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Found 1,113 Skills
Use when Obsidian note automation runs in cron/headless environments and obsidian-cli emits URI failure signatures (for example, `Failed to execute Obsidian URI`) that may not set a non-zero exit code. Detect false-success cases, fallback to deterministic markdown file writes, and record traceable fallback paths in run artifacts.
Use DBML as the standard format for database schema documentation. Apply this whenever creating, updating, reviewing, or repairing database docs, ERDs, schema diagrams, table inventories, migration summaries, Doctrine migration changes, SQL schema docs, ORM model docs, or CI schema drift failures. Prefer db/schema.dbml over Mermaid, Prisma schema, ad hoc Markdown tables, or prose-only database documentation unless the user explicitly requests another format.
Write Substack articles in Michael Hanko's exact voice. Enforces strict styling rules (no em dashes, no markdown tables) and captures his irreverent, self-deprecating, recovery-infused tone.
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Use when the user asks to threat model a codebase or path, enumerate threats or abuse paths, or perform AppSec threat modeling. Do NOT use for general architecture summaries, code review, security best practices (use security-best-practices), or non-security design work.
Audit a WordPress plugin's REST surface and produce a standardized audit document proposing Abilities API registrations. Produces a markdown doc with a YAML schema and prose sections that humans and agents can both consume when planning a registration rollout. Works on any WP plugin.
DPoP-signed (RFC 9449) authenticated calls to Alien-aware services. Discover any Alien-aware service's manifest at /.well-known/alien-agent-id.json, render its operations as actionable markdown, emit DPoP headers for one request, or one-shot a signed HTTP call with the agent's identity attached. Use when the user gives you a URL on an Alien-aware service (alien-api.com, alien.org, agent-sso.*), asks to call an Alien-aware endpoint, asks what an Alien-aware service can do, or mentions DPoP, agent-bound access tokens, or `cnf.jkt`.
Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Two responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, and (2) run ruff against any Python files Claude has just generated or edited. Stops at "the touched files pass `ruff check`." TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run on the touched files; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and the project has no `ruff.toml` at its root yet — drop the bundled template; (3) the user asks about lint, format, docstring style, or reaches for `black` / `isort` / `flake8` / `pydocstyle` (redirect to ruff — the stack's canonical linter, owned by `data-science-python-stack` Tier 1). SKIP when: the project is non-Python; the only edits in this turn are to Markdown / TOML / JSON / YAML; the file lives in a third-party vendored directory the user doesn't own. HOW TO USE: run ruff manually on the files you just touched — do not configure a PostToolUse hook for this. **Read the "Stop conditions" block and emit the Pre-flight checklist as visible text in your response — both are mandatory before running ruff.**
Inspect an existing memory corpus (wiki substrate) and align it to this repo's Obsidian-friendly note-graph conventions. Use this when the user wants to import, normalize, retrofit, or clean up existing memory, notes folder, vault, docs tree, or mixed markdown knowledge base. In monorepos, also use it to align relevant AGENTS.md and CLAUDE.md files. Excludes goals/ from normalization. Not for routine wiki maintenance; use /loam::linting-memory for that.
Uses the user's own paid TikHub API to research public social-media creators, accounts, posts, videos, comments, transcripts, topics, and performance data, then exports traceable JSON, Markdown, CSV, or Excel assets. Activate this Skill whenever the user mentions terms like Shengjiang Research, cross-platform research, influencer research, benchmark accounts, post scraping, comment scraping, transcripts, TikHub, Douyin, Xiaohongshu, WeChat Channels, TikTok, YouTube, Bilibili, Weibo, Instagram, X, Reddit, Zhihu, or public social media data monitoring. Always disclose API charges and show a request-count and cost preview before conducting paid batch collection.
Universal document converter for transforming Markdown to PDF, DOCX, HTML, LaTeX, and 40+ other formats. Covers templates, filters, citations with BibTeX/CSL, and batch conversion automation scripts.
Read GitHub repos the RIGHT way - via gitmcp.io instead of raw scraping. Why this beats web search: (1) Semantic search across docs, not just keyword matching, (2) Smart code navigation with accurate file structure - zero hallucinations on repo layout, (3) Proper markdown output optimized for LLMs, not raw HTML/JSON garbage, (4) Aggregates README + /docs + code in one clean interface, (5) Respects rate limits and robots.txt. Stop pasting raw GitHub URLs - use this instead.
AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific cancer types, biomarker profiles, or post-progression scenarios.