memgentic
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: memgentic
description: >- Use when this capability is needed. Memgentic captures knowledge from all your AI tools and m…
category: other
runtime: no special runtime
---
# memgentic output preview
## PART A: Task fit
- Use case: >- Use when this capability is needed. Memgentic captures knowledge from all your AI tools and makes it searchable. Every memory carries full provenance: which tool, which session, when it happened. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use / CLI Commands / Semantic Search (primary)” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “>- Use when this capability is needed. Memgentic captures knowledge from all your AI tools and makes it searchable. Every memory carries full provenance: which tool, which session, when it happened. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “When to Use / CLI Commands / Semantic Search (primary)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “When to Use / CLI Commands / Semantic Search (primary)”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: memgentic
description: >- Use when this capability is needed. Memgentic captures knowledge from all your AI tools and m…
category: other
source: tomevault-io/skills-registry
---
# memgentic
## When to use
- >- Use when this capability is needed. Memgentic captures knowledge from all your AI tools and makes it searchable. Ev…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Use / CLI Commands / Semantic Search (primary)” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "memgentic" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Use / CLI Commands / Semantic Search (primary)
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Memgentic — Universal AI Memory Layer
Memgentic captures knowledge from all your AI tools and makes it searchable. Every memory carries full provenance: which tool, which session, when it happened.
When to Use
Search memory before doing work, not after:
- Starting a new task — Check for prior decisions, context, or related work.
- Making architectural decisions — Check if this was discussed before.
- Debugging — Search for related past issues and solutions.
- User asks about history — "what did we decide about...", "remember when...", "how did we handle..."
- Encountering unfamiliar code — Check for context on why it was written that way.
- Setting up or configuring something — Check for user preferences and conventions.
When in doubt, search. It costs little and prevents repeated work.
CLI Commands
Semantic Search (primary)
# Search memories by meaning
memgentic search "database migration strategy"
# Compact output — fewer tokens, good for scanning
memgentic search "database migration strategy" --format compact
# Filter by platform
memgentic search "auth implementation" -s claude_code
memgentic search "API design" -s chatgpt
# Filter by content type
memgentic search "why we chose PostgreSQL" -t decision
memgentic search "user coding style" -t preference
Store a Memory
# Save an important fact or decision
memgentic remember "We chose Qdrant over Pinecone for local-first vector storage"
# Memory type is auto-classified from content
memgentic remember "Always use UV for package management"
Other Commands
# See which platforms have memories and how many
memgentic sources
# Generate standalone context file with recent activity
memgentic update-context
# Explore knowledge graph around an entity
memgentic graph "authentication"
# Check system health
memgentic doctor
MCP Tools
If the Memgentic MCP server is running, these tools are available directly:
| Tool | Purpose |
|---|---|
memgentic_recall |
Semantic search with source filtering |
memgentic_remember |
Store a new memory |
memgentic_search |
Full-text keyword search |
memgentic_recent |
Recent memories by timestamp |
memgentic_briefing |
Cross-agent briefing of recent activity |
memgentic_sources |
List platforms and memory counts |
memgentic_configure_session |
Set session-level source filters |
memgentic_stats |
Memory statistics and analytics |
memgentic_forget |
Archive (soft-delete) a memory |
memgentic_export |
Export memories as JSON |
MCP Examples
memgentic_recall(query="authentication flow", limit=5)
memgentic_recall(query="auth", sources=["claude_code", "chatgpt"])
memgentic_remember(content="Project uses JWT with refresh tokens", memory_type="decision")
memgentic_search(query="PostgreSQL", content_type="decision")
memgentic_configure_session(exclude_sources=["codex_cli"])
memgentic_briefing(hours=24)
Efficient Retrieval Strategy
Minimize token usage with a progressive approach:
- Start compact — Use
--format compactfor an overview of what exists. - Narrow down — Add platform filter (
-s claude_code) or type filter (-t decision) when you know what you are looking for. - Full detail — Drop
--format compactonly when you need the complete memory content. - Limit results — Use
--limitto cap the number of results returned.
# Step 1: Quick scan
memgentic search "deployment" --format compact --limit 10
# Step 2: Found relevant results, get details from a specific platform
memgentic search "deployment" -s claude_code --limit 3
Memory Types
Memories are classified into types for filtering:
| Type | Contains |
|---|---|
decision |
Architectural and technical decisions with rationale |
learning |
Things learned during development |
preference |
User preferences, conventions, coding style |
fact |
General knowledge and project context |
bug_fix |
Bug fixes, root causes, and solutions |
conversation_summary |
Summaries of complete sessions |
Filter by type to get precise results:
memgentic search "database" -t decision # Only decisions about databases
memgentic search "testing" -t preference # Only testing preferences
memgentic search "auth" -t bug_fix # Only auth-related bug fixes
Cross-Platform Context
Memgentic captures from multiple AI tools. Each memory records its source platform:
| Platform | Source ID |
|---|---|
| Claude Code | claude_code |
| Gemini CLI | gemini_cli |
| ChatGPT | chatgpt |
| Aider | aider |
| Codex CLI | codex_cli |
| Copilot CLI | copilot_cli |
| Claude Web/Desktop | claude_web |
| Antigravity | antigravity |
Use source filtering to scope searches:
# What did we discuss in Claude Code about this project?
memgentic search "project architecture" -s claude_code
# What did ChatGPT suggest about this topic?
memgentic search "caching strategy" -s chatgpt
# See all available sources
memgentic sources
Capture Methods
Memories enter the system through:
- auto_daemon — File watcher captures conversations automatically in the background.
- mcp_tool — Stored via MCP tool calls during AI sessions.
- cli — Manually saved via
memgentic remember. - json_import — Bulk imported from conversation exports (ChatGPT, Claude Web).
Setup
# Check prerequisites
memgentic doctor
# Interactive setup (model selection, configuration)
memgentic setup
# Import existing conversations from all detected AI tools
memgentic import-existing
# Start background capture daemon
memgentic daemon
# Start MCP server for AI tool integration
memgentic serve
Source: Chariton-kyp/Memgentic — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review