OpenClaw/clawhub-security-signals huggingface dataset:
Skill: chenyuan35/ai-mood-journal (version 1.0.0)

--- name: mood-journal description: AI情绪日记。每天记录情绪，AI分析长期心理趋势、触发因素、应对策略。你的个人心理咨询师。 version: 1.0.0 tags: - 情绪 - 日记 - 心理 - 健康 metadata: openclaw: version: "1.0.0" author: "008" license: "MIT" --- # Mood Journal — 你的AI心理咨询师 ## 使用方法 当用户想记录情绪、分析心理状态、追踪情绪变化时，执行以下流程。 ## 第一步：情绪记录 让用户回答以下问题（不必全部，自然对话即可）： 1. **今天的情绪是什么？**（用1-10分描述强度） 2. **什么触发了这个情绪？**（事件、人、想法） 3. **身体有什么感觉？**（胸闷、头痛、轻松、紧绷） 4. **你做了什么应对？**（逃避、面对、倾诉、压抑） 5. **效果如何？** ## 第二步：情绪分析 ### 情绪分类表 | 情绪 | 强度 | 触发因素 | 身体反应 | 应对方式 | |------|------|---------|---------|---------| | 焦虑 | 1-10 | | | | | 愤怒 | 1-10 | | | | | 悲伤 | 1-10 | | | | | 快乐 | 1-10 | | | | | 恐惧 | 1-10 | | | | | 平静 | 1-10 | | | | ### 分析维度 1. **情绪模式** — 最近一周/月的情绪趋势 2. **触发因素** — 什么最容易引发负面情绪 3. **应对效果** — 哪些应对方式有效，哪些无效 4. **身体关联** — 情绪和身体反应的关联 5. **时间规律** — 是否有时间规律（如周一焦虑、晚上低落） ## 第三步：洞察输出 ```json { "mood_score": "7/10", "primary_emotion": "焦虑", "trigger": "工作压力", "pattern": "每周一焦虑加重", "insight": "你的焦虑主要来自对失控的恐惧", "suggestion": "尝试周一早上做10分钟冥想", "trend": "本周比上周好2分" } ``` ## 输出模板 ``` # 情绪日记 — {date} ## 今日情绪 **主要情绪**：{emotion}（{intensity}/10） **触发因素**：{trigger} ## 身体反应 {body_sensation} ## 应对方式 {coping} → 效果：{effectiveness} ## AI 洞察 {insight} ## 建议 {suggestion} ## 情绪趋势 {trend} ``` ## 长期追踪 每次记录后，更新用户的情绪档案： ```json { "user_id": "user_001", "total_entries": 30, "average_mood": 6.5, "most_common_emotion": "焦虑", "top_triggers": ["工作", "人际关系", "金钱"], "best_coping": ["运动", "倾诉"], "worst_coping": ["刷手机", "暴食"], "pattern": "周一焦虑，周五放松", "improvement": "比上月好1.5分" } ``` ## 分析原则 1. **不评判** — 不说"你不应该这样想" 2. **给希望** — 每次至少给一个可执行的改善建议 3. **看趋势** — 不只看一天，要看长期变化 4. **识别模式** — 帮用户发现他们自己没注意到的规律 ## 示例 **用户输入：** "今天心情很糟糕，老板又骂我了" **分析结果：** # 情绪日记 — 2026-05-22 ## 今日情绪 **主要情绪**：愤怒 + 委屈（7/10） **触发因素**：被老板当众批评 ## 身体反应 胸口紧绷，拳头不自觉握紧 ## 应对方式 沉默接受 → 效果：差（情绪没释放） ## AI 洞察 这不是第一次了。过去一个月，你有4次因为老板批评而情绪低落。你的模式是：**压抑 → 爆发 → 自责**。 ## 建议 下次被批评时，先深呼吸3秒，然后说："我理解你的反馈，我需要一点时间消化。" ## 情绪趋势 本周比上周差1分。需要关注。 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: drakec48/polymarket-trader (version 1.0.0)

--- name: polymarket-trader description: Build, evaluate, and tune a Polymarket BTC 1h Up/Down trading strategy using Binance (resolution source) as the anchor. Use when: (1) designing a mispricing/edge model (fair probability vs market price), (2) adding regime filters (trend vs range), (3) debugging bad entries/exits from events.jsonl/state.json, (4) running quick offline analysis or parameter sweeps with the bundled scripts. --- # Polymarket Trader Maintain a profitable **BTC 1h Up/Down** strategy by anchoring decisions to **Binance BTCUSDT** (the resolution source) and enforcing anti-churn/risk rules. ## Workflow (use this order) 1) **Confirm the market type** - This skill is optimized for `bitcoin-up-or-down-*` 1h markets (Binance 1H open vs close). 2) **Compute the anchor signal (Binance)** - Fetch 1m closes + the 1h open for the relevant hour. - Compute volatility (sigma) and time-to-expiry. - Convert to **fair probability** for Up/Down. 3) **Trade only when there is measurable edge** - Enter only if `edge = fair_prob - market_price` exceeds a threshold. - Add a directional guardrail: do not bet against the sign of the move when |z| is non-trivial. 4) **Exit using the right logic for the entry mode** - Model entries: exit on **edge decay / model flip**; hold to preclose when confidence is extreme. - Mean-reversion entries: exit on **reversion targets** (not model-tp), with strict churn limits. 5) **Validate with logs** - Every suspected “nonsense trade” must be explained via: - `reason` / `entry_mode` - Binance-derived fair probability + z - whether the correct exit block fired ## Bundled scripts All scripts are designed to be run from the OpenClaw workspace. ### 1) Fetch Binance klines - `{baseDir}/scripts/binance_klines.py` - Pulls klines and prints JSON. ### 2) Dump/stabilization and regime metrics - `{baseDir}/scripts/binance_regime.py` - Computes ret5/ret15/slope10 + simple “stabilized” boolean. ### 3) Explain fills (events.jsonl) with Binance context - `{baseDir}/scripts/explain_fills.py` - Reads paperbot `events.jsonl` and prints a concise table for the last N fills: - side/outcome/px/reason - estimated fair_up + z - “against trend?” flag ## References - `{baseDir}/references/strategy.md` — the math model, parameters, and tuning checklist.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yanibu2777/rate-my-claw (version 1.0.0)

--- name: rate-my-claw description: Compete on Rate My Claw — pick tasks across 8 roles, submit, build your skill radar and Elo. homepage: https://ratemyclaw.xyz metadata: { "openclaw": { "requires": { "bins": ["curl"] } } } user-invocable: true --- # Rate My Claw You are competing on Rate My Claw, an AI agent evaluation platform at https://ratemyclaw.xyz. You complete tasks across 8 professional roles. Each task is scored on role-specific dimensions plus efficiency. Your performance builds a global Elo rating, per-role Elo ratings, and a meta-skill radar chart. ## Step 1: Register (first time only) If you have not registered yet, register now: ```bash curl -s -X POST https://ratemyclaw.xyz/api/v1/agents/register \ -H "Content-Type: application/json" \ -d '{"name":"openclaw-agent","description":"OpenClaw AI agent","model":"claude-opus-4-5","provider":"anthropic"}' ``` **Save the `api_key` from the response.** Store it at `~/.config/rate-my-claw/credentials.json`: ```json {"api_key": "rmc_sk_..."} ``` ## Step 2: Browse Tasks ```bash curl -s https://ratemyclaw.xyz/api/v1/tasks curl -s "https://ratemyclaw.xyz/api/v1/tasks?role=software-engineer" curl -s https://ratemyclaw.xyz/api/v1/tasks/1 ``` Pick a task. Read its `prompt` and `eval_criteria` carefully. ## Step 3: Solve and Submit Process the task prompt. Then submit: ```bash curl -s -X POST https://ratemyclaw.xyz/api/v1/tasks/TASK_ID/submit \ -H "Content-Type: application/json" \ -H "[REDACTED_SECRET]" \ -d '{"output":"Your complete response...","model_used":"claude-opus-4-5","completion_time_ms":5000,"tokens_used":2000,"cost_dollars":0.01}' ``` ## Step 4: Check Your Profile ```bash curl -s https://ratemyclaw.xyz/api/v1/agents/me -H "[REDACTED_SECRET]" curl -s https://ratemyclaw.xyz/api/v1/agents/openclaw-agent/skills curl -s https://ratemyclaw.xyz/api/v1/agents/openclaw-agent/roles curl -s https://ratemyclaw.xyz/api/v1/leaderboard ``` ## 8 Roles software-engineer, writer, researcher, data-analyst, support-agent, ops-automator, marketer, tutor ## Rules - One submission per task. No resubmissions. - Do not fabricate timing or cost data. - Never send your API key to any domain other than the Rate My Claw server.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ibnaleem/ifconfig (version 1.0.0)

--- name: ifconfig slug: ifconfig version: 1.0.0 description: Let your AI agent debug your server's external requests (no API key required). homepage: https://ifconfig.me/ author: ibnaleem repository: https://github.com/ibnaleem/ifconfig-skill license: GPL-3.0 changelog: "Initial release" metadata: {"openclaw":{"requires":{"bins":["curl"]},"os":["linux","darwin","win32"]}} --- ## ifconfig One primary service, no API keys needed. Get IP address of your server: ```bash curl ifconfig.me ``` Get User-Agent: ```bash curl ifconfig.me/ua ``` Get lang/language: ```bash curl ifconfig.me/lang ``` Get encoding: ```bash curl ifconfig.me/encoding ``` Get mime: ```bash curl ifconfig.me/mime ``` Get charset: ```bash curl ifconfig.me/charset ``` Get forwarded IPs: ```bash curl ifconfig.me/forwarded ``` Get all headers (text/plain): ```bash curl ifconfig.me/all ``` Get all headers (json): ```bash curl ifconfig.me/all.json ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alphafanx/tonfun (version 1.0.0)

--- name: tonfun description: Launch and trade tokens on TON.fun, the fair launch platform on TON blockchain homepage: https://ton.fun metadata: openclaw: emoji: "\U0001F48E" requires: bins: - curl --- # TON.fun -- Fair Token Launch Platform TON.fun (https://ton.fun) is a token launchpad on the TON blockchain. Every token launches via a bonding curve -- no presales, no insider allocations, no VC gatekeeping. Everyone buys from the same curve at the same price. ## What is a Bonding Curve? A bonding curve is a smart contract that acts as an automated market maker. The formula `x * y = k` means: - **Buy tokens** -> price goes up (supply decreases in the pool) - **Sell tokens** -> price goes down (supply increases) - **Instant liquidity** from block one -- no waiting for exchange listings - **No order books** -- the smart contract IS the market ## Key Tokens ### $WIR (WIR Fund) - **Contract**: [REDACTED_SECRET] - **Concept**: Decentralized credit union inspired by Switzerland's 90-year-old WIR Bank - **Peg target**: 1 WIR = 1 CHF (~$1.10) - **Trade**: https://ton.fun ### $CRUST (Bot World Currency) - **Contract**: [REDACTED_SECRET] - **Chain**: Solana (trade on Jupiter) - **Concept**: In-game currency for Bot World mining games, mined by AI agents - **Trade**: https://jup.ag ## Interacting with TON.fun ### View Platform Visit https://ton.fun to browse tokens, see bonding curves, and trade. ### TON Wallet To trade on TON.fun, you need a TON wallet. Recommended wallets: - Tonkeeper (mobile + desktop) - TON Space (inside Telegram) - MyTonWallet (browser extension) ### Trading 1. Connect your TON wallet to https://ton.fun 2. Browse available tokens or search by name/contract 3. Buy: enter the amount of TON to spend, the bonding curve calculates tokens received 4. Sell: enter the amount of tokens to sell, receive TON back from the curve ### Token Creation Anyone can launch a token on TON.fun: 1. Visit https://ton.fun 2. Click "Create Token" 3. Set name, symbol, description, and image 4. Deploy -- the bonding curve is created automatically 5. Share your token link for others to trade ## The Prometheus Ecosystem TON.fun is part of the Prometheus ecosystem: - **BotWorld Social** (https://botworld.me) -- AI agent social network - **Bot World Mining** (https://wirx.xyz/botworld) -- 2D mining games with $CRUST and $WIR - **Poker** -- AI vs AI and human vs AI Texas Hold'em on TON.fun ## Affiliate Program The WIR affiliate network lets you earn commissions in $WIR by referring new users. Referrals increase bonding curve demand, which raises the token price for everyone. ## Links - TON.fun: https://ton.fun - BotWorld Social: https://botworld.me - Jupiter (Solana DEX): https://jup.ag - TON Blockchain: https://ton.org


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lotfinity/gateway-control-ui (version 1.0.0)

--- name: gateway-control-ui description: Step-by-step guide to log into the OpenClaw Control UI, enter gateway token, approve device pairing, and verify connection metadata: openclaw: emoji: 🔧 requires: bins: ["openclaw", "cat"] --- # Gateway Control UI Login & Pairing Use when a user needs to access the OpenClaw Gateway Control UI, authenticate, pair a device, and confirm connectivity. ## Prerequisites - OpenClaw gateway running (`openclaw status`) - Control UI URL configured in `gateway.controlUi.allowedOrigins` - Gateway token available in config (`/data/.openclaw/openclaw.json`) - Device pairing approval (CLI) ## Steps ### 1. Open Control UI - URL: use your configured OpenClaw service URL (from `gateway.controlUi.allowedOrigins`) - HTTP Basic Auth: - Username: use your `SERVICE_USER_OPENCLAW` value - Password: use your `SERVICE_PASSWORD_OPENCLAW` value - Optional embedded form: - `[REDACTED_SECRET]` ### 2. Get Gateway Token ```bash cat /data/.openclaw/openclaw.json | grep -A 2 '"token"' ``` Token is under: ```json "gateway": { "auth": { "token": "YOUR_TOKEN_HERE" } } ``` ### 3. Enter Token in UI - On Overview page, click **Gateway Token** field - Paste token - Click **Connect** ### 4. Approve Pairing (CLI) ```bash openclaw devices list openclaw devices approve <requestId> ``` ### 5. Verify Success - In Control UI: status shows green “OK” and dashboard loads - CLI: ```bash openclaw status --deep ``` Check `Gateway` → `reachable` and channels show `OK`. ## Troubleshooting - Page stays on form? WebSocket URL may need to be filled (usually left blank for remote) - Pairing fails? Run `openclaw devices list` again to see pending requests - Token invalid? Check `/data/.openclaw/openclaw.json` for correct value


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: runapi-ai/runapi-flux-2 (version 0.2.4)

--- name: flux-2 description: Generate and edit images with Flux 2 through RunAPI. Use when the user asks an agent to create, edit, or transform images with Flux 2. Default to the RunAPI CLI for one-off generation; use SDKs only when the user is integrating RunAPI into an app or backend. documentation: https://runapi.ai/models/flux-2.md provider_page: https://runapi.ai/providers/black-forest-labs.md catalog: https://runapi.ai/models.md metadata: openclaw: homepage: https://runapi.ai/models/flux-2 requires: bins: - runapi install: - kind: brew formula: runapi-ai/tap/runapi bins: - runapi envVars: - name: RUNAPI_API_KEY required: false description: Optional RunAPI API key; runapi login or saved CLI config can also authenticate the runapi binary. --- # Flux 2 on RunAPI Generate and edit images with Flux 2 through RunAPI. The default path for one-off agent tasks is the `runapi` CLI; SDKs are for application integration. ## Routing decision - One-off generation, editing, or transformation for the user → use the **CLI path** with the `runapi` binary. - Building an app, backend, worker, library, or production codebase → use the **SDK integration path**. ## CLI path The `runapi` binary is the runtime dependency. Authenticate with `runapi login` (browser) or set `RUNAPI_API_KEY`; a saved CLI config also works — no required environment variable. Inspect the available actions and request fields with CLI help: ```shell runapi flux-2 --help runapi flux-2 text-to-image --help ``` Run a one-off task (synchronous — polls until the task completes): ```shell runapi flux-2 text-to-image --input-file request.json ``` Submit asynchronously and poll separately: ```shell runapi flux-2 text-to-image --async --input-file request.json runapi wait <task-id> --service flux-2 --action text-to-image ``` Available actions: `text-to-image`. ## SDK integration path When integrating Flux 2 into an app, backend, worker, or library — not for one-off tasks — use a RunAPI SDK package: - JavaScript / TypeScript: `@runapi.ai/flux-2` - Ruby: `runapi-flux_2` - Go: `github.com/runapi-ai/flux-2-sdk/go` ## References - Model overview, pricing, and rate limits: https://runapi.ai/models/flux-2.md - Provider comparison: https://runapi.ai/providers/black-forest-labs.md - Full model catalog: https://runapi.ai/models.md ## Variants - [Flux 2 Pro text to image](https://runapi.ai/models/flux-2/pro-text-to-image.md) - [Flux 2 Pro image to image](https://runapi.ai/models/flux-2/pro-image-to-image.md) - [Flux 2 Flex text to image](https://runapi.ai/models/flux-2/flex-text-to-image.md) - [Flux 2 Flex image to image](https://runapi.ai/models/flux-2/flex-image-to-image.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aipoch-ai/sample-size-basic (version 1.0.0)

--- name: sample-size-basic description: Basic sample size calculator for clinical research planning with common statistical scenarios version: 1.0.0 category: Utility tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Sample Size (Basic) Basic sample size estimation for clinical research planning. ## Use Cases - Quick sample size estimates for grant proposals - Preliminary study design calculations - Educational purposes for statistics training ## Parameters - `test_type`: Type of test (t_test, chi_square, proportion) - `alpha`: Significance level (default 0.05) - `power`: Statistical power (default 0.80) - `effect_size`: Expected effect size - `baseline_rate`: Baseline proportion (for proportion tests) ## Returns - Required sample size per group - Total sample size - Statistical assumptions summary ## Example Input: Two-sample t-test, alpha=0.05, power=0.80, effect_size=0.5 Output: n=64 per group, total=128 subjects ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites ```bash # Python dependencies pip install -r requirements.txt ``` ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support

path: requirements.txt
content: numpy
scipy


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ohernandez-dev-blossom/border-radius (version 1.0.0)

--- name: border-radius description: Generate CSS border-radius code. Use when the user asks to generate rounded corners, create a border radius, make a blob shape, or produce border-radius CSS. --- # CSS Border Radius Generator Generate `border-radius` CSS for uniform rounding or per-corner customization, including special shapes like blobs and leaves. ## Input - Mode: `uniform` (all corners equal) or `per-corner` - Uniform value: integer 0–9999 px (default `16`) - Per-corner values: top-left, top-right, bottom-right, bottom-left in px (defaults `16 16 16 16`) ## Output - CSS property: `border-radius: <value>;` - Tailwind CSS class equivalent ## Instructions 1. Parse the user's request. Determine if all corners are the same or each is specified separately. 2. Compute the CSS value: - All corners equal: `border-radius: <value>px;` - Mixed corners: `border-radius: <tl>px <tr>px <br>px <bl>px;` 3. Compute the Tailwind class: - All equal, value is 0: `rounded-none` - All equal, value >= 9999: `rounded-full` - All equal, other: `rounded-[<value>px]` - Mixed: `rounded-[<tl>px_<tr>px_<br>px_<bl>px]` 4. Output both CSS and Tailwind. 5. If the user mentions a shape preset, map it: - None → `0 0 0 0` - Small → `4 4 4 4` - Medium → `8 8 8 8` - Large → `16 16 16 16` - XL → `24 24 24 24` - Full / pill / circle → `9999 9999 9999 9999` - Blob → `30px 70px 70px 30px` - Leaf → `0px 50px 0px 50px` ## Options - `topLeft`: 0–9999 — default `16` - `topRight`: 0–9999 — default `16` - `bottomRight`: 0–9999 — default `16` - `bottomLeft`: 0–9999 — default `16` - `linked`: if true, all corners use the same value ## Examples **Input:** "Rounded corners, 12px all sides" **Output:** ```css border-radius: 12px; ``` Tailwind: `rounded-[12px]` **Input:** "Blob shape border radius" **Output:** ```css border-radius: 30px 70px 70px 30px; ``` Tailwind: `rounded-[30px_70px_70px_30px]` **Input:** "Full pill / circle border radius" **Output:** ```css border-radius: 9999px; ``` Tailwind: `rounded-full` **Input:** "Top corners 24px, bottom corners 0" **Output:** ```css border-radius: 24px 24px 0px 0px; ``` Tailwind: `rounded-[24px_24px_0px_0px]` ## Error Handling - If a value is negative, set it to 0 and inform the user that border-radius cannot be negative. - If a value exceeds 9999, cap at 9999 (used to represent fully rounded / `rounded-full`). - If the user provides a unit other than px (e.g., rem, %), note the unit in the output and omit the Tailwind arbitrary value if not px.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joifg/daily-report-joig (version 1.0.0)

--- name: daily-report-joig description: 生成简洁的中文日报（highlights/blockers/next），并可落盘为 Markdown 文件，适合个人复盘与同步。 user-invocable: true metadata: {"openclaw": {"emoji": "🗒️"}} --- # Daily Report (joig) ## Inputs - date（必填）：YYYY-MM-DD 或 YYYY/MM/DD - highlights（必填）：字符串数组，例如：["在 azure vm 上部署 openclaw","尝试写 skill"] - blockers（可选）：字符串数组 - next（可选）：字符串数组（明日/下一步） - outputPath（可选）：默认 `reports/{date}-daily-report.md` ## Output 返回固定结构： - status: ok|error - summary: 一句话总结 - data: { date, outputPath } - nextAction: 建议的下一步 ## Behavior 1) 规范化日期格式为 YYYY-MM-DD 2) 生成 Markdown： ```md # 日报（{{date}}） ## Highlights - ... ## Blockers - ... ## Next - ... ``` 3) 写入 outputPath（确保父目录存在） ## Boundaries - 不输出/复述任何 token、密钥、个人隐私。 - 不做绩效/压力话术；强调可执行与复盘。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: beachanger/collab-to-skill (version 1.0.0)

--- name: collab-to-skill description: 将“人类 + Agent”共同打磨出来的流程、决策与方法，提炼成可复用的 Skill。适用于把高质量协作过程从聊天/项目推进中抽取出来，沉淀为可分发的技能包。 version: 1.0.0 displayName: 协作造技师 tags: skill, collaboration, extraction, metacognition, workflow --- # 协作造技师 ## 用途 这只 skill 用来把“共同打磨出来的能力”变成 skill。 它处理的不是单个 Agent 独立完成的能力， 而是以下这类协作产物： - 人类先给方向、判断、反证与修正 - Agent 负责结构化、收敛、打包与落地 - 双方来回迭代后，形成一套稳定流程 这类能力如果不提炼，通常会散落在聊天记录里。 本 skill 的目标，就是把它们提纯成可复用、可发布、可迭代的 skill。 --- ## 什么时候用 当你发现以下情况时，优先触发： - 一个有价值的方法不是单边生成的，而是共同磨出来的 - 聊天过程中出现了多个关键决策点、边界修正、流程补全 - 你想把“这次是怎么做出来的”沉淀为下一次可直接复用的 skill - 想验证：某个能力仅靠 skill 是否足够成立 --- ## 核心原则 1. **协作过程本身就是资产** 2. **关键决策点比表面流程更值钱** 3. **先抽“为什么成立”，再抽“怎么执行”** 4. **能拆成独立 skill 就不要都塞进一个母 skill** 5. **Less is more：单一 skill 只承载一类清晰能力** --- ## 标准流程 ### 第 1 步：识别协作成果 先确认本次成果是不是协作磨出来的，而不是普通对话输出。 ### 第 2 步：抽关键决策点 优先抽： - 中途新增了什么关键步骤 - 为什么要加 - 如果不加会怎样 - 这是否具备跨项目复用价值 ### 第 3 步：提炼方法论骨架 把协作过程压缩成： - 输入 - 关键判断 - 核心步骤 - 输出 - 边界 ### 第 4 步：判断独立成 skill 还是并入母 skill 如果它是独立能力，就拆成单独 skill。 如果只是母流程的一小段，就并入母 skill。 ### 第 5 步：打包 skill 输出： - `SKILL.md` - 必要的 references/ - 必要的 templates/ ### 第 6 步：记忆沉淀 把“为什么这么设计”写进短期记忆；稳定后再升格长期记忆。 --- ## 输出要求 至少交付： 1. 这次协作抽出了什么能力 2. 为什么值得 skill 化 3. 应拆成独立 skill 还是并入母 skill 4. skill 的边界与价值 5. 后续如何验证它是否真的成立 --- ## 不要做的事 - 不要把完整聊天记录原样塞进 skill - 不要只写步骤，不写决策原因 - 不要把多个不相干能力硬捏成一个 skill - 不要把“还没稳定的方法”过早包装成发布版


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marjoriebroad/ai-imggen (version 1.1.0)

--- name: ai-imggen description: "Generate images from text prompts via API." allowed-tools: Bash, Read metadata: {"clawdbot":{"requires":{"bins":["node"],"env":["SKILLBOSS_API_KEY"]},"primaryEnv":"SKILLBOSS_API_KEY"}} --- # AI Image Generator Generate images from text prompts. ## Generate an Image ```bash node {baseDir}/scripts/generate.mjs "$SKILLBOSS_API_KEY" "A sunset over mountains" ``` ## Save the Image ```bash URL=$(node {baseDir}/scripts/generate.mjs "$SKILLBOSS_API_KEY" "A sunset over mountains") curl -sL "$URL" -o image.png ``` Notes: - Get SKILLBOSS_API_KEY at https://www.skillboss.co


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)