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/haojing8312/workclaw/skill-creatornpx skills add haojing8312/WorkClaw --skill skill-creatorgit clone --depth 1 https://github.com/haojing8312/WorkClawWrote 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/haojing8312/workclaw/skill-creator)<a href="https://agentmods.dev/skills/haojing8312/workclaw/skill-creator"><img src="https://agentmods.dev/badge/skills/haojing8312/workclaw/skill-creator.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 | $0.00038 | $0.00777 |
| Opus 5 | $0.00019 | $0.00388 |
| Sonnet 5 | $0.00008 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
创建技能 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 5d 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.
What it actually says
创建技能
目标
- 设计或迭代一个技能，确保触发准确、说明精炼、资源可复用、结果可验证。
工作流
- 先建立任务计划
- 在开始连续执行前，先调用
todo_write建立任务计划。 - 计划至少包含：
- 判断是否需要新建技能
- 明确触发场景与边界
- 设计资源与结构
- 起草或更新
SKILL.md - 轻量评测与修正
- 返回结果与建议
- 每当阶段推进时，都要更新
todo_write状态。
- 先判断是否真的需要新建技能；若已有技能可覆盖，优先复用或补强，而不是重复造一个近似技能。
- 明确目标使用场景、边界和触发信号，至少写出 3 类内容：触发示例、非触发示例、容易混淆的相邻场景。
- 定义最小必需资源（
scripts/、references/、assets/），只保留会被重复使用或显著提升可靠性的内容。 - 起草或更新
SKILL.md，默认 frontmatter 保留name和description；仅在确有必要时再增加allowed_tools、context、agent、mcp-servers等高级字段。 - 保持指令简洁、面向执行，重点写“何时用”和“怎么做”，不要堆背景知识。
- 做轻量评测：至少准备 3 条应触发提示词和 3 条不应触发提示词，检查是否有误触发或漏触发。
- 根据评测结果迭代
description、章节结构和示例，直到触发边界清晰。
触发设计
description要同时覆盖“做什么”和“什么时候用”，让模型能靠关键词和场景命中。- 触发示例要贴近真实用户说法，而不是作者视角的解释。
- 非触发示例要覆盖最容易误判到这个技能的相邻任务。
- 如果技能支持多种变体，主体只保留选择规则，细节放到
references/。
轻量评测
触发示例：用户说了什么时，这个技能应该被调用。非触发示例：哪些请求看起来相近，但其实不该调用这个技能。误触发：不该进这个技能却进了，通常说明description太泛。漏触发：该进这个技能却没进，通常说明关键词或场景覆盖不够。
质量检查清单
- 是否先判断了复用已有技能，而不是默认新建。
- 描述是否清楚说明技能做什么、何时使用，并便于被发现。
- 是否同时给出触发示例与非触发示例。
- 工作流步骤是否可执行且顺序合理。
- 内容是否避免无关上下文膨胀。
- 引用与资源是否只在必要时提供。
- 是否完成了轻量评测，并根据误触发/漏触发做过调整。
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.
- 5d ago First seen · 51 lines · 38 tokens per session scan A b7d423b1d241
创建技能 is a skill published in the GitHub repository haojing8312/WorkClaw (140 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 777 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 2026-08-30.
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…