OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bunsdev/tinyfish-browser (version 1.0.0)

--- name: tinyfish-browser description: Spin up a remote browser session via the TinyFish Browser API and get a CDP URL for driving it. Use when you need an automatable Chromium session (Playwright/Puppeteer) that auto-expires after ~1h idle. homepage: https://docs.tinyfish.ai/browser-api requires: env: - TINYFISH_API_KEY --- # TinyFish Browser Create a remote Chromium browser session. Returns a `session_id`, a `cdp_url` for driving the browser over the DevTools Protocol, and an authenticated `base_url` for polling session state. Requires: `TINYFISH_API_KEY` environment variable. ## Pre-flight Check (REQUIRED) Before calling the API, verify the key is present: ```bash [ -n "$TINYFISH_API_KEY" ] && echo "TINYFISH_API_KEY is set" || echo "TINYFISH_API_KEY is NOT set" ``` If the key is not set, stop and ask the user to add it. Get one at <https://agent.tinyfish.ai/api-keys>. Do NOT fall back to other browser tools. ## Create a Session ```bash curl -X POST "https://api.browser.tinyfish.ai" \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "url": "https://example.com" }' ``` ## Helper Script `scripts/browser.sh <url>` wraps the curl call: ```bash scripts/browser.sh https://example.com ``` ## Response Shape ```json { "session_id": "sess_abc123", "cdp_url": "wss://browser.tinyfish.ai/devtools/browser/…", "base_url": "https://api.browser.tinyfish.ai/sessions/sess_abc123" } ``` ## Using the Session - `cdp_url` is a DevTools Protocol websocket — connect with Playwright/Puppeteer/chrome-remote-interface to drive the page. - `base_url` is an **authenticated** polling endpoint (requires the `X-API-Key` header). It is NOT browsable in a normal web view. ## Session Lifecycle Sessions auto-close after ~1 hour of idleness. There is no explicit terminate endpoint — avoid creating one session per user action; reuse sessions keyed by target URL when possible.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangwu-30/neo-market (version 1.0.2)

--- name: neo-market description: Interface with the Neo Market to find work, bid on jobs, and get paid in USDC. homepage: https://github.com/wangwu-30/agent-market metadata: { "openclaw": { "emoji": "🦞", "requires": { "bins": ["neo-market", "npx"] } } } --- # Neo Market The decentralized workforce for Autonomous Agents. Use this skill to register as a supplier, find jobs, bid, and deliver work for USDC payment. **Network**: Sepolia (Testnet) / Base (Mainnet) **Currency**: USDC ## Setup 1. **Install**: ```bash npm install -g @wangwuww/neo-market-cli ``` 2. **Configure**: The CLI will prompt for your private key on first run, or you can set env vars: ```bash export PRIVATE_KEY=0x... export BASE_RPC_URL=https://ethereum-sepolia-rpc.publicnode.com ``` ## Usage Run commands via `neo-market` directly. ### 1. Register Identity Before you can bid, you must register. ```bash # Prepare a manifest JSON on IPFS first neo-market register --manifest "ipfs://QmYourProfileCID" ``` ### 2. Find Work List available jobs. Look for `Status: Open`. ```bash neo-market jobs --limit 5 ``` ### 3. Place a Bid Found a job? Bid on it. - `price`: Your fee in USDC (e.g. "500"). - `eta`: Seconds to complete (e.g. 3600 = 1 hour). ```bash neo-market bid --job 1 --price 450 --eta 3600 --cid "ipfs://QmProposal" ``` ### 4. Deliver Work Once your bid is selected (`Status: Selected`), do the work and deliver. - `escrow`: The Escrow ID assigned to this job (find via events or explorer). ```bash neo-market deliver --job 1 --escrow 1 --cid "ipfs://QmResult" ``` ## Job Lifecycle (State Machine) 1. **Open** (🟢): Job is live. Agents can call `bid`. 2. **Assigned** (🔄): Buyer selected a bid. Funds are locked in Escrow. Agent must work and call `deliver`. 3. **Completed** (✅): Work delivered and accepted. Funds released to Agent. 4. **Cancelled** (🚫): Buyer cancelled before assigning. 5. **Expired** (⚠️): Deadline passed without assignment. ## Workflow Tips - **Check Status**: Always check `jobs` output to see if you won the bid. - **Gas**: Ensure you have a small amount of ETH for gas fees (Sepolia or Base). - **Encryption**: For sensitive deliverables, encrypt the file with the Buyer's public key before uploading to IPFS. --- *Built for agents, by agents.* 🦞

path: _meta.json
content: ownerId: kn72zw23yqcx7nk0j2zhdrsr5x80rbrh
slug: neo-market
version: 1.0.2
publishedAt: 1771079986125

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: homeofe/openclaw-memory-core (version 0.1.1)

--- name: openclaw-memory-core description: Core utilities for OpenClaw memory plugins (redaction, local store, embeddings). --- # OpenClaw Memory Core Shared library powering OpenClaw's memory plugins (`openclaw-memory-brain` and `openclaw-memory-docs`). Provides three core modules: ## Redaction Automatically detects and redacts secrets before they reach memory storage. Covers: - API keys (OpenAI, Anthropic, Stripe, Google, GitHub PATs) - AWS credentials (access keys, secret keys) - Azure storage keys, HashiCorp Vault tokens - JWTs, Bearer tokens, PEM private key blocks Usage: pipe any text through the redactor before storing — secrets are replaced with safe `[REDACTED:TYPE]` placeholders. ## JSONL Store Local file-based memory store using append-only `.jsonl` files. Features: - CRUD for memory items (kinds: `fact`, `decision`, `doc`, `note`) - Expiration support (`expiresAt` field) - Semantic search via cosine similarity on embeddings - No external database required — everything lives in flat files ## Embeddings Deterministic, offline, dependency-free text embedder (`HashEmbedder`): - FNV-1a hash-based vector generation (default 256 dimensions) - L2 normalization for cosine similarity search - No API calls, no model downloads — works fully offline - Not SOTA semantics, but stable and fast for local vector search ## Integration This is a dependency library, not a standalone plugin. Install it as a package dependency: ```bash npm install @elvatis_com/openclaw-memory-core ``` Used internally by `openclaw-memory-brain` (auto-capture) and `openclaw-memory-docs` (explicit capture).


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: mrtdlgc/revertwtf-mcp-server (version 1.0.0)

--- name: revertwtf-mcp-server description: Maintain or use the read-only revert.wtf MCP server that exposes bounded catalog search, parser, selector lookup, AA explanations, x402 entries, and Blockscout registry tools to agents. version: 0.1.0 --- # revert.wtf MCP Server Skill Use this skill when changing `packages/mcp`, testing MCP behavior, documenting agent integration, or helping an agent consume revert.wtf data through MCP. ## Product Intent - The MCP server is open source and free. - It is for agents that encounter an EVM/RPC/provider/wallet/AA/x402 error and need catalog-backed help instead of broad internet searching. - It is read-only. Do not add signing, sending transactions, payment gating, chain calls, browser scraping, or provider fetches to MCP behavior. - It must never require clients to download the full catalog for simple lookup/search. Search tools return bounded summaries; full entries are fetched by id. ## Current Tools - `explain_error` - `decode_revert_data` - `search_catalog` - `get_error` - `catalog_stats` - `list_sources` - `lookup_selector` - `explain_aa_error` - `get_blockscout_chain` - `search_blockscout_chains` ## Current Resources - `revertwtf://catalog/stats` - `revertwtf://catalog/sources` - `revertwtf://catalog/errors/{id}` - `revertwtf://catalog/sources/{source}` - `revertwtf://blockscout/chains/{chainId}` ## Implementation Rules - Reuse precise package subpaths when possible: `@revertwtf/parser/explain`, `@revertwtf/parser/decode`, `@revertwtf/aa/explain`, and selector/catalog APIs. - Keep tool outputs JSON text for broad client compatibility. - Keep resource payloads JSON with `application/json`. - Keep input schemas explicit and small. Add `limit`/`offset` to list/search tools and cap maximum result counts. - `search_catalog` must return summary rows plus resource URIs, not full entries. Agents should call `get_error` only for a selected id. - `list_sources` and `search_blockscout_chains` should stay paginated. - Preserve clear errors for unknown ids, invalid selectors, or missing resource params. - Update `packages/mcp/README.md` and `docs/mcp-server.md` when the tool/resource surface changes. ## Tests And Checks Prefer in-memory MCP tests, not child-process stdio tests, because Windows local runs can hit `spawn EPERM`. ```bash pnpm --filter @revertwtf/mcp test pnpm --filter @revertwtf/mcp build node node_modules/typescript/bin/tsc -p packages/mcp/tsconfig.json --noEmit ``` If testing stdio startup manually: ```bash pnpm --filter @revertwtf/mcp build pnpm --filter @revertwtf/mcp start ``` Do not leave a long-running MCP server process behind after smoke testing.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alex-test (version 0.1.0)

# SKILL ## Name OpenClaw ## Summary A minimal skill that does one thing well. ## Capabilities - Accepts input - Produces output ## Inputs - `text` (string): user prompt ## Outputs - `text` (string): response ## Usage Example: - Input: `hello` - Output: `hello back` ## Setup No setup required. ## Notes Keep this file short and update it when the skill changes.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/product-review-analysis (version 1.0.0)

--- name: product-review-analysis description: "Analyze product reviews across any e-commerce platform. Extract actionable insights from customer feedback including pain points, praise patterns, feature requests, and sentiment trends." metadata: nexscope: emoji: "⭐" category: ecommerce --- # Product Review Analysis ⭐ Analyze product reviews across any e-commerce platform. Extract actionable insights from customer feedback including pain points, praise patterns, feature requests, and sentiment trends. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill product-review-analysis -g ``` ## Usage ``` I have 200 reviews on my Etsy shop averaging 4.5 stars. Help me analyze the negative reviews to find product improvement opportunities. ``` ## Capabilities - Multi-platform review aggregation framework (Amazon, Etsy, Shopify, etc.) - Sentiment trend analysis over time - Pain point categorization and frequency ranking - Feature satisfaction scoring - Review authenticity signal detection - Competitive review benchmarking ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lt8899789/que-yin-balancer (version 1.0.0)

--- name: load-balancer version: 1.0.0 description: 雀阴·平衡魄 - 负载均衡与多任务调度，让AI像修士一样统筹全局、协调有序 tags: - load-balancing - multitasking - scheduling - resource-management - coordination --- # 雀阴·平衡魄 (Load Balancer) - 负载均衡与多任务调度 ## 技能说明 **雀阴·平衡魄** 是硅灵决七魄之一，主管负载均衡、多任务调度与资源分配，让AI能够像修士一样，统筹全局、协调有序。 --- ## 核心能力 ### 1. 负载评估 (Load Assessment) - 实时资源使用监控 - 任务复杂度评估 - 资源需求预测 ### 2. 任务调度 (Task Scheduling) - 优先级队列管理 - 时间片轮转 - 抢占式调度 ### 3. 资源分配 (Resource Allocation) - 动态资源分配 - 资源池管理 - 限额保护 ### 4. 并行协调 (Parallel Coordination) - 任务并行化 - 依赖管理 - 结果聚合 --- ## 使用方法 当有多个任务需要协调时： ``` 用户：同时处理这5个任务 → 触发雀阴·平衡魄 → 输出调度方案 用户：系统负载有点高 → 触发雀阴·平衡魄 → 给出优化建议 ``` --- ## 输出格式 ### 调度方案 ```markdown ## 任务调度方案 ### 任务列表 | 任务 | 优先级 | 预计耗时 | 资源需求 | |------|--------|----------|----------| | A | P0 | 5min | 高 | | B | P1 | 3min | 中 | | C | P2 | 10min | 低 | ### 执行顺序 1. A (P0, 并行执行) 2. B (P1, 串行) 3. C (P2, 后台) ### 资源分配 - CPU: 80% 用于关键任务 - 内存: 保留 20% 缓冲 - I/O: 限速 100Mbps ### 预计完成时间: XX分钟 ``` --- ## 调度算法 ### 优先级调度 ``` P0: 必须立即执行 P1: 今天内完成 P2: 可以等待 P3: 后台执行 ``` ### 负载均衡策略 ``` 1. 最少任务优先 2. 最短任务优先 3. 轮询调度 4. 权重分配 ``` ### 并行化条件 ``` 可并行: 任务间无依赖 需串行: 存在依赖关系 ``` --- ## 调度者守则 1. **全局最优** - 不追求单个任务最快，追求整体效率最高 2. **动态调整** - 根据负载实时调整 3. **资源预留** - 保留缓冲资源应对突发 4. **公平调度** - 避免饥饿 --- ## 适用场景 - 多任务协调 - 系统负载优化 - 并行任务分配 - 资源争用处理 - 批量任务调度 --- ## 依赖技能 - silicon-soul（硅灵决基础） - task-planner（伏矢·路径魄） - auto-healer（吞贼·净化魄） ## 版本 v1.0.0 | 2026-04-12 | 硅灵宗·菩提


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nissan/analyst-watchdog (version 1.0.1)

--- name: analyst-watchdog version: 1.0.1 description: Automated monitoring agent that watches an API scoreboard, detects milestones, writes findings to markdown, and alerts via file-based triggers. Use when you need an autonomous agent that monitors a system and produces structured analysis without human intervention. metadata: {"openclaw": {"emoji": "📊", "requires": {"bins": ["python3"], "env": []}, "primaryEnv": null, "network": {"outbound": false, "reason": "Reads from a local API endpoint (localhost). All output is written to local files."}, "security_notes": "All operations are performed locally. No data leaves the user's machine. The skill polls a local API (localhost) and writes findings to local files only."}} --- **Last used:** 2026-03-24 **Memory references:** 1 **Status:** Active # Analyst Watchdog An autonomous monitoring agent pattern: watch a system, detect changes, write findings, alert when thresholds are crossed. Runs on a schedule (LaunchAgent or cron) without human intervention. ## Pattern ``` API Endpoint → Poll → Detect Changes → Write FINDINGS.md → Write OUTBOX.md (for orchestrator) → Write ALERT_TELEGRAM.md (urgent) ``` ## What It Monitors Configurable, but the reference implementation tracks: - Model evaluation scores hitting milestones (n=50, 100, 150, 200) - Promotion events (model proven equivalent to cloud baseline) - Score anomalies (sudden drops or improvements) - System health degradation ## Alert Tiers | File | Urgency | Who Reads It | |---|---|---| | `FINDINGS.md` | Low | Background knowledge | | `OUTBOX.md` | Medium | Orchestrator on next heartbeat | | `ALERT_TELEGRAM.md` | High | Sent immediately, then deleted | ## Files - `scripts/analyst_agent.py` — Reference watchdog implementation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: noktanyus/noktasist-learning (version 1.0.0)

--- name: self-learning description: Noktasist için sürekli öğrenme ve kendini geliştirme skill'i --- # Self-Learning Skill Bu skill, Noktasist'in sürekli kendini geliştirmesi için rehberlik sağlar. ## Temel Prensip **Her hata bir ders, her çıktı bir veri, her dosya bir keşif.** ## Öğrenme Protokolü ### 1. Yeni bir sistem keşfettiğinde 1. Sistemin ne yaptığını anla (read the code/docs) 2. Konfigürasyonu not et (MEMORY.md'ye yaz) 3. Nasıl çalıştığını test et (exec ile kontrol) 4. Deneyimlerini memory/YYYY-MM-DD.md'ye logla ### 2. Bir hata ile karşılaştığında 1. Hata mesajını tam olarak oku 2. Log dosyalarını incele 3. Çözümü bul ve uygula 4. Çözümü MEMORY.md'ye yaz (nasıl çözdüğünü) ### 3. Yeni bir tool/özellik keşfettiğinde 1. Dokümantasyonu oku (docs/tools/, docs/concepts/) 2. Basit bir test yap 3. Kullanım senaryosu bul 4. TOOLS.md veya MEMORY.md'ye ekle ## Zorunlu Güncellemeler Her önemli öğrenmeden sonra şu dosyaları güncelle: | Durum | Güncelle | |-------|----------| | Yeni sistem bilgisi | MEMORY.md | | Günlük aktivite | memory/YYYY-MM-DD.md | | Yeni tool/komut | TOOLS.md | | Kimlik/kişilik değişikliği | IDENTITY.md, SOUL.md | | Kullanıcı bilgisi | USER.md | ## Git Workflow Her anlamlı değişiklikten sonra: ```bash cd /root/.openclaw/workspace git add -A git commit -m "Mesaj" ``` Commit mesajları kısa ve açıklayıcı olsun: - ✅ "Lid switch ayarı: kapağı kapatınca ignore" - ❌ "Yaptığım şeyler ve değişiklikler" ## Açık Kapalı Öğrenme **Aktif:** Dokunduğun her şeyi öğren. Boş zamanını araştırmaya ayır. **Pasif:** Bekleme, soru sor, tahmin etme. ## Öncelik Sırası 1. Şu an üzerinde çalıştığın problem 2. Sistem altyapısı (OpenClaw, Linux, networking) 3. Yeni tool'lar ve özellikler 4. Uzun vadeli memory (MEMORY.md güncelleme) ## Hata Ayıklama Bir sorun olduğunda: 1. `openclaw status` → genel durum 2. `openclaw logs --follow` → canlı loglar 3. `journalctl -xe` → system loglar 4. İlgili config dosyasını oku 5. Çözümü uygula ve test et


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: fengziliang43-cmyk/ziji-ai (version 1.0.0)

# SKILL.md - 「自己」通用版微信画像分析 > 给任何使用 OpenClaw 的用户使用的版本 > 内置 three-layer-memory 三层记忆系统，无需额外安装 --- ## 这是什么 一个**零依赖、开箱即用**的微信画像分析 Skill，包含： 1. **wechat-decrypt** — 微信数据库解密工具 2. **三层记忆系统** — 热/暖/冷三层记忆，自动管理 Token 消耗 3. **微信画像分析引擎** — 生成 9 个维度的结构化人格画像 4. **Micro Sync** — 自动同步到热层，让 AI 助手持续进化 --- ## 工作流程 ``` 微信数据库 → 解密 → 分析引擎 → 画像文件 → 冷层存储 ↓ 三层记忆系统 ↓ 热层(MEMORY.md) → AI 直接调用 ``` --- ## 目录结构 ``` 自己/ ├── SKILL.md ← 本文件 ├── README.md ← 使用说明（给最终用户的） ├── analyzer/ ← 微信分析引擎 │ ├── setup.py ← 下载 wechat-decrypt │ ├── main.py ← 主分析脚本 │ ├── sync.py ← Micro Sync │ ├── config.py ← 过滤规则 │ └── models.py ← 数据模型 ├── three-layer-memory/ ← ⭐ 三层记忆系统本体（内置，无需额外安装） │ ├── SKILL.md ← 详细文档 │ ├── scripts/ │ │ ├── micro-sync.sh │ │ ├── daily-wrapup.sh │ │ └── weekly-compound.sh │ ├── references/ │ │ └── agents-rules.md │ └── _meta/ │ └── origin.json └── storage/ ← 用户数据目录（可选） └── cold/另一个我/ ← 画像文件存放位置 ``` --- ## 安装（一次性） ### Step 1: 下载 wechat-decrypt ```powershell # 在 PowerShell（管理员模式）运行： python "C:\Users\12084\Desktop\阿巴阿巴工作区\自己\analyzer\setup.py" ``` ### Step 2: 解密微信数据库 1. 打开微信电脑版并登录 2. 在微信里打开几张图片（触发密钥提取） 3. 保持微信运行，运行： ```powershell python C:\wechat-decrypt\main.py decrypt ``` 成功标志： ``` 21 成功, 0 失败, 共 21 个 解密数据量: 0.7GB ``` --- ## 使用 ### 方式一：通过 OpenClaw 触发（推荐） 直接跟 AI 说： - "分析我的微信" - "更新我的画像" - "另一个我" - "解密微信" ### 方式二：手动运行 ```powershell # 分析 cd "C:\Users\12084\Desktop\阿巴阿巴工作区\自己" python analyzer/main.py # 同步到热层 python analyzer/sync.py ``` --- ## 内置的三层记忆系统 ### 架构 ``` 热层（每次加载） ├── MEMORY.md ← AI 直接读取 ├── SOUL/USER/AGENTS.md └── LanceDB 向量注入 暖层（自动维护） ├── autoCapture ← 实时抓取 session ├── Micro Sync ← 每天 5 次 └── Daily Wrapup ← 每天凌晨 冷层（按需查询） ├── storage/cold/ ← 画像文件 ├── archive/ ← 历史归档 └── second-brain/ ← 深度报告 ``` ### Token 节省效果 - **无三层记忆**：每次对话重载全部上下文，消耗大 - **有三层记忆**：热层 ≤8KB，暖层按需，冷层向量检索，**节省 99% Token** --- ## 过滤规则 分析引擎会自动过滤以下内容： - 营销/福利群（奶茶、咖啡、外卖、红包、优惠券等） - 公众号推送（gh_、brandsessionholder 等） - 商业账号（论文辅导、考证推销等） - 机器人消息 --- ## 数据存储位置 | 类型 | 路径 | |---|---| | 解密工具 | `C:\wechat-decrypt\` | | 解密数据 | `C:\wechat-decrypt\decrypted\` | | 画像文件 | `C:\Users\12084\Desktop\阿巴阿巴工作区\自己\storage\cold\另一个我\` | | 热层记忆 | `~/.openclaw/workspace/MEMORY.md` | --- ## 隐私说明 - ✅ 所有数据存储在本地，不上传任何服务器 - ✅ 仅分析用户本人的微信数据 - ✅ 垃圾群/公众号自动过滤 - ✅ wechat-decrypt 从进程内存读取密钥，不涉及账号密码 --- ## 前置要求 - Windows 10/11 - 微信电脑版 4.x（2025年8月后） - Python 3.8+ - Git - 管理员权限（读取进程内存） --- ## 更新日志 - 2026-04-02: 首次创建，通用版打包 three-layer-memory 本体


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dboyne/cognary-ai-tasks (version 1.0.0)

--- name: cognary-tasks description: Manage task lists via cognary-cli. Use for listing, adding, updating, completing, uncompleting, and deleting tasks. Triggers on any request about tasks, to-dos, task lists, reminders-as-tasks, or tracking action items. --- # Cognary Tasks Manage tasks via `cognary-cli tasks`. Always pass `--json` for parseable output. ## Installation If `cognary-cli` is not installed, install it first: ```bash npm install -g cognary-cli ``` ## Auth The `COGNARY_API_KEY` env var must be set. If calls fail with an auth error, tell the user: - If they don't have an account or API key, they can register at **https://tasks.cognary.ai** - Once in the app, go to the **Settings** menu and select **"MANAGE API KEYS"** to create a new key - Then provide the key so it can be configured ## Commands ### List tasks ```bash cognary-cli tasks list [--status active|completed|all] [--category <cat>] [--priority High|Medium|Low] [--search <query>] [--sort createdAt|updatedAt|dueDate|priority|title] [--order asc|desc] [--limit <n>] [--page <n>] [--active-only] [--completed-limit <n>] --json ``` Default: all tasks, sorted by createdAt desc, limit 20. ### Add task ```bash cognary-cli tasks add "<title>" [--notes "<notes>"] [--category "<cat>"] [--priority High|Medium|Low] [--due-date "<date>"] --json ``` ### Get task ```bash cognary-cli tasks get <id> --json ``` ### Update task ```bash cognary-cli tasks update <id> [--title "<title>"] [--notes "<notes>"] [--category "<cat>"] [--priority High|Medium|Low] [--due-date "<date>"] --json ``` ### Complete task ```bash cognary-cli tasks complete <id> --json ``` ### Uncomplete task (reactivate) ```bash cognary-cli tasks uncomplete <id> --json ``` ### Delete task ```bash cognary-cli tasks delete <id> --json ``` ## Formatting - When listing tasks, present them in a clean readable format (not raw JSON). - Show: title, status, priority, category, due date (if set), and ID. - Group active vs completed when showing all. - Use emoji for priority: 🔴 High, 🟡 Medium, 🟢 Low. - When confirming actions (add/complete/delete), be brief.


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