12,529 mods in this category, of every kind an
agent can take. Each one carries what it costs per session, what the
scan found, and whether it is the original.
Long-term Markdown project memory for AI coding agents. Use when the user wants to record, recall, audit, sync, or compress project decisions, architecture, conventions, monorepo scopes, or .lore/ entries, including natural-language requests like "remember this decision" or explicit lore…
Search, browse, and resume past AI coding sessions across GitHub Copilot, Claude Code, OpenCode, Cursor, and Codex using the sessfind CLI. Use this skill whenever the user wants to find a previous conversation, search through session history, resume or continue an old session, check what was discussed before, look up…
Skills for agents you leave and come back to: /whats-next and /human-context-rebuild re-orient you, /stale-check and /worktree-cleanup handle finished branches, /cold-read checks words before users see them, and the idle-return hook runs whats-next when you come back.
★not rated 24
changed 5d agoA
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originalMIT
Use agent-experience-capitalization as a project-owned, team-shareable engineering memory layer for coding agents. Use it to activate prior experience before work, save reusable lessons after work, inspect save/get/log health, and diagnose Milvus-centric retrieval.
A starter kit for creating an Obsidian vault, which is a folder of linked Markdown notes used as a personal knowledge base. It sets up folders, note templates, Obsidian settings, dashboards, and helper skills.
How to use Memento — a local-first MCP memory layer for AI assistants — to remember and recall durable knowledge about the user, their preferences, conventions, decisions, and projects across sessions. Trigger this skill whenever the user has Memento connected and any of the following happens, even when the user does…
A self-developing knowledge management system for Claude Code. Bridges AI memory to an Obsidian vault with feature-organized spine notes, auto-capture skills, and color-coded graph visualization.
★not rated 24
changed yesterdayA
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originalMIT
MCP server for Obsidian — semantic knowledge graph with auto-classification, DAG hierarchy, and cross-domain bridge detection. Runs locally from the nouz-mcp Python package. Needs 8 environment variables to run.
★not rated 24 21d agoA
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originalMIT
Your self-updating engineering brain — tracks work patterns, expertise, habits, progress gaps, and generates daily sync-up notes and quarterly review content. Invoke when you say "engineer brain", "update brain", "daily sync", "standup", "sync up", "quarterly connections", "what did I work on", "my progress", "where…
Cognitive memory for AI agents — semantic recall, a knowledge graph, contradiction detection and procedural learning. Local-first: one SQLite file, no account, no telemetry.
★not rated 24▲
+1 4d agoA
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Personal assistant workflow for durable memory, connected actions, decisions, planning and review. Use whenever the user discusses their life, commitments, goals, notes, choices, follow-ups, or schedule.
Analyze a complete literary work into a structured Basic Memory knowledge graph. Covers schema design, entity seeding, chapter-by-chapter processing, cross-referencing, validation, and visualization.
Pipeline PQ di proiezione del contesto: T1 compressione signal-preserving degli output dei tool (canary end-to-end con auto-degrade: dopo N violazioni updatedToolOutput nella sessione, raw pass-through; auto-heal su evidenza dal transcript: auto-ack dei failure transitori dopo M verified consecutive e un-degrade a son.
★not rated 23 3mo agoA
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originalMIT
Use this skill when working in a project that uses Memory by Aictx as product-layer project memory. It guides the agent to query memory on demand mid-task, save product-meaningful changes after meaningful work, and sync memory at session end.
Local evidence recall across Claude Code and Codex histories: durable text and tool-request retrieval, checked citations, compaction recovery, optional private coding sessions, and separately configured Claude app readers.
★not rated 23
changed 1mo agoA
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originalMIT
The missing feedback loop for agentic development. Builds a living user model from AI coding session transcripts — extracts who the user is, what the AI keeps getting wrong, and what rules would prevent those mistakes. Writes the result to the memory system so every new session starts informed.
Scheduled KB maintenance — ingest meetings and Claude Code/Codex JSONL sessions, synthesize raw notes into existing topical docs (decisions, people changes, conventions), resolve conflicts, age out stale entries, fix frontmatter and links. Translates raw data into knowledge. Runs unattended; never asks for…
Detects correction or long-term note-taking scenarios, runs lightweight scholar preflight on fresh tasks, keeps the agent in an explicit closed loop until the user confirms completion, escalates into Ascended Mode after repeated failed corrections or manual trigger, archives detailed mistake / note entries into pro.
★not rated 23 6mo agoA
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originalMIT
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: