Results for “any-run”

Security audit orchestrator for agent skills — scans for prompt injection, data exfiltration, credential theft, supply chain risks, and instruction hierarchy violations before any skill is installed, created, improved, or read from a GitHub repo. Load when creating skills from external sources, when improve-skills reads from GitHub repos, when research-skill fetches community SKILL.md files, when a user installs a third-party skill, or when the user asks to audit skill security, scan for injection, check if a skill is safe, scan all skills, or run a security sweep. Orchestrates all secure-* skills in sequence. Content is SAFE only if ALL secure-* skills return SAFE. 36% of community skills contain flaws (Snyk ToxicSkills 2026). This skill is the first line of defense.

Run a structured discovery interview and produce a complete, developer-ready Product Requirements Document. Load when the user asks to write a PRD, create product requirements, document a feature, define user stories with acceptance criteria, or turn a rough idea into a formal product requirements document. Also triggers on "document this feature", "write requirements for", "create a one-pager", "turn this into a PRD", "I need a PRD for", or any request to produce a structured product document for stakeholder alignment or engineering handoff. Supports Full PRD, Lean PRD, and One-Pager formats. Note: for executable feature specifications (FRs, NFRs, ACs as Given/When/Then consumable by AI coding agents), route to `feature-spec` instead — PRDs frame the product, feature-specs encode the implementable contract.

Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk-of-bias assessment, meta-analysis, APA 7.0 report compilation, editorial and devil's-advocate review, ethics review, and post-research literature monitoring. Use when the request mentions research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, or 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題. Part of the AlterLab Academic Skills suite.

Compact the current conversation into a handoff document for another agent to pick up. Save to a user-configured location (OS temp, home folder, or per-project .handoff/), redact secrets before write, suggest skills for the next session, and auto-load the latest handoff on the next SessionStart. First-run setup asks where to save so the project folder never gets cluttered. Use when the user says 'hand this off', 'handoff doc', 'summarize this for a new session', 'compact this conversation', 'I'm ending this session', 'pick this up later', or any variation signaling intent to pass work to a fresh agent. Also trigger on implicit signals: the user announcing they're switching machines, ending the day mid-task, or context is growing long without a natural stopping point.

>- Skill Refiner, the eval-guided improvement loop for SKILL.md files. Runs the bootstrap, score, propose, apply, and status cycle as a thin wrapper over the published @intentsolutions/refiner CLI, proposing safe, minimal, bounded SKILL.md edits and accepting an edit only when a held-out eval score strictly improves with no regression on any other case. Ships a 3-layer cost-tiered hook architecture (sinker, line, hook) that gates skill quality at edit time, end of turn, and commit time. Use when improving an existing skill, refining a SKILL.md against measured behavior, bootstrapping an eval set for a skill, or gating skill edits before they ship. Trigger with "/j-rig", "refine this skill", "bootstrap an eval set", "propose a skill edit", "promote the candidate", or "skill refiner status".

Generates a structured developer-facing markdown document that teaches how a specific feature, module, or piece of functionality works in the codebase. Use this skill whenever the user runs `/teach`, asks "how does X work", "explain the auth flow", "document how payments work", "walk me through the webhook system", or any request to understand and document internal codebase functionality. Also trigger when the user wants to produce onboarding docs, feature walkthroughs, or "how it works" references for developers. Always use this skill even if the user only gives a vague description — use the provided context or file hints to locate the relevant code yourself and build the teaching doc from there. This skill writes for developers reading the codebase — for end-user/customer-facing help docs, use the eli5-features skill instead.

Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.

Mechanics for building and managing pollers — subprocess scripts that check external services on a schedule and emit events when something has changed. Use when authoring a new poller (a `pollers.json` manifest plus a script in any language), debugging why a poller isn't firing, or extending an existing one. Pollers run on cron, emit JSONL events when there's something to report, and stay silent otherwise (silence-as-filter). The framework discovers `<home>/skills/<name>/pollers.json` files at startup and via `reload_pollers`; each emitted event becomes a fresh turn on a `poller:<name>` synthetic channel. Companion to the `world-scanning` skill, which catalogs *what's worth polling*. Distinct from `async-tasks` (one-shot wake-up via bash_async, not recurring) and from in-process scheduler callables (saga-consolidate, oauth-usage-poll — those mutate mimir-internal state and aren't subprocess-isolated).

Reconstruct a product flow, then audit how usable it is — whether it makes sense, where users get confused or stuck, whether the copy and instructions are clear, and where friction causes drop-off. Produces a prioritized, severity-weighted findings report. Works from three inputs: frontend code in a repo, a live running app driven via a browser, or a written flow / spec / screenshots. Use this skill whenever the user asks for a UX review, usability audit, "is this flow confusing", "does this signup/onboarding/checkout make sense", "why are people dropping off", "audit this form", "where's the friction", or hands over a flow and asks whether it's user-friendly. Trigger even on casual phrasings ("is my checkout any good?") — it imposes the structure ad-hoc UX opinions miss. Audits flow usability and friction; for visual polish and interface quality the impeccable audit/critique skills apply instead.

This skill should be used when the user asks about crypto prices, market data, K-line charts, funding rates, open interest, long/short ratios, whale orders, liquidation data, crypto news, newsflash, Twitter crypto tweets, trending coins, airdrops, drop radar, airdrop research, project analysis, exchange listings, stock quotes, treasury holdings, or any crypto market query. Also use when user asks about configuring or checking AiCoin API key. Use when user says: 'BTC price', 'check price', 'show K-line', 'funding rate', 'open interest', 'whale orders', 'long/short ratio', 'crypto news', 'newsflash', 'ETF', '监管', '政策', 'trending coins', 'airdrop', 'drop radar', '查行情', '看价格', '大饼多少钱', 'K线', '资金费率', '多空比', '鲸鱼单', '新闻', '快讯', '新闻快讯', '热门币', '空投', '空投项目', '空投机会', '空投研报', '项目分析', '项目详情', '上了哪些交易所', '推特', 'Twitter', 'liquidation map', '配置AiCoin key', 'AiCoin API key', 'AiCoin key安全吗'. Covers 200+ exchanges with real-time data. MUST run node scripts to fetch real data. NEVER generate fake prices or hallucinate market

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST

Translates stakeholder feature requests into validated outcome statements before any work is committed. Use this skill — proactively and without waiting to be asked — whenever a stakeholder, exec, or customer arrives with a pre-packaged solution: "we need a dashboard", "add a Slack notification", "build an export feature", "create a report", "let's add a filter", "can we just add X". Also triggers for: "how do I push back on this request", "what outcome does this feature solve", "outcome vs output", "outcomes not features", "what are we really trying to achieve", "we're being a feature factory", "I need to reframe this as a problem", "the stakeholder is pushing a specific solution", "discovery before delivery", "assumption testing", "translate this request into an outcome", "ship outcomes not features". Runs the 'One Framework. Four Questions.' protocol (Liatti + Cagan + Torres): Behavior Change → Assumption Test → Cheapest Test → Success Metric. Produces an Outcome Brief with embedded AI prompts ready to pas

Turn a work-items.md file (produced by /plan-work-items) into Linear issues, one per slice, in a single target Linear team. Use when you want to publish work items as Linear issues, create implementation tickets to track in Linear, or push a broken-down plan into a Linear team. Requires a configured Linear MCP server and a target team. Reads the team's real workflow states, labels, Projects, and members and resolves every option against them before creating anything; defaults each issue to the team's initial state, unassigned, uncategorized, with no parent or Project unless you ask. Links within-file `Depends on` relationships as native Linear "blocked by" relations and annotates the source file so re-runs resume cleanly. Does not produce the work-items file itself — use plan-work-items first. Does not post to Jira — use work-items-to-jira. Does not post to GitHub — use work-items-to-issues.

Build editorial/magazine/report webpages on a GENUINE Müller-Brockmann modular grid (International Typographic Style), not a decorative one. Encodes the discipline — columns + modules + baseline, grotesque type, flush-left ragged-right, restrained black/white/red — AND the front-end engineering that makes the grid load-bearing: one CSS-variable source of truth, a grid-toggle overlay that lives in the SAME content box as the content, subgrid 'bands' so every element snaps to a column line, an 8px baseline lock, and runtime optical alignment that puts display type's ink (not its box) on the line. Ships a scaffold generator and a Puppeteer harness that proves 0px adherence. Use when: building any editorial, magazine, report, or longform page that must read as rigorously grid-aligned, Swiss, International Typographic Style, or 'Müller-Brockmann'. Triggers: magazine spread, grid system, Swiss design, editorial layout, show the grid, grid overlay toggle, baseline grid, modular grid, 12-column layout, 瑞士网格, 版面网格.

Create, set up, or update the personal context portfolio: structured markdown files describing who you are, how you work, your teams, and your tool/ADO configuration. Runs the interview workflow for first-time setup and targeted edits for updates. Trigger this skill when the user asks to: set up their context, create or update their context portfolio, "create my IQ", "set up my IQ", edit their profile, add/remove a stakeholder, update ADO config, change team info, update pillars, or set up any plugin configuration. Trigger when another skill fails to find context (missing files or TODO markers) and needs context populated. Also trigger when the user mentions a context change in passing (e.g., "my manager changed", "we added someone to the team") to offer a context file update. Do NOT trigger for read-only questions like "who's on my team?" or "what's my ADO config?". Those are answered directly from the context files referenced in the loaded custom instructions; no skill is needed.