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Import Skills

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CortexReach CortexReach
from GitHub Data & AI
  • 📄 SKILL.md
conversation lesson long-term

lesson

Store a lesson learned from the current conversation. Triggered by /lesson command. Use when Master signals that the recent conversation contains a pitfall, fix, or key insight that should be persisted to long-term memory.

⬇0 ❤4K 12 days ago · Uploaded Detail →
sopaco sopaco
from GitHub Tools & Productivity
  • 📄 SKILL.md
context conversations automation

cortex-mem-mcp

Persistent memory enhancement for AI agents. Store conversations, search memories with semantic retrieval, and recall context across sessions. Use this skill when you need to remember user preferences, past conversations, project context, or any information that should persist beyond the current session. Provides tiered access (abstract/overview/content) for efficient context management.

⬇0 ❤229 10 days ago · Uploaded Detail →
elevenlabs elevenlabs
from GitHub Tools & Productivity
  • 📁 references/
  • 📄 SKILL.md
voice automation conversation

agents

Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots, interactive voice characters, or any real-time voice conversation experience.

⬇0 ❤152 9 days ago · Uploaded Detail →
Ar9av Ar9av
from GitHub Tools & Productivity
  • 📁 references/
  • 📄 SKILL.md
claude user conversations

claude-history-ingest

Ingest Claude Code conversation history into the Obsidian wiki. Use this skill when the user wants to mine their past Claude conversations for knowledge, import their ~/.claude folder, extract insights from previous coding sessions, or says things like "process my Claude history", "add my conversations to the wiki", "what have I discussed with Claude before". Also triggers when the user mentions their .claude folder, Claude projects, session data, or past conversation logs. --- # Claude History Ingest — Conversation Mining You are extracting knowledge from the user's past Claude Code conversations and distilling it into the Obsidian wiki. Conversations are rich but messy — your job is to find the signal and compile it. ## Before You Start 1. Read `.env` to get `OBSIDIAN_VAULT_PATH` and `CLAUDE_HISTORY_PATH` (defaults to `~/.claude`) 2. Read `.manifest.json` at the vault root to check what's already been ingested 3. Read `index.md` at the vault root to know what the wiki already contains ## Ingest Modes ### Append Mode (default) Check `.manifest.json` for each source file (conversation JSONL, memory file). Only process: - Files not in the manifest (new conversations, new memory files, new projects) - Files whose modification time is newer than their `ingested_at` in the manifest This is usually what you want — the user ran a few new sessions and wants to capture the delta. ### Full Mode Process everything regardless of manifest. Use after a `wiki-rebuild` or if the user explicitly asks. ## Claude Code Data Layout Claude Code stores everything under `~/.claude/`. Here is the actual structure: ``` ~/.claude/ ├── projects/ # Per-project directories │ ├── -Users-name-project-a/ # Path-derived name (slashes → dashes) │ │ ├── <session-uuid>.jsonl # Conversation data (JSONL) │ │ └── memory/ # Structured memories │ │ ├── MEMORY.md # Memory index │ │ ├── user_*.md # U

⬇0 ❤29 4 days ago · Uploaded Detail →
ldclabs ldclabs
from GitHub Tools & Productivity
  • 📁 assets/
  • 📁 src/
  • 📄 API.md
  • 📄 API_cn.md
  • 📄 app.html
memory automation conversations

anda-hippocampus

Long-term memory service for LLM agents. Provides persistent, structured memory (Cognitive Nexus) through three operations: Formation (encode conversations into memory), Recall (query memory with natural language), and Maintenance (consolidate and prune memory).

⬇0 ❤32 12 days ago · Uploaded Detail →
QuantumBFS QuantumBFS
from GitHub Development & Coding
  • 📄 extract_dialog.py
  • 📄 SKILL.md
graesser dialog ai

conversation-dump

Use when analyzing conversation patterns — extracts dialog from Claude Code or Codex CLI history, classifies each user message across 6 academic dimensions (Bloom's cognitive level, Graesser question depth, Paul & Elder reasoning probe, Walton presupposition quality, Long & Sato discourse function, Graesser generation mechanism), and outputs tagged dialog reports

⬇0 ❤21 12 days ago · Uploaded Detail →
taylorai taylorai
from GitHub Tools & Productivity
  • 📄 SKILL.md
claude conversation codex

agent-history

CLI tool to explore and inspect past Claude Code and Codex conversation histories. Use this skill when: - You need to catch up on a previous conversation that ran out of context - You want to review what was discussed or accomplished in past sessions - You need to search across conversation history for specific topics - You want to generate a summary of past work to paste into a new session - The user asks about their Claude Code or Codex conversation history - The user wants to resume work from a previous session and needs context --- # Agent History CLI A unified tool to explore past Claude Code (`~/.claude/projects/`) and Codex (`~/.codex/sessions/`) conversations from a single interface. ## Installation ```bash pip install agent-history # Install the skill (default: ~/.claude/skills/) agent-history install-skill ``` ## Source Tagging

⬇0 ❤13 8 days ago · Uploaded Detail →

Skill File Structure Sample (Reference)

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

SKILL.md Requirements

├─ ⭐ 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

Why SkillWink?

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.

Keyword Search Version Updates Multi-Metric Ranking Open Standard Discussion

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.

FAQ

Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.

1. What are Agent Skills?

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.

2. How do Skills work?

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.

3. How can I quickly find the right skill?

Use these three together:

  • Semantic search: describe your goal in natural language.
  • Multi-filtering: category/tag/author/language/license.
  • Sort by downloads/likes/comments/updated to find higher-quality skills.

4. Which import methods are supported?

  • Upload archive: .zip / .skill (recommended)
  • Upload skills folder
  • Import from GitHub repository

Note: file size for all methods should be within 10MB.

5. How to use in Claude / Codex?

Typical paths (may vary by local setup):

  • Claude Code：~/.claude/skills/
  • Codex CLI：~/.codex/skills/

One SKILL.md can usually be reused across tools.

6. Can one skill be shared 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.

7. Are these skills safe to use?

Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.

8. Why does it not work after import?

Most common reasons:

  • Wrong folder path or nested one level too deep
  • Invalid/incomplete SKILL.md fields or format
  • Dependencies missing (Python/Node/CLI)
  • Tool has not reloaded skills yet

9. Does SkillWink include duplicates/low-quality skills?

We try to avoid that. Use ranking + comments to surface better skills:

  • Duplicate skills: compare differences (speed/stability/focus)
  • Low quality skills: regularly cleaned up