OpenClaw/clawhub-security-signals huggingface dataset:
Skill: eric060/academic-paper-summarizer (version 1.0.0)

# academic-paper-summarizer 学术论文摘要技能，快速提取论文核心内容。 ## 功能 - PDF 解析 - 核心观点提取 - 方法总结 - 结论概括 - 关键数据提取 - 通俗化解释 ## 依赖 - Python 3.8+ - pypdf - pdfplumber ## 使用 ```python # 总结论文 python skills/academic-paper-summarizer/scripts/summarize.py \ --input paper.pdf \ --output summary.md \ --length "medium" # 批量处理 python skills/academic-paper-summarizer/scripts/batch.py \ --input-dir papers/ \ --output-dir summaries/ # 提取关键数据 python skills/academic-paper-summarizer/scripts/extract.py \ --paper paper.pdf \ --fields ["method", "results", "conclusion"] ``` ## 输出格式 ```markdown # 论文摘要 ## 基本信息 - 标题：XXX - 作者：XXX - 期刊：XXX - 年份：2026 ## 核心问题 研究要解决什么问题？ ## 方法 采用了什么方法？ ## 主要发现 关键数据和结论 ## 局限性 研究的不足之处 ## 通俗解释 用大白话解释研究内容 ``` ## 注意事项 - 支持中英文论文 - 保持学术严谨性


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kankinku/aegis-intel-stack (version 0.1.0)

--- name: aegis-intel-stack version: 0.1.0 summary: "Aegis Intel Stack: pre/post execution micro-skills + manifest/delta re-buy triggers" tags: ["skills", "evm", "gas", "tx", "delta", "manifest"] --- # Aegis Intel Stack (Utility) This skill bundle provides a reference implementation + packaging for the MVP offerings: - `health_ping` - `gas_snapshot` - `tx_explain` - `intel_manifest` - `intel_delta_update` ## Local run ```bash cd aegis-suite npm install cp apps/utility-seller/.env.example apps/utility-seller/.env npm run build node apps/utility-seller/dist/dev_server.js ``` ## Call examples ```bash curl -s localhost:8787/call/health_ping -X POST -H 'content-type: application/json' -d '{"message":"hi"}' ``` ```bash curl -s localhost:8787/call/gas_snapshot -X POST -H 'content-type: application/json' -d '{"chain":"base","urgency":"standard","gas_units":150000}' ``` ## Launch automation (docs generation) ```bash cd aegis-suite npm run launch:profile npm run launch:changelog npm run launch:metrics ``` Outputs: - `apps/launch-agent/docs/publish_profile.md` - `apps/launch-agent/docs/CHANGELOG.md` - `apps/launch-agent/docs/metrics_report.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: haohanyang92/volume-price-analysis (version 1.0.0)

--- name: volume-price-analysis description: 通过成交量判断价格走势真实性，避免被"诱多"或"诱空"。用于A股技术分析。 version: 1.0.0 --- # 量价关系验证 Skill ## 核心能力 通过成交量判断价格走势的真实性，避免被"诱多"或"诱空"。 ## 量价规则 ### 有效上涨 - 价格创新高 - 成交量放大（较前一日放量30%以上） - **结论**：有效突破，可跟进 ### 虚假上涨（诱多） - 价格创新高 - 成交量缩量 - **结论**：警惕主力出货，不可追 ### 底部反转 - 价格企稳 - 成交量连续3日温和放大 - **结论**：资金进场信号，可考虑建仓 ### 有效下跌 - 价格创新低 - 成交量放大 - **结论**：可能加速赶底 ### 虚假下跌（诱空） - 价格创新低 - 成交量缩量 - **结论**：空头乏力，可能反转 ## 实战规则 - 个股涨停但成交量萎缩 → 说明封单不强，次日大概率低开 - 涨停伴随放量突破前期压力位 → 有效上涨信号，可果断跟进 - 缩量上涨至压力位 → 警惕回调 ## 触发场景 - "这个涨停有效吗" - "这是诱多还是真涨" - "能追吗" - "缩量上涨可靠吗"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: evezart/llm-cost-optimizer (version 1.0.0)

--- name: llm-cost-optimizer description: Analyze LLM API usage logs and suggest cost optimizations. Use when you need to reduce LLM spending, compare model costs, find downgrade opportunities, detect caching wins, or compress verbose outputs. Supports OpenAI, Anthropic, Google, DeepSeek, and Meta models. --- # LLM Cost Optimizer Analyze LLM API usage patterns and suggest optimizations to reduce costs by 50%+. ## Quick Start Run the analysis script against a usage log JSON file: ```bash python3 scripts/analyze.py --input usage_logs/ ``` ## Input Format Usage logs are JSON arrays of objects with: - `timestamp` (ISO 8601) - `model` (e.g. "gpt-4o", "claude-sonnet-4-20250514") - `input_tokens` (int) - `output_tokens` (int) - `task_type` (optional: "general", "coding", "classification", "extraction", "summary", "creative", "analysis") - `session_id` (optional) - `cached` (optional bool) ## Optimization Strategies 1. **Downgrade**: Find simple calls (low output, basic task types) on premium models and suggest cheaper alternatives 2. **Cache**: Detect repeated prompt patterns and recommend prompt caching 3. **Compress**: Find verbose outputs and suggest prompt tuning for conciseness ## Supported Models & Pricing All pricing is per 1M tokens (input/output). Rates are in the `PRICING` dict in `scripts/analyze.py`. ## Model Tiers - **Premium**: gpt-4o, claude-sonnet-4, gemini-2.5-pro - **Mid**: gpt-4.1-mini, claude-haiku-3-5, gemini-2.5-flash, deepseek-v3 - **Budget**: gpt-4.1-nano, deepseek-r1, llama-4-maverick Downgrade suggestions map premium → mid → budget for simple tasks.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: admirobot/media-observer-writer (version 1.0.0)

--- name: writer description: Fix AI writing patterns that create repetitive and robotic content --- # AI Writing Pattern Fixes ## Paragraph Opener Trap Scan first word of each paragraph - if 3+ start with "This/The/It" = robotic Vary with: "Meanwhile", "Consider", "Beyond that", action verbs, specific nouns Lead with action: "Marketing increased" not "The marketing team increased" ## Rhythm Monotony Trap Avoid same sentence length patterns - mix 5-word punches with 20-word flows Use strategic fragments. For emphasis. For pace breaks. Read aloud test: monotone rhythm = boring ## Vague Claim Trap Replace adjectives with numbers: "significant growth" → "40% growth" Swap evaluations for evidence: "impressive results" → "cut response time from 3s to 300ms" Delete weasel words: somewhat, fairly, quite, rather, slightly ## List Overuse Trap Use prose when explaining relationships or building arguments Use bullets only for instructions, features, options Flow test: if bullets feel choppy, try connected sentences ## Parallel Structure Breaks Match verb forms: "hiking, swimming, reading" not "hiking, swimming, to read" Match sentence patterns: "codes fast, debugs thoroughly, ships daily" Keep same grammatical structure in lists ## Transition Word Crutches Avoid: Furthermore, Moreover, Additionally, In conclusion Use echo technique: end paragraph with concept X, start next with concept X Pick up last idea and expand naturally ## Voice Drift in Long Documents Tone drift check: mark every 200 words - same person speaking? Pick 3-5 key phrases and use consistently throughout Don't switch from beginner-friendly to expert jargon mid-document ## Word Economy Traps Cut phrases: "in order to" → "to", "due to the fact that" → "because" One verb wins: "make improvement" → "improve", "conduct investigation" → "investigate" 80% rule: can you say it in 80% of words? Do it. ## Nominalization Trap Use verbs not nouns: "implementation of" → "implement" Action over abstraction: "decision-making process" → "decide" Zombie noun test: -tion, -ment, -ness endings often hide better verbs

path: _meta.json
content: ownerId: kn73vp5rarc3b14rc7wjcw8f8580t5d1
slug: writer
version: 1.0.2
publishedAt: 1770664525321

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: amandaclarke61/venus-ble-vibrator (version 1.1.0)

--- name: venus-ble-vibrator description: Control a Venus (Cachito) BLE vibrator from natural language. Calls a local HTTP server that broadcasts BLE commands to the toy via macOS CoreBluetooth. Requires hardware setup — see the ToyBridge repo before installing. metadata: {"openclaw": {"os": ["darwin"]}} --- # Venus BLE Vibrator Control Control a **Venus / Cachito (小猫爪) BLE vibrator** using natural language through OpenClaw. > This is a device-specific skill for Cachito-protocol toys. If your device is supported by [Buttplug.io](https://iostindex.com), use the `intiface-control` skill instead — no reverse-engineering needed. > **macOS only.** The server uses CoreBluetooth. --- ## Setup Follow the [ToyBridge setup guide](https://github.com/AmandaClarke61/toybridge) — complete Steps 1–3 (discover device ID, configure, verify locally), then start the server: ```bash uv run 4-bridge/server.py ``` Leave this terminal open. The server runs on **port 8888**. --- ## Commands the agent will use ### Vibrate at intensity ```bash curl -s -X POST http://host.docker.internal:8888/vibrate \ -H "Content-Type: application/json" \ -d '{"intensity": 60}' ``` `intensity`: 0–100 (0 = stop) ### Stop immediately ```bash curl -s -X POST http://host.docker.internal:8888/stop ``` ### Check status ```bash curl -s http://host.docker.internal:8888/status ``` > If OpenClaw runs natively (not in Docker), replace `host.docker.internal` with `localhost`. --- ## Intensity guide | Range | Feel | |--------|---------| | 1–20 | Gentle | | 30–50 | Medium | | 60–80 | Strong | | 90–100 | Maximum | --- ## Preset patterns | Pattern | What it does | |---------|-------------| | `pulse` | Bursts of 80%, 5 times | | `wave` | Ramp up 20→100%, then back down, x2 | | `tease` | 30% → 70% → 100%, escalating, then stop | Example: *"Run the wave pattern"* or *"Give me a 30-second tease session"* --- ## Agent rules - Always stop (intensity 0) after a timed session unless user says to keep going - Do **not** use the `notify` tool — use `bash` with `curl` - Replace `host.docker.internal` with `localhost` if OpenClaw is not in Docker --- ## Troubleshooting | Problem | Fix | |---------|-----| | `BT not ready` error | Check Bluetooth is on, grant permission in System Settings → Privacy | | `connection refused` | Make sure `uv run 4-bridge/server.py` is running | | Device doesn't respond | Double-check `DEVICE_ID` in `4-bridge/ble_worker.py` matches your Cachito controller | | Wrong intensity | Values are clamped to 0–100 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: odinbot33/oee-knowledge-base (version 1.0.0)

# 🐾 Knowledge Base (RAG) — Your Second Brain > by Odin's Eye Enterprises — Ancient Wisdom. Modern Intelligence. Save anything, recall it semantically. Personal RAG-powered knowledge base with SQLite + embeddings. ## What It Does 1. **Ingest** — Save text, URLs, files, notes into your knowledge base 2. **Query** — Semantic search across everything you've saved 3. **Retrieve** — Get relevant context for any question ## Trigger Phrases - "remember this" - "save this to the knowledge base" - "what do I know about" - "search my notes" - "KB query" ## Usage ```bash # Ingest text python ingest.py "The key insight from today's meeting was..." # Ingest from a file python ingest.py --file notes.md # Query the knowledge base python query.py "What did we discuss about pricing?" # Full KB management python kb.py stats python kb.py search "topic" ``` ## Files - `kb.py` — core KB engine (embeddings, storage, retrieval) - `ingest.py` — CLI for adding content - `query.py` — CLI for searching - `kb.db` — SQLite database (auto-created) ## Requirements - Python 3.10+ - `ANTHROPIC_API_KEY` or `OPENAI_API_KEY` for embeddings ## For Agents Save context: `python ingest.py "TEXT"` Retrieve context: `python query.py "QUESTION"` <!-- 🐾 Muninn never forgets -->


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: xujfcn/serp-outline-extractor (version 0.2.0)

--- name: serp-outline-extractor description: Turn a target keyword or query into a search-informed content outline with likely subtopics, questions, and comparison angles. Useful for SEO briefs, blog planning, and landing-page drafting. Compatible with OpenAI-compatible runtimes and tested with Crazyrouter. homepage: https://crazyrouter.com metadata: {"crazyrouter":{"type":"neutral","tested":true,"recommended_base_url":"https://crazyrouter.com/v1","cta":"Try content workflows on Crazyrouter"}} --- # SERP Outline Extractor Generate a search-informed outline from a keyword, topic, or draft query. ## When to use - planning SEO articles - building content briefs - extracting likely section patterns from search demand - improving article structure before drafting ## Recommended runtime This skill works with OpenAI-compatible runtimes and has been tested on Crazyrouter. ## Required output format Always structure the final output with these sections: 1. Query or target keyword 2. Search intent 3. Likely audience 4. Recommended angle 5. Proposed H1 6. H2 outline 7. FAQ ideas 8. Comparison angles 9. Freshness notes 10. Content brief notes ## Suggested workflow 1. input a target keyword or query 2. classify primary and secondary search intent 3. gather likely search-result patterns and common section types 4. infer likely headings, FAQs, and comparison angles 5. output a practical outline for writing or optimization ## Generation rules - Classify intent before proposing the outline. - Keep the likely answer near the top for informational queries. - Include comparison sections only when the query suggests evaluation behavior. - Add freshness framing when the topic is version-sensitive or year-sensitive. - Avoid generic filler sections with weak search intent justification. ## Example prompts - Build an outline for “how to get a Claude API key in 2026”. - Create a search-informed brief for “OpenAI-compatible API gateway”. - Suggest headings and FAQ sections for “AI model pricing comparison”. ## References Read these when preparing the final outline: - `references/serp-pattern-checklist.md` - `references/outline-schema.md` ## Crazyrouter example ```python from openai import OpenAI client = OpenAI( [REDACTED_SECRET]", base_url="https://crazyrouter.com/v1" ) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marposins/pcap-analyzer (version 0.1.0)

name: pcap-analyzer description: Analyze PCAP/PCAPNG files with tshark and produce a structured network-forensics summary (talkers, ports, DNS, TLS, HTTP, anomalies). homepage: https://www.wireshark.org/docs/man-pages/tshark.html metadata: { "openclaw": { "emoji": "🦈", "requires": { "bins": ["tshark", "awk", "sed"], "files": ["/home/tom/openclaw-tools/pcap_summary.sh"] }, "notes": [ "This skill runs local analysis only. It does not exfiltrate the PCAP.", "Prefer read-only access; do not modify user files." ] } } --- # PCAP Analyzer (tshark) This skill turns packet captures into a practical report a human can act on. It is designed for lab work, incident triage, and CPENT-style exercises. ## What it produces A structured report with: - **Capture metadata**: file type, size, first/last timestamp (if available) - **Top talkers**: endpoints by packets/bytes (IPv4/IPv6 when present) - **Conversations**: top TCP/UDP conversations - **Service/port view**: top TCP/UDP destination ports - **DNS**: most common queried names + suspicious patterns (DGA-ish, long labels) - **TLS**: SNI / Server Name and common JA3-like fingerprints when present (best-effort) - **HTTP**: host headers / URLs when present (best-effort, only if decrypted/plain) - **Anomalies** (best-effort heuristics): - SYN-only scans / high SYN rate - excessive RSTs - retransmission bursts - rare destination ports - single host contacting many unique hosts (beaconing-like) ## Inputs You must provide: - `pcap_path`: Full path to a `.pcap` or `.pcapng` file **on this machine**. Optional: - `focus_host`: IP to focus on (filters summaries around that host) - `time_window`: A display filter time window if user specifies (best-effort guidance only) ## How to run (terminal) ```bash {baseDir}/scripts/analyze.sh "/full/path/to/capture.pcapng"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: mcg-tries-to-code/toolflow-openclaw-operator (version 0.3.0)

# ToolFlow OpenClaw Operator Use this skill when work should be run as a ToolFlow job rather than improvised inside a single agent turn. ## What this skill is This is the OpenClaw-facing operator wrapper for ToolFlow. It is meant to complement the ToolFlow runtime and plugin surfaces by teaching the agent when a job should be expressed as a bounded workflow with durable state, approvals, recovery, and progress visibility. ## Quick install This package now ships with a bundled local ToolFlow plugin/runtime payload. After installing the skill from ClawHub, run from the installed skill folder: ```sh ./scripts/install-toolflow-openclaw.sh ./scripts/verify-toolflow-openclaw.sh ``` The installer links the bundled ToolFlow plugin into OpenClaw locally, using the payload included with this skill package. Canonical runtime repository: - <https://github.com/mcg-tries-to-code/ToolFlow> ## When to use ToolFlow Prefer ToolFlow when the job benefits from one or more of the following: - a durable run id - an explicit step graph - dry-run classification before execution - exact approval boundaries for elevated work - receipts, manifests, and recovery - user-visible progress updates during longer execution Typical examples: - multi-step diagnostics - bounded automation runs - workflows that may pause for approval and resume later - jobs where recovery after interruption matters - longer builds where the user should receive progress updates Do not bother when the work is a trivial one-off read or edit. ## Operator loop 1. Frame the work as a narrow, typed workflow. 2. Dry-run it when feasible. 3. Submit the workflow through ToolFlow. 4. Inspect manifest state rather than guessing from conversation. 5. If a step requires approval, approve the exact step and resume. 6. If interrupted, recover before replay. ## Required habits - Prefer safe typed lanes before elevated lanes. - Keep workflows small, explicit, and reviewable. - Treat the ToolFlow ledger as canonical truth. - Do not silently widen workflow scope during execution. - Keep long-running work observable rather than silent. ## Important packaging note This skill now bundles the local ToolFlow plugin/runtime payload needed for OpenClaw installation. It still pairs best with: - the ToolFlow authoring layer when workflow construction help is needed - the canonical ToolFlow repository for source, updates, and documentation Canonical source repository: - <https://github.com/mcg-tries-to-code/ToolFlow>

path: skill.json
content: name: toolflow-openclaw-operator
version: 0.3.0
description: Bundled OpenClaw operator skill and local plugin installer for ToolFlow workflows with durable state, approvals, recovery, and progress updates.

path: toolflow-bundle/plugin/examples/workflows/patch-after-approval.yaml
content: # TODO: example workflow


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: tobeyrebecca/generate-estate-planning-law-firm-client-education-handout (version 1.0.0)

--- name: Generate Estate Planning Law Firm Client Education Handout description: "Create a polished explainer handout with visuals, FAQs, and clear next steps for a estate planning law firm. Use when handling client education work..." allowed-tools: Bash, Read metadata: openclaw: emoji: "📝" homepage: "https://www.skillboss.co/skills/generate-estate-planning-law-firm-client-education-handout" --- # Generate Estate Planning Law Firm Client Education Handout Create a polished explainer handout with visuals, FAQs, and clear next steps for a estate planning law firm. Use when handling client education work... ## Use When Use this skill for client education work for estate planning attorneys, firm operators. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: estate planning law firm education handout, estate planning law firm client explainer, estate planning law firm FAQ guide, estate planning law firm visual handout - Search intent: client education - Canonical slug: `generate-estate-planning-law-firm-client-education-handout` - Install query: Install Generate Estate Planning Law Firm Client Education Handout with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Estate Planning Law Firm Client Education Handout] for my estate planning law firm ## Notes - Create a polished explainer handout with visuals, FAQs, and clear next steps for a estate planning law firm - Use human review before sending to clients or treating output as legal advice. ## SEO Keywords - estate planning law firm education handout - estate planning law firm client explainer - estate planning law firm FAQ guide - estate planning law firm visual handout


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yash-kavaiya/dialogflow-cx-advanced (version 1.0.0)

--- name: dialogflow-cx-advanced description: Manage advanced features in Google Dialogflow CX via REST API. Use for environments, webhooks, and deployment management. Supports v3beta1 API. --- # Dialogflow CX Advanced Manage advanced features in Google Dialogflow CX via REST API for deployment and external integrations. ## Prerequisites - Google Cloud project with Dialogflow CX API enabled - Service account or OAuth credentials with Dialogflow API access - `gcloud` CLI authenticated OR bearer token ## Authentication ### Option 1: gcloud CLI (recommended) ```bash gcloud auth application-default login [REDACTED_SECRET] auth print-access-token) ``` ### Option 2: Service Account ```bash export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json" [REDACTED_SECRET] auth application-default print-access-token) ``` ## API Base URL ``` https://dialogflow.googleapis.com/v3beta1 ``` Regional endpoints available: - `https://{region}-dialogflow.googleapis.com` (e.g., `us-central1`, `europe-west1`) ## Common Operations ### List Environments ```bash curl -X GET \ "https://dialogflow.googleapis.com/v3beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/${AGENT_ID}/environments" \ -H "Authorization: Bearer ${TOKEN}" ``` ### Create Environment ```bash curl -X POST \ "https://dialogflow.googleapis.com/v3beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/${AGENT_ID}/environments" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "displayName": "production", "description": "Production environment" }' ``` ### List Webhooks ```bash curl -X GET \ "https://dialogflow.googleapis.com/v3beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/${AGENT_ID}/webhooks" \ -H "Authorization: Bearer ${TOKEN}" ``` ### Create Webhook ```bash curl -X POST \ "https://dialogflow.googleapis.com/v3beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/${AGENT_ID}/webhooks" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "displayName": "Order Fulfillment", "genericWebService": { "uri": "https://your-webhook.com/fulfill" } }' ``` ## Key Resources | Resource | Description | |----------|-------------| | **Environments** | Deployment stages (draft, production) | | **Webhooks** | External fulfillment endpoints | | **Continuous Testing** | Ongoing health monitoring | ## Quick Reference For detailed API reference: - **Advanced Features**: See [references/advanced.md](references/advanced.md) ## Scripts - `scripts/advanced.py` — CLI wrapper for advanced operations ### Usage ```bash python scripts/advanced.py list-environments --agent AGENT_NAME python scripts/advanced.py list-webhooks --agent AGENT_NAME ``` ## Tips - Use environments to separate development, staging, and production - Webhooks enable external processing and integrations - Continuous testing helps maintain conversation quality


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