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-worknpx skills add Jerrylalala/compound-engineering --skill ce-workgit clone --depth 1 https://github.com/Jerrylalala/compound-engineeringWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jerrylalala/compound-engineering/ce-work)<a href="https://agentmods.dev/skills/jerrylalala/compound-engineering/ce-work"><img src="https://agentmods.dev/badge/skills/jerrylalala/compound-engineering/ce-work.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00031 | $0.17444 |
| Opus 5 | $0.00015 | $0.08722 |
| Sonnet 5 | $0.00006 | $0.03489 |
| Haiku 4.5 | $0.00003 | $0.01744 |
Grade A, and why
ce:work scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
4. 执行对应的 curl 请求或 DB CLI 查询 How it starts
The opening of the file, as written. The whole thing — 1,200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Work Execution Command
Execute work efficiently while maintaining quality and finishing features.
Introduction
This command takes a work document (plan, specification, or todo file) or a bare prompt describing the work, and executes it systematically. The focus is on shipping complete features by understanding requirements quickly, following existing patterns, and maintaining quality throughout.
Input Document
<input_document> #$ARGUMENTS </input_document>
Execution Workflow
Phase -1: 参数检测与模式初始化
检测所有可选标志并 strip from arguments before passing to Phase 0。
[R] 历史检索标志检测(最先执行,防止污染路径解析):
- 如果
$ARGUMENTS包含[R]或[r]:- 设置 R_MODE_ENABLED = true
- 从参数中移除
[R]
- 否则:R_MODE_ENABLED = false
向后兼容(参数别名):以下旧参数名在传入时自动识别并映射:
[team]→ 等同[T][team:full]→ 等同[T+][PW]→ 等同[V+]传入旧名不会报错,等同传入新名。
[T]现在固定表示 Agent Teams;四层自验证使用[V]。不要再把[T]当作旧自验证别名。
[V] 自验证标志检测(独立执行):
- 如果
$ARGUMENTS包含[V]或[v]:- 设置 V_MODE_ENABLED = true
- 从参数中移除
[V] - 宣告:「✅ [V] 自验证模式已启用——执行完成后将运行四层验证(Phase 3.5)」
- [V+] 检测([V+] 自动启用四层验证,无需单独传 [V]):
- 如果
$ARGUMENTS包含[V+]或[pw]:- 检查 Playwright MCP 工具可用性(
mcp__playwright__browser_navigate是否在工具列表中) - 若可用:设置 V_PLUS_MODE_ENABLED = true,从参数中移除
[V+],宣告:「✅ [V+] Playwright MCP 模式已启用——Layer 2 将使用 Playwright MCP(高精度浏览器验证)」 - 若不可用:V_PLUS_MODE_ENABLED = false,输出:「⚠️ Playwright MCP Server 未配置,[V+] 模式不可用,Layer 2 自动降级为 agent-browser。如需 Playwright MCP,请先安装并配置 Playwright MCP Server。」
- 检查 Playwright MCP 工具可用性(
- 否则:V_PLUS_MODE_ENABLED = false(Layer 2 使用 agent-browser,token 低 30-50 倍)
- 如果
- 否则:V_MODE_ENABLED = false
- 如果
$ARGUMENTS包含[V+]或[pw](向后兼容旧[PW]):- 自动升级:设置 V_MODE_ENABLED = true([V+] 隐含 [V],无需单独传 [V])
- 按上方 [V+] 检测逻辑继续执行(检查 Playwright MCP 可用性)
- V_PLUS_MODE_ENABLED = false(默认)
- 如果
[C] Codex 标志检测:
- 如果
$ARGUMENTS包含[C]或[c]:- 设置 CODEX_ENABLED = true
- 从参数中移除
[C] - 宣告:「✅ [C] 标志已检测——标记外部 AI(Codex)已参与整体工作流。注:[C] 不透传给内嵌 ce:review(透传无收益,详见 Phase 3)」
- 否则:CODEX_ENABLED = false
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.
- 4d ago First seen · 1,200 lines · 31 tokens per session scan A 26f3856d63ef
ce:work is a skill published in the GitHub repository Jerrylalala/compound-engineering (5 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 17,444 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
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…