- 📄 SKILL.md
adversarial-machine-learning
Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks.
Free to get · One-click to use
Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks.
Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources. Use when the user needs thorough investigation, market research, technical deep-dives, due diligence, or comprehensive analysis on any subject.
AKTIVIERT SICH AUTOMATISCH bei vagen Auftraegen. LIEBER EINMAL ZU OFT NACHFRAGEN als falsch implementieren. Erkennungsmerkmale (EINES genuegt!): - Auftrag <25 Woerter - Keine konkreten Dateinamen/Pfade - Vage Verben: besser, optimieren, fixen, machen, aendern, verbessern, anpassen, erweitern, refactoren, aufraumen, ueberarbeiten - Unsichere Sprache: irgendwie, vielleicht, mal eben, schnell, einfach, bisschen, koennte, sollte - Fehlende Erfolgskriterien: Kein damit, sodass, weil, um zu - Relative Begriffe ohne Kontext: schneller, besser, schoener, einfacher Output ist STRUKTURIERTES JSON fuer prompt-architect Skill.
Work with Airflow 3.x on Astronomer. Start/stop, create/trigger DAGs, read logs, run tests. Triggers on "astro", "airflow", "start airflow", "trigger dag", "dag logs".
Deep research infrastructure for comprehensive analysis. Use when users need to research a topic in depth, consult domain experts, preview research curricula, run async investigations, compare options with citations, or manage research budgets.
NeuroSkill™ exposes a real-time EEG analysis API through a local WebSocket server and an HTTP tunnel.
General-purpose Excel skill for workbook analysis, transformation, reporting, and visualization tasks.
Deep scientific investigation with papercli. Iterative search, broad PDF corpus download and reading, equation-level analysis, and exhaustive referenced markdown findings.
Fetch and persist article full text for RSS entries already stored in SQLite by ai-tech-rss-fetch. Use when backfilling or incrementally syncing body text from entries.url or entries.canonical_url into a companion table for downstream indexing, retrieval, or summarization.
メールを確認する・receive_email.pyで受信する・send_email.pyで送信する・メール認証エラーが出たとき → このスキルを読む
Triage static analysis findings, assess merit, and accept noise or irrelevant items
Obol Stack development, testing, and LLM routing validation through LiteLLM. Use when developing, testing, or validating inference paths (Ollama, Anthropic, OpenAI) through the LiteLLM gateway, writing integration tests, or working with obol CLI wrappers.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide AI semantic + keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills: