Capture a specific piece of knowledge, constraint, or decision to the Kindex knowledge graph. Use when the user says "remember this", "add this to kindex", or "this is important".
Use when Claude makes repeated mistakes, users correct behavior, tool failures indicate missing knowledge, or managing learning via /coach commands.
Bootstrap a knowledge store by exploring codebase architecture — use when starting with a new or empty project, seeding knowledge, or running /bootstrap
LLM-compiled knowledge base manager. Activates when user works with wiki directories, mentions knowledge base management, asks knowledge questions in a project with a wiki, wants to ingest/compile/query/lint knowledge, or uses /wiki commands. Also activates when user says "wiki", "knowledge base", "ingest", "compile wiki", "add to wiki", "search wiki", or asks a factual question in a directory containing .wiki/ or when ~/wiki/ exists.
Build and maintain LLM-powered personal knowledge bases (inspired by Karpathy + kepano).
Add a source to your Mnemon knowledge library — articles, YouTube videos, podcasts, books, ideas. Captures content and generates a structured extract with personal context framing. Use when the user provides a URL to save, says "add source", "capture this", "save this article/video/podcast", or wants to process any content into their knowledge library.
Query mahdi navigator about Knowledge navigator for Mahdi. Use when user asks to "ask mahdi" or needs information from this knowledge base.
Search and retrieve knowledge from agentic_kb knowledge base. Use when the user requests to search the KB, asks "How do I..." questions that should consult the KB, wants to document new knowledge, or at session start to update the KB submodule. Also use when User wants to udpate the knowledge base with new knowledge. Knowledge Capture when you learn new, reusable knowledge during tasks. Supports Typesense (fast full-text search), FAISS (semantic vector search), and ripgrep (exact pattern matching). All KB is Obsidian formatted and can be browsed easily and visually with network maps in Obsidian.
Self-amendment MCP server framework with layered skill architecture and blockchain auditability. Use when the user wants to manage knowledge, evolve skills, track changes with blockchain, audit skill health, promote knowledge between layers, or perform persona assembly for decision support. --- # KairosChain - Self-Amendment MCP Server Framework KairosChain provides a layered skill architecture (L0/L1/L2) where skills can evolve, promote, and audit themselves with blockchain-backed auditability. ## Architecture ### Three-Layer System - **L0 (Constitution/Law)**: Immutable safety rules and meta-governance. Changes require human approval and full blockchain recording. - **L1 (Knowledge)**: Project knowledge in Anthropic skills format. Changes recorded with hash references. - **L2 (Context)**: Temporary session context. Free modification, no blockchain recording. ### Core Capabilities #### Knowledge Management - `knowledge_list` / `knowledge_get` - Browse and read L1 knowledge skills - `knowledge_update` - Create, update, or delete L1 knowledge with blockchain recording - `context_save` - Save temporary L2 context for session work #### Skill Evolution - `skills_evolve` - Propose and apply changes to L0 skill definitions (requires human approval) - `skills_rollback` - Version management with snapshot and rollback capabilities - `skills_promote` - Promote knowledge between layers (L2→L1, L1→L0) with optional Persona Assembly #### Blockchain Auditability - `chain_status` / `chain_verify` - Check and verify blockchain integrity - `chain_history` - View skill transitions, knowledge updates, and state commits - `chain_record` - Record data to the blockchain - `state_commit` - Create snapshots of all layers for auditability #### Health & Safety - `skills_audit` - Audit knowledge health across layers (conflicts, staleness, dangerous patterns) - `tool_guide` - Dynamic tool discovery and workflow recommendations #### Persona Assembly - Multi-perspective decision support using pe
Intelligent skill knowledge gateway. Routes tasks to the right knowledge without loading all skills into context. MUST be consulted before any coding task — call the search_skills MCP tool to retrieve relevant expertise from 100+ indexed skills covering Swift, SwiftUI, concurrency, testing, architecture, performance, and security.
Use this skill when the user wants to manage AI agent knowledge, organize agent configurations, or set up post-session learning systems. Triggers include: organizing agent skills or knowledge into a structured shelf, extracting insights from conversation transcripts, splitting monolithic agent config files into modular pieces, managing memory files, searching indexed knowledge, or diagnosing shelf health. Use ShelfAI whenever a user mentions managing agent context, chunking configs, learning from sessions, compacting memory, or organizing reusable agent knowledge pieces.
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:
