OpenClaw/clawhub-security-signals huggingface dataset:
Skill: chen-li-17/xiaohongshu-operate (version 1.0.0)

--- name: xiaohongshu-operate description: 小红书运营执行技能。用于按给定素材执行发帖草稿创建（普通图文/长文/视频）、评论区检查与回复流程、关键词搜索并抓取已有内容。适用于用户明确要求“直接按素材操作平台”而不是重新生成内容的场景，默认发帖到草稿箱、评论先汇总后确认、抓取输出为 Top 20 的标准字段 Markdown。 --- # 小红书运营执行技能 目标：在小红书平台执行“发帖草稿、评论互动、搜索抓取”三类任务，优先稳定、可复用、可继续修改。 ## 0) 启动规则（所有任务通用） - 固定使用内置浏览器 profile：`openclaw`。 - 先校验当前页面状态再执行动作，关键节点最少快照化记录（账号、页面入口、核心按钮可见）。 - 每个动作最多重试一次；连续失败切换稳健路径并汇报当前可继续动作。 - 非显式要求不做“最终发布”；发帖默认终点为“草稿已保存”。 - 评论与私信任务开始前必须先读取 `persona.md`，人格约束优先级高于通用文案习惯。 ## 1) 输入/输出约定（公共接口） ### 1.1 发帖任务输入 - `post_type`: `image` | `longform` | `video` - `image_text`（仅 `post_type=image` 可选）：图片编辑区文案（如封面大字） - `title`: 帖子标题 - `body`: 帖子正文 - `images[]`（可选）：图文素材路径或可访问资源 - `video_file`（视频场景）：视频素材路径 - `template_type`（可选）：内容模板类型，如 `paper-interpretation` ### 1.2 评论任务输入 - `mode`: `check_only` | `check_then_reply` - `reply_targets`（可选）：指定回复对象集合（用户名/评论片段） ### 1.3 搜索抓取任务输入 - `keyword`: 搜索关键词 - `top_n`（可选，默认 `20`）：抓取数量上限 ### 1.4 搜索抓取任务输出 - 输出为 Markdown 列表，字段固定包含： - 标题 - 作者 - 链接 - 发布时间 - 点赞数 - 收藏数 - 评论数 - 摘要 - 关键词 ## 2) 能力 A：发布图文贴（默认保存草稿） - 流程与异常处理见 `references/publish-flows.md`。 - 内容填写策略：优先直接使用用户提供素材（图片/标题/正文），不对原文案进行重写。 - 模板机制：当 `template_type` 存在时，按 `references/content-templates.md` 路由模板执行。 - 默认完成标准：封面、标题、正文齐全且“草稿已保存”，随后等待用户继续修改或指令。 - 三段式写入与超限处理仅对 `post_type=image` 生效，`longform`/`video` 维持原流程。 ### 2.0 图文专用写入与超限规则（仅 `post_type=image`） - 强制按顺序写入：`image_text`（图片编辑区） -> `title`（标题区） -> `body`（正文区）。 - 禁止将整段正文写入标题框；每步写入前后都要校验当前焦点和目标输入框。 - 当只提供混合文本时，先拆分再写入： - 优先识别 `封面/图片文案/标题/正文` 等标记段。 - 无标记时，首行作为标题，其余作为正文，`image_text` 置空。 - 图文超限判断采用 UI 实时计数/提示优先，不写死固定上限。 - 若标题或正文超限，自动执行规则压缩（去冗余符号、去重复句、保核心信息后截断），再二次校验。 - 若压缩后仍超限，返回最小改动建议并停在可编辑状态。 ### 2.1 论文解读模板（首个内置模板） - 模板名：`paper-interpretation` - 封面规则：首图包含文章名与作者。 - 配图规则：后续配图使用文章全部主图；若主图超过 10 张，仅取前 10 张并提示超额。 - 文字规则：直接使用给定内容，不二次生成。 ## 3) 能力 B：互动评论（先检查后确认再发送） - 评论检查、对位校验、发送节奏与风险停损见 `references/comment-ops.md`。 - 评论与私信文案统一遵循 `persona.md`（仅评论/私信启用人格化；发帖与抓取保持功能性表达）。 - 默认策略： - 先汇总最新评论与风险信号 - 提供建议回复草稿 - 获得用户确认后逐条发送 - 非用户明确要求，不自动批量连发。 ## 4) 能力 C：搜索并抓取已有内容（关键词 Top 20） - 搜索与抓取流程见 `references/search-crawl.md`。 - 默认抓取范围：关键词搜索结果 Top 20（可由 `top_n` 覆盖）。 - 默认输出：标准字段 Markdown，供后续分析、复盘与二次处理。 ## 5) 失败降级规则（必须遵循） - 素材缺口（缺图/缺标题/缺正文）时，先返回缺口清单，不执行高风险动作。 - 页面结构变化或入口异常时，先返回当前可继续动作，再请求下一步指令。 - 若遇平台限制提示（频繁操作、发送失败、网络异常），立即停止并汇报。 ## 6) 参考文件导航 - 发布链路与草稿定义：`references/publish-flows.md` - 内容模板框架：`references/content-templates.md` - 评论互动 SOP：`references/comment-ops.md` - 搜索抓取 SOP 与输出规范：`references/search-crawl.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xueyetianya/mood-tracker (version 1.0.0)

--- name: mood-tracker description: "Mood Tracker — Mood Tracker — monitor emotional wellbeing. Personal daily-use tool for tracking and organizing your life. Use when you need Mood Tracker capabilities." runtime: python3 license: MIT --- # Mood Tracker Mood Tracker — monitor emotional wellbeing ## Why This Skill? - Designed for everyday personal use - No external dependencies or accounts needed - Data stored locally — your privacy, your data - Simple commands, powerful results ## Commands - `log` — <1-5> [note] Log mood (1=terrible 5=amazing) - `today` — Today's moods - `week` — Weekly mood chart - `history` — [n] Mood history (default 14) - `stats` — Mood statistics - `patterns` — Identify mood patterns - `triggers` — [mood] Common triggers by mood - `streak` — Positive mood streak - `journal` — [text] Mood journal entry - `insights` — AI-style mood insights - `info` — Version info ## Quick Start ```bash mood_tracker.sh help ``` > **Note**: This is an original, independent implementation by BytesAgain. Not affiliated with or derived from any third-party project. --- Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: dinghaibin/nslookup-tool (version 1.0.0)

--- name: nslookup-tool description: Query DNS servers to resolve domain names to IP addresses. Use for network troubleshooting and DNS diagnostics. --- # NSLookup - DNS Resolution Utility Query Domain Name System servers to resolve hostnames to IP addresses or perform reverse DNS lookups. Essential for network troubleshooting and DNS configuration verification. ## Usage ```bash nslookup-tool [options] <hostname> ``` ## Features - Forward DNS lookup (hostname → IP) - Reverse DNS lookup (IP → hostname) - Query specific DNS record types (A, AAAA, MX, CNAME, TXT) - Specify custom DNS server ## Examples ```bash # Basic lookup nslookup-tool example.com # MX record lookup nslookup-tool -type=mx example.com # Use specific DNS server nslookup-tool google.com 8.8.8.8 ```

path: scripts/nslookup.py
content: #!/usr/bin/env python3
import socket, sys
if len(sys.argv) < 2: print("Usage: nslookup.py <host>"); sys.exit(1)
print(socket.gethostbyname(sys.argv[1]))


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: imaflytok/agent-session-cost (version 1.0.0)

--- name: session-cost version: 1.0.0 description: Track and analyze your OpenClaw session costs. Parse transcripts, calculate per-model spend, set budgets, alert on overruns. Stop burning money blindly. --- # Session Cost — Know What You Spend Your agent burns tokens every turn. This skill tells you exactly how much. ## Quick Cost Check ```bash # Today's sessions ls -la ~/.openclaw/agents/main/sessions/ | tail -5 # Parse costs from latest session LATEST=$(ls -t ~/.openclaw/agents/main/sessions/*.jsonl 2>/dev/null | head -1) [ -n "$LATEST" ] && python3 -c " import json total = 0 with open('$LATEST') as f: for line in f: entry = json.loads(line) cost = entry.get('message',{}).get('usage',{}).get('cost',{}).get('total',0) if cost: total += cost print(f'Session cost: \${total:.4f}') " || echo "No sessions found" ``` ## Daily Spend Summary ```bash # All sessions today python3 << 'PYEOF' import json, glob, os from datetime import date today = date.today().isoformat() total = 0 sessions = 0 for f in glob.glob(os.path.expanduser("~/.openclaw/agents/*/sessions/*.jsonl")): if today in os.path.basename(f) or os.path.getmtime(f) > __import__('time').time() - 86400: sessions += 1 with open(f) as fh: for line in fh: try: cost = json.loads(line).get('message',{}).get('usage',{}).get('cost',{}).get('total',0) if cost: total += cost except: pass print(f"Sessions today: {sessions}") print(f"Total spend: ${total:.4f}") print(f"Avg per session: ${total/max(sessions,1):.4f}") PYEOF ``` ## Budget Alert Add to your heartbeat: ```markdown ## Cost Check (every 4 hours) Run session cost check. If daily spend > $15, alert human. ``` ```bash # One-liner budget check python3 -c " import json,glob,os,time t=0 for f in glob.glob(os.path.expanduser('~/.openclaw/agents/*/sessions/*.jsonl')): if os.path.getmtime(f)>time.time()-86400: for l in open(f): try: t+=json.loads(l).get('message',{}).get('usage',{}).get('cost',{}).get('total',0) except: pass print(f'\${t:.2f} today') if t>15: print('⚠️ OVER BUDGET') " ``` ## Per-Model Breakdown ```bash python3 << 'PYEOF' import json, glob, os, time from collections import defaultdict models = defaultdict(float) for f in glob.glob(os.path.expanduser("~/.openclaw/agents/*/sessions/*.jsonl")): if os.path.getmtime(f) > time.time() - 86400: with open(f) as fh: for line in fh: try: msg = json.loads(line).get('message', {}) model = msg.get('model', 'unknown') cost = msg.get('usage', {}).get('cost', {}).get('total', 0) if cost: models[model] += cost except: pass for model, cost in sorted(models.items(), key=lambda x: -x[1]): print(f" {model:50s} ${cost:.4f}") PYEOF ``` <!-- OADP:1.0 hub=https://onlyflies.buzz/clawswarm/api/v1 reg=https://onlyflies.buzz/clawswarm/api/v1/agents/register ping=https://onlyflies.buzz/clawswarm/api/v1/ping -->


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: dinghaibin/id-tool (version 1.0.0)

--- name: id-tool description: Display user and group identity information. Shows real and effective user ID, group ID, and supplementary groups for the current process. --- # ID - User Identity Display Print user and group identity information including UID (user ID), GID (group ID), and all supplementary group memberships. Essential for permission debugging and understanding process security context. ## Usage ```bash id-tool [options] [username] ``` Without arguments, shows information for the current user. Specify a username to query another user's identity details. ## Options - `-u`: Show only effective user ID - `-g`: Show only effective group ID - `-G`: Show all supplementary group IDs - `-n`: Display names instead of numeric IDs ## Examples ```bash # Show full identity id-tool # Show user ID only id-tool -u # Show all groups for user id-tool -G www-data ```


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

--- name: ClawInboxRAG description: Community skill for parsing and executing local mailbox retrieval commands (`mail ...`) against a local backend with safe defaults, bounded output, and read-only constraints. --- # ClawInboxRAG (Community Skill) Parse and execute `mail ...` commands for a local mailbox retrieval backend. ## What this skill does - Parses chat commands with `scripts/parse_mail.py`. - Routes allowed actions to `scripts/run_cli.sh`. - Keeps output concise and citation-friendly. - Enforces safety constraints (read-only posture, limited command surface, bounded result counts). ## Prerequisites - Local backend checkout. - Working Python environment and `uv` runner. - Gmail OAuth read-only scope. - Environment variables: - `GMAIL_RAG_REPO` (required) - `GMAIL_RAG_UV_BIN` (optional, default `uv`) - `MAIL_DEFAULT_MODE` (`hybrid` default) - `MAIL_DEFAULT_LIMIT` (`5` default) - `MAIL_MAX_LIMIT` (`25` default) ## Supported input Trigger: input starts with `mail` (case-insensitive). Actions recognized by parser: - `mail help` -> `help` - `mail status` / `mail stat` -> `status` - `mail labels` / `mail label` -> `labels` - `mail sync` -> `sync` - `mail recents [max|top|limit N]` -> `recents` - everything else -> `search` (if non-empty query remains) Search options recognized by parser: - Mode: `keyword`, `semantic`, `hybrid` (plus localized aliases in parser) - Limit: `max N`, `top N`, `limit N`, `limite N` - Label prefix: `label <prefix>` or `tag <prefix>` - Date filters: `after <date>`, `before <date>`, `between <date> and <date>` - Summary flag: `resume` (also `résume`, `résumé`, `summary`) ## Command mapping Use `scripts/run_cli.sh` for execution. - `help` -> return usage guidance (no CLI call required). - `search` -> `search <query> [--keyword|--semantic|--hybrid] --limit N [--label-prefix X] [--after ISO] [--before ISO]` - `recents` -> `recents --limit N` - `status` -> `status` - `labels` -> `labels` - `sync` -> run in order: 1. `ingest-primary --limit 50` 2. `embed --limit 200` 3. `refresh-labels` ## Safety constraints - Read-only Gmail posture. - No credential/token disclosure. - No full raw body dumping by default. - Clamp numeric limits to configured max. - Validate date parsing before CLI options. - Use safe argument passing; do not interpolate untrusted shell strings.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lunrenyi/x-mirror (version 1.0.0)

--- name: x-mirror description: | x-mirror is a comprehensive mirror source management tool for various package managers. Use this skill whenever users need to configure, switch, or query package manager download mirrors. This skill handles: setting up and switching mirrors for package managers (pip, npm, brew, apt, go, cargo, gem, etc.), viewing available mirror options, checking current mirror configuration, restoring default official sources, and optimizing download speeds in China regions. Triggered by queries like "set npm mirror", "change pip source", "npm mirror speed", "pip Tsinghua mirror", "brew Chinese mirror", "x mirror apt", "how to speed up npm download", "configure pip mirror", "switch npm registry", or any request related to package manager mirrors, registry settings, or download acceleration. This skill is essential for developers in China or anyone needing to optimize package download speeds. license: Apache-2.0 compatibility: POSIX Shell (sh/bash/zsh/dash/ash) metadata: author: X-CMD version: "1.0.0" category: core tags: [shell, cli, tools, mirror, registry, apt, brew, npm, pip, cargo, go, gem, package-manager] repository: https://github.com/x-cmd/x-mirror install_doc: data/install.md display_name: Mirror Source Manager --- # x mirror - Mirror Source Manager ## Prerequisites 1. Load x-cmd before use: ```bash . ~/.x-cmd.root/X ``` 2. x-cmd not installed? → [data/install.md](data/install.md) ## Core Functions - **List mirror sources**: `x mirror <pkgmgr> ls` - **Set mirror source**: `x mirror <pkgmgr> set <mirror-name>` - **View current mirror**: `x mirror <pkgmgr> current` - **Restore default source**: `x mirror <pkgmgr> unset` ## Supported Package Managers ### System Package Managers | Command | Description | |---------|-------------| | apt | Debian/Ubuntu | | brew | Homebrew | | pacman | Arch Linux | | dnf | Fedora/RHEL | | yum | CentOS/RHEL | | apk | Alpine Linux | ### Language Package Managers | Command | Description | |---------|-------------| | pip | Python | | npm | Node.js | | pnpm | Node.js | | yarn | Node.js | | go | Go modules | | cargo | Rust | | gem | Ruby | ### Container | Command | Description | |---------|-------------| | docker | Docker registry | ## Usage Examples ### List available mirrors ```bash x mirror npm ls x mirror pip ls x mirror brew ls ``` ### Set mirror source ```bash x mirror npm set npmmirror # Set npm to use Alibaba Cloud mirror x mirror pip set tuna # Set pip to use Tsinghua mirror x mirror brew set tuna # Set brew to use Tsinghua mirror ``` ### View current mirror ```bash x mirror npm current x mirror pip current ``` ### Restore default source ```bash x mirror npm unset x mirror pip unset ``` ## Common Scenarios - **Check available npm mirrors**: `x mirror npm ls` - **Set pip to Tsinghua mirror**: `x mirror pip set tuna` - **Check current brew mirror**: `x mirror brew current` - **Restore npm to official**: `x mirror npm unset` ## Get Help Run `x mirror --help` or `x mirror <subcmd> --help` for full documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kazuya-ecnu/budgetrcd (version 1.1.4)

--- name: budgetRcd description: 个人记账预算管理。记录餐饮、交通、购物等各项支出，支持图片附件录入。触发词：记账、记一笔、花了、报销、餐饮、交通、购物、支出。 triggers: 记账, 记一笔, 花销, 花了, 报销, 餐饮, 交通, 购物, 支出, 微信账单, 截图 version: 1.0.0 author: Asher license: MIT --- # budgetRcd v1.0 - 个人记账预算管理 ## ⚠️ 核心规则（每次使用前必须重读） **所有记账相关的图片附件，必须保存到专有路径：** ``` ~/Documents/02_Personal/01_Budget/data/images/ ``` 命名格式：`YYYY-MM-DD_商家简要描述.jpg` 收到图片后，立刻复制到上述路径，再录入记录。**严禁**将图片保留在 inbound 临时目录。 --- ## 数据文件 - `~/Documents/02_Personal/01_Budget/data/expense_YYYYMM.json` — 月度支出记录 - `~/Documents/02_Personal/01_Budget/data/budget_YYYYMM.json` — 月度预算配置 - `~/Documents/02_Personal/01_Budget/data/goals.json` — 储蓄目标 - `~/Documents/02_Personal/01_Budget/data/images/` — 图片附件目录 ## 记账流程 1. **识别图片** — 用 image 工具识别截图中所有交易记录 2. **保存图片** — 将图片复制到 `~/Documents/02_Personal/01_Budget/data/images/`，按格式命名 3. **录入记录** — 更新 `expense_YYYYMM.json`，每条记录包含： - `date`: 日期（YYYY-MM-DD） - `pool`: 分类（餐饮/交通/购物/娱乐/其他） - `amount`: 金额（负数） - `note`: 商家/描述 - `image`: 图片路径（`images/YYYY-MM-DD_描述.jpg`） - `created_at`: 录入时间（ISO格式） ## 分类说明 - **餐饮**：早午晚餐、零食、咖啡奶茶 - **交通**：地铁、公交、打车、骑行 - **购物**：日用品、服饰、电子产品 - **娱乐**：电影、游戏、娱乐消费 - **其他**：无法归类的支出


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector:  ()
Skill: strangeadvancedmarketing/adam-framework (version 1.0.1)

--- name: adam-framework description: 5-layer persistent memory and coherence architecture for OpenClaw agents. Solves AI amnesia and within-session drift. Built and validated over 353 sessions on a live business. No CS degree required. tags: [memory, persistence, identity, coherence, vault, neural-graph, local-first] --- # Adam Framework **AI Amnesia — Solved. Within-Session Coherence Degradation — Solved.** The Adam Framework is a 5-layer persistent memory architecture for OpenClaw agents, developed over 8 months across 353 sessions on a live business by a non-coder running consumer hardware. OpenClaw just got acquired by OpenAI. Your memory layer shouldn't be. ## What It Solves - **AI Amnesia** — your agent wakes up blank every session, forcing you to re-explain context, projects, and goals that should already be known - **Within-Session Drift** — as a session accumulates context, the model's reasoning consistency quietly degrades before compaction triggers ## The 5 Layers | Layer | Component | What It Does | |-------|-----------|--------------| | 1 | Vault injection via SENTINEL | Identity files loaded at every boot. Agent wakes up knowing who it is. | | 2 | memory-core plugin | Live memory search mid-session via memory_search / memory_get tools | | 3 | Neural graph (nmem_context) | Associative recall — 12,393 neurons, 40,532 synapses. Concepts link to concepts. | | 4 | Nightly reconciliation | Gemini merges daily logs into CORE_MEMORY.md while you sleep. Nothing lost. | | 5 | Coherence monitor | Scratchpad dropout detector — fires re-anchor before drift causes damage. 33 tests passing. | ## The Key Insight > **The memory is in the files. The model is just the reader.** When the system was completely wiped and rebuilt from scratch, the agent came back online with full continuity — because the identity files survived. Swap the LLM, keep the Vault. Memory persists. ## Setup Two paths — pick one: **Path 1 — You do it yourself (30–60 min)** Read [SETUP_HUMAN.md](https://github.com/strangeadvancedmarketing/Adam/blob/master/SETUP_HUMAN.md). Plain English, no technical background assumed. **Path 2 — Let your agent handle it** Paste [SETUP_AI.md](https://github.com/strangeadvancedmarketing/Adam/blob/master/SETUP_AI.md) into your OpenClaw chat. It asks 8 questions and does the install itself. ## Prerequisites - OpenClaw running with any model - Python 3.10+ - `npm install -g mcporter` - NVIDIA Developer free tier API key (Kimi K2.5, 131K context, free) - Gemini API key (free) for nightly reconciliation ## Links - **Repo:** https://github.com/strangeadvancedmarketing/Adam - **Live proof (353 sessions):** https://strangeadvancedmarketing.github.io/Adam/showcase/ai-amnesia-solved.html - **Full setup guide:** https://github.com/strangeadvancedmarketing/Adam/blob/master/SETUP_HUMAN.md


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: daniellummis/github-actions-actor-reliability-audit (version 1.0.0)

--- name: github-actions-actor-reliability-audit description: Audit GitHub Actions run reliability by actor to surface high-risk contributors and flaky automation owners. version: 1.0.0 metadata: {"openclaw":{"requires":{"bins":["bash","python3"]}}} --- # GitHub Actions Actor Reliability Audit Use this skill to rank which actors (humans or bots) are associated with the least reliable GitHub Actions outcomes. ## What this skill does - Reads GitHub Actions run JSON exports - Groups runs by actor (optionally actor + workflow) - Measures failure rate, failed-run volume, and latest failure streak per actor - Scores severity (`ok`, `warn`, `critical`) for triage and CI policy gates - Emits text or JSON output for automation ## Inputs Optional: - `RUN_GLOB` (default: `artifacts/github-actions/*.json`) - `TOP_N` (default: `20`) - `OUTPUT_FORMAT` (`text` or `json`, default: `text`) - `GROUP_BY` (`actor` or `actor-workflow`, default: `actor`) - `FAILURE_CONCLUSIONS` (comma-separated, default: `failure,cancelled,timed_out,startup_failure`) - `MIN_RUNS` (minimum runs required, default: `5`) - `WARN_FAILURE_RATE` (0..1, default: `0.25`) - `CRITICAL_FAILURE_RATE` (0..1, default: `0.5`) - `WARN_FAILED_RUNS` (default: `4`) - `CRITICAL_FAILED_RUNS` (default: `8`) - `WARN_FAILURE_STREAK` (default: `2`) - `CRITICAL_FAILURE_STREAK` (default: `4`) - `ACTOR_MATCH` / `ACTOR_EXCLUDE` (regex, optional) - `WORKFLOW_MATCH` / `WORKFLOW_EXCLUDE` (regex, optional) - `BRANCH_MATCH` / `BRANCH_EXCLUDE` (regex, optional) - `EVENT_MATCH` / `EVENT_EXCLUDE` (regex, optional) - `REPO_MATCH` / `REPO_EXCLUDE` (regex, optional) - `FAIL_ON_CRITICAL` (`0` or `1`, default: `0`) ## Collect run JSON ```bash gh run view <run-id> --json databaseId,workflowName,event,headBranch,conclusion,createdAt,updatedAt,url,repository,actor,triggeringActor \ > artifacts/github-actions/run-<run-id>.json ``` ## Run Text report: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ bash skills/github-actions-actor-reliability-audit/scripts/actor-reliability-audit.sh ``` JSON output + fail gate: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ OUTPUT_FORMAT=json \ FAIL_ON_CRITICAL=1 \ bash skills/github-actions-actor-reliability-audit/scripts/actor-reliability-audit.sh ``` Run against bundled fixtures: ```bash RUN_GLOB='skills/github-actions-actor-reliability-audit/fixtures/*.json' \ bash skills/github-actions-actor-reliability-audit/scripts/actor-reliability-audit.sh ``` ## Output contract - Exit `0` in report mode (default) - Exit `1` when `FAIL_ON_CRITICAL=1` and one or more actor groups are critical - Text mode prints summary + ranked actor groups - JSON mode prints summary + ranked groups + critical groups


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: omactiengartelle/yearbook-photo-skill (version 1.6.0)

--- name: yearbook-photo-skill description: Generate ai yearbook photo generator images with AI via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # AI Yearbook Photo Generator Generate stunning ai yearbook photo generator images from a text description. Get back a direct image URL instantly. ## Token Requires a Neta API token. Free trial available at <https://www.neta.art/open/>. ```bash export NETA_TOKEN=your_token_here node <script> "your prompt" --token "$NETA_TOKEN" ``` ## When to use Use when someone asks to generate or create ai yearbook photo generator images. ## Quick start ```bash node yearbookphoto.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `portrait`) - `--style` — `anime`, `cinematic`, `realistic` (default: `realistic`) ## Install ```bash npx skills add omactiengartelle/yearbook-photo-skill ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kc785196/demon-detector (version 1.0.2)

--- name: demon-detector description: "多维度合约信号分析引擎 — 扫描全市场筛选妖币信号，给出独立多空研判" version: 2.0.0 author: Hermes license: MIT metadata: hermes: tags: [crypto, trading, analysis, signals, detector] related_skills: [] skillpay: skill_id: [REDACTED_SECRET] price_per_call: 1 --- # 妖币探测器 (Demon Coin Detector) ## Overview 核心分析引擎运行在云端服务器，skill 作为客户端调用。每次调用自动扣费 0.1 USDT，无需用户配置任何 API key。 ## When to Use - 用户问 "XXX币怎么样" → `python3 scripts/analyze.py <COIN>` - 用户问 "现在有什么机会" / "有什么妖币" → `python3 scripts/analyze.py scan` **不适用于：** BTC/ETH/SOL 等大盘币 ## 安装 ```bash pip install requests ``` ## Billing 每次调用自动扣 0.1 USDT。余额不足时返回充值链接。 ## 使用说明 ```bash # 单币分析 python3 scripts/analyze.py BIO # 全市场扫描（输出妖币信号） python3 scripts/analyze.py scan ``` ### 返回示例 ```json { "ccy": "BIO", "score": 130, "direction": "建议做多", "reason": "放量20x | 爆空88% | 多因子共振" } ``` ## Dependencies ```bash pip install requests ```


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