Manage old recordings — find large files, archive old meetings, delete processed originals. Use when the user says "clean up recordings", "how much space are meetings using", "delete old recordings", "archive meetings", "manage meeting storage", or asks about disk space from minutes.
Join a Google Meet or Zoom call as a video meeting agent via PikaStreaming.
Interacts with live Hunk diff review sessions via CLI. Inspects review focus, navigates files and hunks, reloads session contents, and adds inline review comments. Use when the user has a Hunk session running or wants to review diffs interactively.
Manage ACPX sessions for delegating tasks from an orchestrator agent to coding agents. Use for agent-to-coding-agent work delegation with named sessions, parallel workstreams, and status tracking.
Manage persistent coding sessions across Claude Code, Codex, Gemini, and Cursor engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, or switching models/tools at runtime. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent".
Analyze Claude Code session bloat — shows token count, context usage %, and bloat breakdown. Use when the user asks about session size, context usage, or when you notice the context window is getting full.
Coordinates meeting scheduling via Gmail and Calendar APIs. Use when: scheduling a meeting, checking participant availability, sending coordination emails, creating calendar events, or notifying organizers of confirmed bookings.
Query previous pi sessions to retrieve context, decisions, code changes, or other information. Use when you need to look up what happened in a parent session or any other session file.
3-tier agent memory system with 5-level compaction tree. OpenClaw version. Defines session start protocol, end-of-task checkpoints, and memory file management. MUST be followed every session.
Evaluate and score agent behavior against a golden reference. Use this skill whenever the user wants to run evaluation, check pass/fail status, understand metric scores, compare sessions for regressions, validate agent behavior, or score a trace from a file or a live session. Trigger on phrases like "eval this trace", "check my agent output", "did my agent do the right thing", "compare runs", "did my agent regress", "score session X", "evaluate against golden", "run evals". Works with both local trace files and live streaming sessions. --- Evaluate agent behavior and explain what the scores mean. ## Determine the input type First, figure out what to evaluate: - **Trace file(s)** — user mentions a `.json` or `.jsonl` file path → use `evaluate_traces` - **Sessions vs golden** — user has multiple live sessions and wants regression testing → use `evaluate_sessions` - **Single live session** — user wants to score one session against a golden eval set → guide them to use `evaluate_sessions` with one session as golden ## Evaluating trace files 1. Get the file path(s). Check the extension: `.jsonl` → `trace_format: "otlp-json"` | `.json` → `"jaeger-json"` (default) 2. Ask if they have a golden eval set JSON. For `tool_trajectory_avg_score` (the default metric), an eval set is required — it provides the expected tool call sequence to compare against. If they don't have one yet, explain this and suggest starting with `hallucinations_v1`, or ask if they want to create a golden set from a reference run first. 3. Call `evaluate_traces` with the file(s), format, and eval set. 4. Present results as a score table (see Score interpretation below) and explain failures. ## Evaluating sessions (regression testing) This workflow requires the server to be running with the `--dev` flag (which enables WebSocket and session streaming). Plain `agentevals serve` will not have sessions. If you get a connection error from any tool below, tell the user: ```bash uv run agentevals serve --dev ```
Monitors context window health throughout a session and rides peak context quality for maximum output fidelity. Activates automatically after plan-interview and intent-framed-agent. Stays active through execution and hands off cleanly to simplify-and-harden and self-improvement when the wave completes naturally or exits via handoff. Use this skill whenever a multi-step agent task is underway and session continuity or context drift is a concern. Especially important for long-running tasks, complex refactors, or any work where degraded context would silently corrupt the output. Trigger even if the user doesn't say "context surfing" — if an agent task is running across multiple steps with intent and a plan already established, this skill is live. --- # Context Surfing ## Install ```bash npx skills add pskoett/pskoett-ai-skills/skills/context-surfing ``` The agent rides the wave of peak context. When the wave crests, it commits. When it detects drift, it pulls out cleanly — saving state, handing off, and letting the next session catch the next wave. No wipeouts. No zombie sessions. Only intentional, high-fidelity execution. --- ## Mental Model
Capture current session transcript to workspace history. Use at session end or when preserving conversation context.
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:
