Free to get · One-click to use
Write JavaScript code in n8n Code nodes. Use when writing JavaScript in n8n, using $input/$json/$node syntax, making HTTP requests with $helpers, working with dates using DateTime, troubleshooting Code node errors, or choosing between Code node modes.
Four sweep operations: (1) Model perf sweep — find optimal batch size / TGS for a model. Use for: sweep batch size, tune TGS, benchmark throughput, find optimal config. (2) Node perf sweep — compare per-node GPU performance to find outliers. Use for: check nodes, node performance, find slow node, compare nodes. (3) Node network health sweep — detect inter-node network issues via multi-node bisection. Use for: network health, IB issues, RCCL problems, node pair testing, isolate network problem. (4) Model sweep — run all model configs on one or two commits. Use for: regression test, validate commit, test all models, smoke test, CI, compare branches.
Structured memory system for AI workspaces. Indexes markdown memory files into SQLite FTS5 for fast, cited search. Extracts structured facts, maintains memory health, and provides an MCP server with live search + write-path for Claude Code integration. --- # Structured Memory Engine ## MCP Tools (v4) When running as an MCP server (`node lib/mcp-server.js`), exposes: - `sme_query` — Search memory. Supports `query`, `limit`, `since`, `type`, `minConfidence`, `includeStale`. - `sme_context` — Get relevant context for a message. Returns ranked, token-budgeted, formatted context for injection. Supports `message`, `maxTokens`. - `sme_remember` — Save a fact/decision/preference to today's memory log. Auto-indexed. - `sme_index` — Re-index workspace. Use `force: true` for full rebuild. - `sme_reflect` — Run maintenance: decay, reinforce, stale detection, contradictions, prune. Use `dryRun: true` to preview. - `sme_status` — Index statistics. ## CLI Commands ```bash # Index workspace memory files node lib/index.js index [--workspace PATH] [--force] [--include extra.md,other.md] # Search indexed memory node lib/index.js query "search terms" [--limit N] [--since 7d|2w|3m|2026-01-01] [--context N] [--type fact|confirmed|inferred|...] [--min-confidence 0.5] [--include-stale] # Show index status node lib/index.js status [--workspace PATH] # Memory maintenance node lib/index.js reflect [--workspace PATH] [--dry-run] node lib/index.js contradictions [--workspace PATH] [--unresolved] node lib/index.js archived [--workspace PATH] [--limit N] node lib/index.js restore <chunk-id> [--workspace PATH] ``` ## Configuration
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:
