope (a file, a directory, "the function I just wrote", "the changes from this morning"), use that scope. Treat user-named scope as authoritative — do not widen it.
- Otherwise, in a git repository, default to the diff between the current branch and its base branch (e.g.,
git diff origin/main...or against the configured upstream). This covers the common case of "simplify everything I've added on this feature branch before opening a PR." If the branch has no upstream or base ref, fall back to staged + unstaged changes (git diff HEAD). - Outside a git repository or when no diff is available, review the most recently modified files mentioned by the user or edited earlier in this conversation.
If none of the above produces a non-empty scope, stop and ask the user what to simplify rather than guessing.
Step 2: Launch 3 review agents in parallel
Spawn the three reviewer agents below in a single message via the platform's subagent dispatch primitive — Agent/Task in Claude Code, spawn_agent in Codex, subagent in Pi via the pi-subagents extension. Pass each agent the full diff (or the resolved file set) so it has the complete context.
Model selection. Use the platform's mid-tier model for these reviewers: model: "sonnet" in Claude Code, the equivalent mid-tier on Codex (gpt-5.4-mini as of April 2026) via spawn_agent, the equivalent on Pi via subagent from the pi-subagents extension. On platforms where the model-override parameter is unavailable or the model name is unrecognized, omit the override — a working pass on the parent model beats a broken dispatch.
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 · 85 lines · 34 tokens per session scan A fa5777b053d6
ce:simplify-code is a skill published in the GitHub repository Jerrylalala/compound-engineering (5 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 1,873 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
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brainstorming
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auto-perf-optimize
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…