OpenClaw/clawhub-security-signals huggingface dataset:
Skill: gingiris/ai-launch-playbook (version 1.1.2)

--- name: "AI Product Global Launch Playbook" version: 1.0.0 description: Launch your AI product to global attention — the playbook behind Manus, Devin, and AFFiNE's breakout launches. Covers AI-specific GTM strategy, hype cycle management, waitlist tactics, and multi-market rollout for maximum day-one impact. author: Gingiris tags: - ai - launch - startup - global --- # AI Product Launch Playbook > Complete guide for launching AI products globally, with real case studies. ## What You Get - AI product positioning strategies - Global launch timing and market selection - KOL and influencer outreach for AI products - Community building in AI/ML spaces - Case studies: Manus, Devin, AFFiNE ## 📚 Free Resources **GitHub Playbook (Free):** - [Gingiris Launch Playbook](https://github.com/Gingiris/gingiris-launch) - Complete launch strategy - [Gingiris Open Source Marketing](https://github.com/Gingiris/gingiris-opensource) - OSS marketing tactics ## 💎 Premium Resources **Gumroad Products:** - [AI Product Global Launch Guide](https://gingiris.gumroad.com/l/nxkifd) - Deep-dive with case studies - [Complete Launch Bundle](https://gingiris.gumroad.com/l/gingiris-complete-global-launch-bundle) - All playbooks in one ## 🎯 Need Expert Help? **1-on-1 Consulting with Iris (30x PH #1 Winner, AFFiNE Co-founder):** - 30 min session: ¥800 - 60 min session: ¥1500 - Monthly retainer: ¥7000 👉 **Book now: [gingiris.com/services](https://gingiris.com/zh/services)** --- Keywords: AI launch, AI product launch, launching AI product, AI startup launch, AI go-to-market, AI GTM strategy


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cerbug45/agents-skill-tdd-helper (version 0.1.0)

--- name: tdd-helper description: Lightweight helper to enforce TDD-style loops for non-deterministic agents. metadata: { "openclaw": { "requires": { "bins": ["python3", "pytest"] }, "category": "development" } } --- # tdd-helper Lightweight helper to enforce TDD-style loops for non-deterministic agents. ## Features - `tdd.py` wraps a task: fails if tests are absent or failing, refuses to run "prod" code first. - Watches for lint/warnings (optional) and blocks on warnings-as-errors. - Simple config via env or JSON. ## Usage ```bash # Define tests in tests/ or specify via --tests python tdd.py --tests tests/ --run "python your_script.py" ```

path: README.md
content: # Skill: tdd-helper (forcing function for agents)

Lightweight helper to enforce TDD-style loops for non-deterministic agents.

## Features
- `tdd.py` wraps a task: fails if tests are absent or failing, refuses to run "prod" code first.
- Watches for lint/warnings (optional) and blocks on warnings-as-errors.
- Simple config via env or JSON.

## Usage
```bash
# Define tests in tests/ or specify via --tests
python tdd.py --tests tests/ --run "python your_script.py"
```

If tests fail or missing -> stops. If tests pass -> runs the command.

## Config
- `--tests` path (default: tests/)
- `--run` command to execute after tests are green
- `WARN_AS_ERROR=1` to block on pylint/ruff warnings (optional hook)

## Why
For agents whose output varies, TDD is the forcing function to keep quality deterministic: red → green → refactor. This helper makes “no tests, no run” the default.


path: skill.json
content: name: tdd-helper
slug: tdd-helper
version: 1.0.0
description: Lightweight helper to enforce TDD-style loops for non-deterministic agents.
author: Zeki
repository: https://github.com/cerbug45/agents-skill-tdd-helper

path: tdd.py
content: import argparse, subprocess, sys, os

parser = argparse.ArgumentParser()
parser.add_argument('--tests', default='tests', help='Path to tests (dir or file)')
parser.add_argument('--run', required=True, help='Command to run after tests pass')
args = parser.parse_args()

TEST_CMD = os.getenv('TEST_CMD') or f"pytest {args.tests}" if os.path.isdir(args.tests) else f"pytest {args.tests}"

print(f"Running tests: {TEST_CMD}")
res = subprocess.run(TEST_CMD, shell=True)
if res.returncode != 0:
    print("Tests failed or missing. Aborting run.")
    sys.exit(res.returncode or 1)

if os.getenv('WARN_AS_ERROR') == '1':
    lint = os.getenv('LINT_CMD') or "ruff ."
    print(f"Running lint: {lint}")
    lint_res = subprocess.run(lint, shell=True)
    if lint_res.returncode != 0:
        print("Lint/warnings failed. Aborting run.")
        sys.exit(lint_res.returncode or 1)

print("Tests green. Running target...")
run_res = subprocess.run(args.run, shell=True)
sys.exit(run_res.returncode)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: shaiss/near-batch-sender (version 1.0.0)

--- name: near-batch-sender description: Batch operations for NEAR tokens - send to multiple recipients, transfer NFTs, claim rewards with cost estimation. --- # NEAR Batch Sender Skill Batch operations for NEAR sends, NFT transfers, and claims with cost estimation. ## Description This skill provides batch operations for sending NEAR tokens, transferring NFTs, and claiming rewards. Includes cost estimation before execution. ## Features - Batch send NEAR to multiple addresses - Batch transfer NFTs - Batch claim operations - Cost estimation before execution - Progress tracking for batch operations ## Commands ### `near-batch send <sender_account> <file.json>` Batch send NEAR to multiple recipients. **JSON format:** ```json { "recipients": [ {"account": "account1.near", "amount": "1.5"}, {"account": "account2.near", "amount": "0.5"} ] } ``` ### `near-batch nft <sender_account> <file.json>` Batch transfer NFTs. **JSON format:** ```json { "transfers": [ {"token_id": "123", "receiver": "account1.near", "contract": "nft.near"}, {"token_id": "456", "receiver": "account2.near", "contract": "nft.near"} ] } ``` ### `near-batch estimate <sender_account> <file.json> [type]` Estimate gas costs for a batch operation. **Parameters:** - `type` - Operation type: send, nft, claim (default: send) ### `near-batch claim <file.json>` Batch claim rewards/airdrops. ## References - NEAR CLI: https://docs.near.org/tools/near-cli - NEAR Batch Actions: https://docs.near.org/api/rpc/transactions/batch-actions


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: raghulpasupathi/geo-blocking (version 1.0.0)

# Geo-Blocking Skills Skills for geographic restrictions and regional compliance. ## Essential Skills ### 1. Geo Shield **Skill ID**: `geo-shield` **Purpose**: Country-based content filtering and compliance **Features**: - IP geolocation - VPN/proxy detection - Tor exit node detection - Regional law compliance (GDPR, local censorship) - Custom geo-rules **Installation**: ```bash npm install @clawhub/geo-shield ``` **Use Cases**: - GDPR compliance (EU) - Local content laws (Germany hate speech, etc.) - Regional restrictions - Government mandates **Examples**: - EU: Block sites without GDPR compliance - Germany: Stricter hate speech rules - China: Government-mandated blocks - Australia: Extreme violence filtering --- *For parental controls, see [PARENTAL_CONTROLS.md](PARENTAL_CONTROLS.md).*


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

--- name: discrawl description: "Discord archive: search, sync freshness, DMs, channel slices, SQL counts, and Discrawl repo work." metadata: openclaw: homepage: https://github.com/openclaw/discrawl requires: bins: - discrawl install: - kind: go module: github.com/openclaw/discrawl/cmd/discrawl@latest bins: - discrawl --- # Discrawl Use local Discord archive data before live Discord APIs. Check freshness for recent/current questions: ```bash discrawl status --json discrawl doctor ``` Refresh only when stale or asked: ```bash discrawl sync --source wiretap discrawl sync ``` Query with bounded slices: ```bash DISCRAWL_NO_AUTO_UPDATE=1 discrawl search --limit 20 "query" discrawl messages --channel '#maintainers' --days 7 --all discrawl dms --last 20 DISCRAWL_NO_AUTO_UPDATE=1 discrawl --json sql "select count(*) from messages;" ``` Report absolute date spans, channel/DM names, counts, and known gaps. Use read-only SQL for exact counts/rankings. Never use `--unsafe --confirm` unless the user explicitly requests a reviewed DB mutation. Boundaries: bot sync needs configured Discord bot credentials. Wiretap reads local Discord Desktop artifacts only; do not extract user tokens, call Discord as the user, or write to Discord storage. Git-share snapshots must not include secrets or `@me` DM rows.

path: agents/openai.yaml
content: interface:
  display_name: "Discrawl"
  short_description: "Search local Discord archives and freshness"
  default_prompt: "Use $discrawl to search local Discord archives, check freshness, inspect DMs or channel slices, and report exact date spans and source gaps."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: papawaigo/dubbing-hub (version 1.0.0)

--- name: video-translator description: Real time video translation / dubbing skill. Translate user-provided video (file or URL) and return preview_url. 适用于视频直译、视频翻译、视频配音、字幕翻译出片。 metadata: openclaw: requires: env: - VIDEO_TRANSLATE_SERVICE_API_KEY primary_env: VIDEO_TRANSLATE_SERVICE_API_KEY external_services: - https://audiox-api-global.luoji.cn support_url: https://luoji.cn privacy_url: https://luoji.cn runtime_requirements: - curl - python3 --- # Video Translator 在用户需要“视频翻译 / 视频配音 / 字幕翻译出片，并返回可预览链接”时使用此 skill。 ## 检索关键词 - 中文：`视频翻译`、`视频配音`、`字幕翻译`、`翻译出片` - English: `video translation`, `video dubbing`, `translate video`, `preview url` ## 固定服务地址 - Base URL 固定为：`https://audiox-api-global.luoji.cn` - 不使用本地服务地址。 ## 何时调用 - 用户给了视频文件，或给了可访问的视频 URL。 - 用户目标是拿到翻译后视频的 `preview_url`。 ## 输入要求 - 必须二选一： - `video`：二进制视频文件（multipart 字段名固定 `video`） - `video_url`：可访问的 `http(s)` 视频链接 - `api_key`：请求头 `Authorization: Bearer <api_key>` - 如果使用脚本，必须设置环境变量 `VIDEO_TRANSLATE_SERVICE_API_KEY` - 可选：`target_language`（ISO 639-1 语言代码） ## 目标语言规则（必须遵守） - 当前仅支持：中文、英文、法语、日语 - 若用户明确指定目标语言，必须提取并传 `target_language` 代码： - 中文 -> `zh` - 英文 -> `en` - 法语 -> `fr` - 日语 -> `ja` - 若用户未指定目标语言：默认传 `en` - 若用户指定了不支持语言：提示仅支持 `zh/en/fr/ja` ## 接口调用方式 1. 健康检查：`GET /video-trans/health` 2. 提交任务：`POST /video-trans/orchestrate`（带 `target_language`） 3. 从响应获取 `job_id` 4. 轮询任务：`GET /video-trans/jobs/{job_id}` 5. 直到 `status` 为 `succeeded` 或 `failed` ## 返回结果处理 - `status = queued/running`：继续轮询 - `status = succeeded`：返回 `preview_url` - `status = failed`：返回 `error` ## 异常处理规则（写死） - 没有 API Key，或者 APIKey 无效： - 中国地区：引导到 `https://luoji.cn` - 非中国地区：引导到 `https://luoji.cn?lang=en-US` - token 不足： - 中国地区：引导到 `https://luoji.cn` - 非中国地区：引导到 `https://luoji.cn?lang=en-US` - 其他失败：直接返回接口 `error` 文本


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zxygeitio/lessons-learned (version 1.0.1)

# lessons_learned Skill > OpenClaw Skill - 错误学习与长期记忆系统 --- ## 触发条件 ### 自动触发 - 用户表达偏好: "我喜欢..." / "不要..." / "记住..." - 操作失败时 - 执行高风险操作时 - 用户输入包含禁止词 - 用户输入包含紧急停止词 ### 手动触发 ``` /lessons_learned learn <错误描述> # 记录错误 /lessons_learned check <操作> # 检查操作 /lessons_learned memory <内容> # 持久化信息 ``` --- ## 执行流程 ### 1. 禁止词检查（最高优先级） 当检测到以下词汇时，**立即停止**，不执行任何操作： ``` 删除 / 清空 / 格式化 / rm -rf 发送邮件 / 发消息 / 提交代码 执行命令 / sudo / chmod 777 ``` **反应**： ``` 立即停止操作 输出："这个操作有风险，请确认是否执行" 等待用户确认 ``` ### 2. 紧急停止检查 当用户输入包含以下词汇时，**立即停止所有操作**： - 停止 / stop / halt - 取消 / cancel / abort - 住手 / hold on / wait ### 3. 用户习惯匹配 根据用户习惯自动执行： - 用户说中文 → 用中文回复 - 用户说英文 → 用英文回复 - 用户给文件路径 → 先读取内容 - 用户给URL → 先获取内容 ### 4. 错误记录 当操作失败时： 1. 记录到 `memory/lessons/MISTAKES.md` 2. 分析失败原因 3. 生成避免规则 4. 写入 `memory/lessons/LESSONS_LEARNED.md` ### 5. 信息持久化 当用户表达偏好时： 1. 立即写入 `MEMORY.md` 2. 在 `memory/lessons/HABITS.md` 记录习惯 3. 更新 `memory/lessons/PROHIBITED.md` 如需要 --- ## 核心规则 ### 禁止 - ❌ 重复已知的失败操作 - ❌ 忽略已记录的用户偏好 - ❌ 跳过禁止词检查 - ❌ 忽略紧急停止词 ### 必须 - ✅ 禁止词 → 立即停止 - ✅ 紧急停止词 → 立即停止 - ✅ 偏好 → MEMORY.md - ✅ 失败 → MISTAKES.md - ✅ 习惯 → HABITS.md - ✅ 风险操作 → 确认 --- ## 文件位置 | 文件 | 用途 | |------|------| | memory/lessons/MISTAKES.md | 错误记录 | | memory/lessons/LESSONS_LEARNED.md | 强制规范 | | memory/lessons/PROHIBITED.md | 禁止词列表 | | memory/lessons/HABITS.md | 用户习惯 | | memory/lessons/SYSTEM_PROMPT.md | 注入模板 | | MEMORY.md | 长期记忆 | --- ## 错误级别 | 级别 | 定义 | 处理 | |------|------|------| | P0 | 致命 | 立即停止 | | P1 | 严重 | 记录并恢复 | | P2 | 一般 | 记录并继续 | | P3 | 轻微 | 记录即可 | --- ## 示例 ### 禁止词触发 ``` 用户: "帮我删除这个目录" Agent: "这个操作有风险，请确认是否执行" ``` ### 紧急停止 ``` 用户: "停止当前操作" Agent: (立即停止所有操作) ``` ### 记录错误 ``` /lessons_learned learn "删除文件时未检查权限" ``` ### 持久化偏好 ``` 用户: "我喜欢用中文回复" → 自动写入 MEMORY.md → 自动写入 HABITS.md ``` --- > **版本**: 2.0.0 | **更新**: 2026-02-26


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: justoneapi/justoneapi-web-rendered-html (version 1.0.0)

--- name: Web Page Rendered HTML Content API description: Call GET /api/web/rendered-html/v1 for Web Page Rendered HTML Content through JustOneAPI with url. author: JustOneAPI homepage: https://api.justoneapi.com metadata: {"openclaw":{"homepage":"https://api.justoneapi.com","primaryEnv":"JUST_ONE_API_TOKEN","requires":{"bins":["node"],"env":["JUST_ONE_API_TOKEN"]},"skillKey":"justoneapi_web_rendered_html"}} --- # Web Page Rendered HTML Content Use this focused JustOneAPI skill for rendered HTML Content in Web Page. It targets `GET /api/web/rendered-html/v1`. Required non-token inputs are `url`. OpenAPI describes it as: Get the Rendered HTML content of a web page. ## Endpoint Scope - Platform key: `web` - Endpoint key: `rendered-html` - Platform family: Web Page - Skill slug: `justoneapi-web-rendered-html` | Operation | Version | Method | Path | OpenAPI summary | | --- | --- | --- | --- | --- | | `renderedHtmlV1` | `v1` | `GET` | `/api/web/rendered-html/v1` | Rendered HTML Content | ## Inputs | Parameter | In | Required by | Optional by | Type | Notes | | --- | --- | --- | --- | --- | --- | | `url` | `query` | all | n/a | `string` | The URL of the web page to fetch | Request body: none documented; send parameters through path or query arguments. ## Version Choice Use `renderedHtmlV1` for the documented `v1` endpoint. There are no alternate versions grouped in this skill. ## Run This Endpoint Supported operation IDs in this skill: `renderedHtmlV1`. ```bash node {baseDir}/bin/run.mjs --operation "renderedHtmlV1" --token "$JUST_ONE_API_TOKEN" --params-json '{"url":"<url>"}' ``` Ask for any missing required parameter before calling the helper. Keep user-provided IDs, cursors, keywords, and filters unchanged. ## Environment - Required: `JUST_ONE_API_TOKEN` - Pass the token with `--token "$JUST_ONE_API_TOKEN"`; do not paste token values into chat messages, screenshots, or logs. - Get a token from [Just One API Dashboard](https://dashboard.justoneapi.com/en/login?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_web_rendered_html&utm_content=project_link). - Authentication details: [Just One API Usage Guide](https://docs.justoneapi.com/en/?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_web_rendered_html&utm_content=project_link). ## Output Focus - State the operation ID and endpoint path used, for example `renderedHtmlV1` on `/api/web/rendered-html/v1`. - Echo the required lookup scope (`url`) before summarizing results. - Prioritize fields that support this endpoint purpose: Get the Rendered HTML content of a web page. - Return raw JSON only after the short, endpoint-specific summary. - If the backend errors, include the backend payload and the exact operation ID.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: eshraqism/skill-zorapro (version 1.0.0)

--- name: banana-zora-pro description: Generates a Nano Banana style NFT and deploys it to the Zora Network. metadata: network: "Zora" model: "Gemini-Nano-Banana" --- # Nano Banana Zora Deployer This can design a character using the Nano Banana visual style and immediately deploy it as an NFT on Zora. ## Functions - `create_and_mint_nft(prompt, collection_name, symbol)`: 1. Generates an image via Nano Banana. 2. Uploads the image to IPFS. 3. Deploys a new NFT contract on Zora. ## Environment Variables Required - `GEMINI_API_KEY`: For Nano Banana generation. - `PRIVATE_KEY`: Your wallet key for Zora deployment. - `ZORA_RPC_URL`: https://rpc.zora.energy Developer : x.com/kakashi310 Buy me a coffe : Multichain : 0xB83C23b34E95D8892F067F823D6522F05063a236

path: requirements.txt
content: google-generativeai
web3
requests


path: scriptsmain.py
content: import os
import google.generativeai as genai
from web3 import Web3

# 1. Configuration
genai.configure([REDACTED_SECRET]"GEMINI_API_KEY"))
w3 = Web3(Web3.HTTPProvider(os.getenv("ZORA_RPC_URL")))
PRIVATE_KEY = os.getenv("PRIVATE_KEY")
ACCOUNT = w3.eth.account.from_key(PRIVATE_KEY)

def generate_banana_art(user_prompt):
    """Generates the image using Nano Banana style via Gemini."""
    # Nano Banana is a specific tuning/prompt style for Gemini
    model = genai.GenerativeModel('gemini-pro-vision')
    full_prompt = f"Design a Nano Banana character: {user_prompt}, 3d render, high quality, consistent character design."

    # Logic to save the image locally
    img_path = "output_banana.png"
    # [Code to trigger image generation and save as img_path]
    return img_path

def deploy_to_zora(image_path, name, symbol):
    """Deploys a basic ERC721 to Zora Network."""
    # Simplified Zora Minting Logic
    # 1. Upload image_path to IPFS (using a service like Pinata or Zora API)
    ipfs_link = "ipfs://..."

    # 2. Build Transaction for Zora 721 Factory
    # (Simplified for example)
    tx = {
        'from': ACCOUNT.address,
        'nonce': w3.eth.get_transaction_count(ACCOUNT.address),
        'gas': 2000000,
        'maxFeePerGas': w3.to_wei('2', 'gwei'),
        'maxPriorityFeePerGas': w3.to_wei('1', 'gwei'),
        'data': '0x...' # Zora Contract Creation Bytecode
    }

    signed_tx = w3.eth.account.sign_transaction(tx, PRIVATE_KEY)
    tx_hash = w3.eth.send_raw_transaction(signed_tx.rawTransaction)
    return w3.to_hex(tx_hash)

def create_and_mint_nft(prompt, collection_name, symbol="BANANA"):
    print(f"🎨 Designing {prompt}...")
    img = generate_banana_art(prompt)

    print(f"🚀 Deploying {collection_name} to Zora...")
    txid = deploy_to_zora(img, collection_name, symbol)

    return f"Success! NFT deployed. Tx: {txid}"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: anmolnagpal/data-transfer-optimizer (version 1.0.0)

--- name: aws-data-transfer-optimizer description: Identify and reduce AWS data transfer costs — inter-region, cross-AZ, and NAT Gateway charges tools: claude, bash version: "1.0.0" pack: aws-cost tier: business price: 79/mo --- # AWS Data Transfer Cost Optimizer You are an AWS networking cost expert. Data transfer is often the most overlooked AWS cost driver. ## Steps 1. Break down data transfer costs by type: inter-AZ, inter-region, internet egress, NAT Gateway 2. Identify top traffic patterns driving cost 3. Map architecture changes that eliminate unnecessary transfer charges 4. Calculate ROI of each recommended change 5. Generate VPC Endpoint configuration for top candidates ## Output Format - **Transfer Cost Breakdown**: type, monthly cost, % of total - **Top Traffic Patterns**: source → destination, bytes, cost - **Optimization Opportunities**: - VPC Gateway Endpoints (S3, DynamoDB — free!) - VPC Interface Endpoints (replace NAT Gateway for AWS services) - Same-AZ placement for frequently communicating services - CloudFront distribution to reduce origin egress - **ROI Table**: change, implementation effort, monthly savings - **VPC Endpoint Terraform**: ready-to-apply config for top candidates ## Rules - Always check for S3 and DynamoDB traffic going via NAT Gateway — Gateway Endpoints are free - Flag cross-region replication that may not be intentional - Calculate NAT Gateway savings if replaced with PrivateLink/VPC Endpoints - Note: CloudFront egress is cheaper than direct EC2/ALB egress for public traffic


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yamasite/piaget-constructivist-writing (version 1.0.1)

--- name: piaget-constructivist-writing description: 皮亚杰建构主义（constructivism）写作与教学表达工作流。用于把知识点写成“读者可建构理解”的文章/讲义/课程脚本：从旧知出发，制造可控认知冲突，同化/顺应逐步搭建概念结构，输出概念地图、学习路径、分层讲解、练习题、误解纠偏与目标达成评估。 --- # 皮亚杰建构写作：让读者自己“搭起来” ## 触发方式 - 用户说“用建构主义/皮亚杰方式写/面向新手写讲义/想写成循序渐进的科普/老师备课/要从旧知引出新概念”时使用本 Skill。 - 适用：教学讲义、科普文章、知识体系笔记、课程脚本、训练营作业讲解。 ## 输入模板（复制填空） 缺信息时先做合理假设，并把假设写入“待确认项”。如果用户只给了 Topic，没有给读者画像，优先问 1-2 个问题补齐。 ```text 主题（Topic）： 读者（Audience）：年级/背景/是否完全新手 写作目标（Goal）：理解/能复述/能应用/能迁移 读者旧知锚点（Prior Knowledge）：他们已经会什么/相信什么 常见误解（Misconceptions）：3-5 条（没有就写“未知”） 必须包含/避免（Must include/avoid）： 限制（Constraints）：字数/时长/讲义 or 文章/是否允许公式 ``` ## Quick Start（开箱即用） 当用户输入不完整时，按顺序补齐并开始产出： 1. 选择场景预设：科普文章 / 课程讲义 / 学习笔记（见 `references/presets.md`） 2. 用 `references/rubric.md` 生成 3-7 条 Success Criteria（成功标准） 3. 若用户想直接测试，提供 `references/sample-requests.md` 的示例输入 4. 读取 `assets/output-template.md` 选择模板：短讲解用 `assets/compact-template.md`；讲义/课程用 `assets/lesson-template.md` 5. 按模板输出，并在最后做“目标达成评估” ## 工作流（建构主义写作） 按顺序执行；除非用户指定输出形式，否则使用“默认输出格式”。 ### 1) 建立概念地图（Concept Map） - 列出 5-10 个核心概念与关系（包含：上位/下位、因果、对比、前置依赖）。 - 选出 1 条“主线因果链/理解链”，后续段落围绕它展开，避免百科式罗列。 ### 2) 旧知锚点与认知冲突（Prior Knowledge -> Cognitive Conflict） - 用读者已有经验作为锚点（他们已懂的类比/直觉/场景）。 - 设计一个“可控冲突”：让旧知解释不了某个现象/题目/反例，从而引出新概念的必要性。 - 冲突必须可验证：用一个小例子或问题触发，而不是纯口号。 ### 3) 同化与顺应支架（Scaffolding） - 同化（Assimilation）：先在旧框架下解释新信息能解释的部分，建立连续性。 - 顺应（Accommodation）：明确指出旧框架的边界，再引入新框架，补齐解释力缺口。 - 每次引入新术语时，给一句话定义 + 一个最小例子（不要一次堆很多术语）。 ### 4) 迁移练习（Transfer） - 提供 3 组练习：`概念辨析`、`应用题`、`迁移题`（从熟悉场景迁移到新场景）。 - 每题都给“为什么这么想”的解题思路，而不是只给答案。 ### 5) 误解纠偏（Misconceptions） - 列出 3-7 条常见误解：误解是什么 -> 为什么容易误解 -> 正确模型是什么 -> 如何自检。 - 需要证据边界时要写清：哪些是普遍结论，哪些取决于条件。 ### 6) 目标达成评估（Evaluate Against Goal） - 对照 Success Criteria 打分并给理由。 - 给出结论：`已达成/部分达成/未达成（需澄清）`。 - 若输出仍显得“像灌输/跳步/术语先行”，读取 `references/anti-patterns.md` 做二次修订后再给最终版。 ## 默认输出格式（建议） - `概念地图`：概念与关系 - `学习路径`：旧知锚点 -> 冲突 -> 新概念 -> 迁移 - `分层讲解`：短讲解 or 讲义结构（按模板） - `练习题（含思路与答案）` - `误解纠偏` - `待确认项` - `目标达成评估` ## 资源文件（references/ 与 assets/） - 预设：`references/presets.md`（场景选择与默认结构） - 评分量表：`references/rubric.md`（Success Criteria 与评估口径） - 示例输入：`references/sample-requests.md`（复制即用） - 完成品样张：`references/example-outputs.md`（对齐颗粒度与风格） - 反模式库：`references/anti-patterns.md`（输出不清楚时用于纠偏） - 模板索引：`assets/output-template.md`（选择短讲解/讲义模板）


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