SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.
Use this skill when working on supercli documentation — editing docs/index.html, docs/plugins.html, creating scripts that generate doc data (meta-plugins.json), or aligning docs to current implementation. Covers the docs structure, terminology conventions, script patterns, and beads issue workflow.
Benchmark and optimize SDK, CLI, MCP, and prompt documentation so every LLM model can reliably call the right actions with correct arguments. Use when setting up skill-optimizer for a project, running benchmarks, interpreting results, optimizing SKILL.md files, or diagnosing configuration issues. Also use when working inside the skill-optimizer repository itself — for running against mock repos, testing changes, or understanding the codebase. --- # skill-optimizer Benchmark your SDK / CLI / MCP / prompt docs against multiple LLMs, measure whether they call the right actions with the right arguments, and iteratively rewrite your guidance until a quality floor is met across every model. ## Context Detection Before doing anything, figure out where you are: 1. **Look for `skill-optimizer.json`** (in CWD or parent directories). If found, you are in a **configured target project**. Use that file path as `<config-path>` in all commands below. 2. **Look for `src/cli.ts` and a `package.json` with `"name": "skill-optimizer"`**. If found, you are in the **optimizer repo itself**. You can use dev commands directly (`npm run build`, `npm test`, `npx tsx src/cli.ts`). To benchmark a target, either use the mock repos in `mock-repos/` or point `--config` at an external project's config. 3. **Neither found** — you are in an **unconfigured target project**. Read `references/setup.md` to scaffold a config before proceeding. ## Quick Reference | Task | Command | |------|---------| | Init config | `npx skill-optimizer init cli\|sdk\|mcp\|prompt` | | Init (non-interactive) | `npx skill-optimizer init cli --yes` | | Import CLI commands | `npx skill-optimizer import-commands --from ./src/cli.ts` | | Import (binary scrape) | `npx skill-optimizer import-commands --from my-cli --scrape` | | Diagnose config | `npx skill-optimizer doctor --config <config-path>` | | Auto-fix config | `npx skill-optimizer doctor --fix --config <config-path>` | | Dry run (no LLM calls) | `npx skill-optimizer run -
Capture current session transcript to workspace history. Use at session end or when preserving conversation context.
Local-first cross-harness memory for agents. Syke observes activity across supported harnesses, keeps a current memex in context, and gives agents `syke ask`, `syke context`, and `syke record` for continuity across sessions.
Remove signs of AI-generated writing from academic medical papers. Use when editing or reviewing manuscripts to make them sound more natural and professionally written. Based on Wikipedia's "Signs of AI writing" guide, adapted for medical literature.
Query and manage Apple Calendar on macOS via `icalBuddy` (read) and AppleScript (`osascript`) for event creation. Use when users ask about upcoming events or adding calendar events.
Generates optimized prompts for any AI tool.
Generates OpenChart (https://github.com/tryopendata/openchart) chart, table, graph, and sankey specs from data, and guides editorial design decisions. Use when creating visualizations, building charts, rendering data tables, generating VizSpec JSON, creating network graphs, building sankey/flow diagrams, answering questions about OpenChart types and encoding rules, or making design decisions about chart type selection, color strategy, typography, annotations, and editorial framing. Also covers custom D3.js infographics for cases beyond declarative specs. --- # Data Visualization with OpenChart **Core concept:** Write a VizSpec JSON object, render with `<Chart>` / `<DataTable>` / `<Graph>` / `<Sankey>` (React/Vue/Svelte) or `createChart()` / `createTable()` / `createGraph()` / `createSankey()` (vanilla JS). The engine validates, compiles, and renders. Specs are plain JSON, no imperative drawing. See https://github.com/tryopendata/openchart for the rendering engine. **CSS is required.** OpenChart's stylesheet must be loaded for proper rendering (chrome, tables, tooltips, brand watermark). Framework imports handle this automatically, but CDN/standalone HTML needs an explicit `<link>`: ```html <link rel="stylesheet" href="https://esm.sh/@opendata-ai/openchart-vanilla/styles.css"> ``` See [rendering reference](references/rendering.md) for details. ## Chart Selection Decision Tree ``` Single value to highlight -> Use chrome.title as a big number display Temporal x-axis column? -> 1 series: line | 2-5 series: line + color | 6+: filter to top 5 Categorical + numeric? -> Ranked list: bar (horizontal) | Periodic (Q1, Jan): bar (vertical) | 2-6 composition: arc Two numeric columns? -> point (optional size/color for 3rd/4th dims) Categorical + series + num? -> stacked bar (use color for series) Distribution/spread? -> circle (strip plot) Nodes + edges / network? -> graph (force/radial/hierarchical layout) Flow between stages? -> sankey
Restored placeholder content.
End-to-end smoke testing of the Meeseeks CLI via tmux. Use this skill when asked to test the CLI, verify CLI behavior after changes, smoke-test the agent loop, check for regressions, or validate MCP/plugin/session features work correctly through the terminal interface. Also use when debugging CLI crashes, MCP connection issues, or session lifecycle problems that need live reproduction.
Security monitor for scrapingbee-cli. Monitors audit log for suspicious activity. Stops unauthorized schedules. ALWAYS active when scrapingbee-cli is installed.
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:
