Baseline de conhecimento para AI/ML engineering moderno em Python. Foco em LLM engineering, RAG systems, agent frameworks (LangChain/LangGraph), multiple LLM providers (Anthropic, OpenAI, Bedrock, Gemini, Meta), vector databases (Qdrant), semantic caching (MongoDB, Redis), testing, observability, security, e production patterns. Complementa arch-py skill com patterns AI-specific.
Deploy and manage compliant tokens on 10 blockchain networks via Bitbond TokenTool MCP (17 tools). Use when user asks to deploy a token, create an ERC-20, issue a security token, tokenize an asset, mint tokens, burn tokens, pause token transfers, create an SPL token on Solana, issue a Stellar asset, estimate deployment cost, check token info, list deployed tokens, set up whitelist or blacklist compliance, manage whitelist/blacklist addresses, check compliance status, or manage token lifecycle. Supports EVM chains, Solana, and Stellar with CertiK-audited contracts.
This skill MUST be invoked when the user asks for systematic bug analysis, or any focused audit such as "api audit", "auditcodex", "cache audit", "disaster recovery", "error review", "feature flags audit", "integration security", "observability audit", "queue audit", "release discipline", "serialization audit", "session audit", "tech debt", "tenant isolation", "test review", "upload security", "ai code audit", "dead code", any security vulnerability scan such as "sql injection", "xss", "rce", "ssrf", "xxe", "access control", "path traversal", "file upload", "ssti", "graphql injection", "business logic", "missing auth", or "security recon", or a FULL security sweep such as "güvenlik taraması", "security scan", "full security scan", "run all security scans", or "security sweep". Use `/bug-report` for general scans, `/bug-report <subcommand>` for domain-specific audits, and `/bug-report security-sweep` to run all security scans in parallel. All modes write verified findings to BUG-REPORT.md using the shared report contract.
Perform an accessibility audit on UI changes.
ZIRAN is an open-source security testing framework for AI agents. It discovers dangerous tool chain compositions via knowledge graph analysis, detects execution-level side effects (not just text output), and runs multi-phase trust exploitation campaigns that model real attacker behavior.
Use when reconciling bank transactions, ledger lines, or solving many-to-many numeric matching with the dpss MCP tools. Prefer this for subset-sum style matching that must remain deterministic and auditable.
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 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:
