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Persistent memory for OpenClaw agents. Store decisions, preferences, and context that survive across sessions. Build knowledge graphs that compound over time. Hybrid search (BM25 + vector + graph) recalls what matters when you need it.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/duclm1x1/Dive-Ai --skill penfield명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
SKILL.md 표시 중
| name | penfield |
| description | Persistent memory for OpenClaw agents. Store decisions, preferences, and context that survive across sessions. Build knowledge graphs that compound over time. Hybrid search (BM25 + vector + graph) recalls what matters when you need it. |
| metadata | {"openclaw":{"emoji":"🧠","install":[{"id":"npm","kind":"node","package":"openclaw-penfield","global":true,"label":"Install Penfield plugin"}],"requires":{"config":["plugins.entries.openclaw-penfield.enabled"]}}} |
Persistent memory that compounds. Your agent remembers conversations, learns preferences, connects ideas, and picks up exactly where it left off—across sessions, days, and channels.
| Tool | Purpose | When to use |
|---|---|---|
penfield_store | Save a memory | User shares preferences, you make a discovery, a decision is made, you learn something worth keeping |
penfield_recall | Hybrid search (BM25 + vector + graph) | Need context before responding, resuming a topic, looking up prior decisions |
penfield_search | Semantic search (higher vector weight) | Fuzzy concept search when you don't have exact terms |
penfield_fetch | Get memory by ID | Following up on a specific memory from recall results |
penfield_update_memory | Edit existing memory | Correcting, adding detail, changing importance or tags |
| Tool | Purpose | When to use |
|---|---|---|
penfield_connect | Link two memories | New info relates to existing knowledge, building understanding over time |
penfield_explore | Traverse graph from a memory | Understanding how ideas connect, finding related context |
| Tool | Purpose | When to use |
|---|---|---|
penfield_save_context | Checkpoint a session | Ending substantive work, preparing for handoff to another agent |
penfield_restore_context | Resume from checkpoint | Picking up where you or another agent left off |
penfield_list_contexts | List saved checkpoints | Finding previous sessions to resume |
penfield_reflect | Analyze memory patterns | Session start orientation, finding themes, spotting gaps |
| Tool | Purpose | When to use |
|---|---|---|
penfield_save_artifact | Store a file | Saving diagrams, notes, code, reference docs |
penfield_retrieve_artifact | Get a file | Loading previously saved work |
penfield_list_artifacts | List stored files | Browsing saved artifacts |
penfield_delete_artifact | Remove a file | Cleaning up outdated artifacts |
Memory content quality determines whether Penfield is useful or useless. The difference is specificity and context.
Bad — vague, no context, unfindable later:
"User likes Python"
Good — specific, contextual, findable:
"[Preferences] User prefers Python over JavaScript for backend work.
Reason: frustrated by JS callback patterns and lack of type safety.
Values type hints and explicit error handling. Uses FastAPI for APIs."
What makes a memory findable:
[Preferences], [Project: API Redesign], [Investigation: Payment Bug], [Decision]Use the correct type. The system uses these for filtering and analysis.
| Type | Use for | Example |
|---|---|---|
fact | Verified, durable information | "User's company runs Kubernetes on AWS EKS" |
insight | Patterns or realizations | "Deployment failures correlate with Friday releases" |
correction | Fixing prior understanding | "CORRECTION: The timeout isn't Redis — it's a hardcoded batch limit" |
conversation | Session summaries, notable exchanges | "Discussed migration strategy. User leaning toward incremental approach" |
reference | Source material, citations | "RFC 8628 defines Device Code Flow for OAuth on input-constrained devices" |
task | Work items, action items | "TODO: Benchmark recall latency after index rebuild" |
strategy | Approaches, methods, plans | "For user's codebase: always check types.ts first, it's the source of truth" |
checkpoint | Milestone states | "Project at 80% — auth complete, UI remaining" |
identity_core | Immutable identity facts | Set via personality config, rarely stored manually |
personality_trait | Behavioral patterns | Set via personality config, rarely stored manually |
relationship | Entity connections | "User works with Chad Schultz on cybersecurity content" |
Use the full range. Not everything is 0.5.
| Score | Meaning | Example |
|---|---|---|
| 0.9–1.0 | Critical — never forget | Architecture decisions, hard-won corrections, core preferences |
| 0.7–0.8 | Important — reference often | Project context, key facts about user's work |
| 0.5–0.6 | Normal — useful context | General preferences, session summaries |
| 0.3–0.4 | Minor — background detail | Tangential facts, low-stakes observations |
| 0.1–0.2 | Trivial — probably don't store | If you're questioning whether to store it, don't |
Connections are what make Penfield powerful. An isolated memory is just a note. A connected memory is understanding.
After storing a memory, always ask: What does this relate to? Then connect it.
Knowledge Evolution: supersedes · updates · evolution_of
Use when understanding changes. "We thought X, now we know Y."
Evidence: supports · contradicts · disputes
Use when new information validates or challenges existing beliefs.
Hierarchy: parent_of · child_of · sibling_of · composed_of · part_of
Use for structural relationships. Topics containing subtopics, systems containing components.
Causation: causes · influenced_by · prerequisite_for
Use for cause-and-effect chains and dependencies.
Implementation: implements · documents · tests · example_of
Use when something demonstrates, describes, or validates something else.
Conversation: responds_to · references · inspired_by
Use for attribution and dialogue threads.
Sequence: follows · precedes
Use for ordered steps in a process or timeline.
Dependencies: depends_on
Use when one thing requires another.
Good queries find things. Bad queries return noise.
Tune search weights for your query type:
| Query type | bm25_weight | vector_weight | graph_weight |
|---|---|---|---|
| Exact term lookup ("Twilio auth token") | 0.6 | 0.3 | 0.1 |
| Concept search ("how we handle errors") | 0.2 | 0.6 | 0.2 |
| Connected knowledge ("everything about payments") | 0.2 | 0.3 | 0.5 |
| Default (balanced) | 0.4 | 0.4 | 0.2 |
Filter aggressively:
memory_types: ["correction", "insight"] to find discoveries and correctionsimportance_threshold: 0.7 to skip noiseenable_graph_expansion: true to follow connections (default, usually leave on)penfield_store({
content: "[Preferences] User wants responses under 3 paragraphs unless complexity demands more. Dislikes bullet points in casual conversation.",
memory_type: "fact",
importance: 0.8,
tags: ["preferences", "communication"]
})
// Start
penfield_store({
content: "[Investigation: Deployment Failures] Reports of 500 errors after every Friday deploy. Checking release pipeline, config drift, and traffic patterns.",
memory_type: "task",
importance: 0.7,
tags: ["investigation", "deployment"]
})
// Discovery — connect to the investigation
discovery = penfield_store({
content: "[Investigation: Deployment Failures] INSIGHT: Friday deploys coincide with weekly batch job at 17:00 UTC. Both compete for DB connection pool. Not a deploy issue — it's resource contention.",
memory_type: "insight",
importance: 0.9,
tags: ["investigation", "deployment", "root-cause"]
})
penfield_connect({
from_memory_id: discovery.id,
to_memory_id: initial_report.id,
relationship_type: "responds_to"
})
// Correction — supersede wrong assumption
correction = penfield_store({
content: "[Investigation: Deployment Failures] CORRECTION: Not a CI/CD problem. Friday batch job + deploy = connection pool exhaustion. Fix: stagger batch job to 03:00 UTC.",
memory_type: "correction",
importance: 0.9,
tags: ["investigation", "deployment", "correction"]
})
penfield_connect({
from_memory_id: correction.id,
to_memory_id: initial_report.id,
relationship_type: "supersedes"
})
penfield_save_context({
memory_ids: [discovery.id, correction.id, initial_report.id],
session_id: "deployment-investigation-2026-02"
})
Next session or different agent:
penfield_restore_context({
checkpoint_id: "checkpoint-uuid",
merge_mode: "append"
})
Keep them short, consistent, lowercase. 2–5 per memory.
Good: preferences, architecture, investigation, correction, project-name
Bad: 2026-02-02, important-memory-about-deployment, UserPreferencesForCommunicationStyle
The native OpenClaw plugin is the fastest path, but Penfield works with any AI tool anywhere:
Claude Connectors
Name: Penfield
Remote MCP server URL: https://mcp.penfield.app
Claude Code
Claude mcp add --transport http --scope user penfield https://mcp.penfield.app
MCP Server — for Gemini CLI, Cursor, Windsurf, Intent, Perplexity Desktop or any MCP-compatible tool:
{
"mcpServers": {
"penfield": {
"command": "npx",
"args": [
"mcp-remote@latest",
"https://mcp.penfield.app/"
]
}
}
}
API — direct HTTP access at api.penfield.app for custom integrations.
Same memory, same knowledge graph, same account. The plugin is 4-5x faster (no MCP proxy layer), but everything stays in sync regardless of how you connect.
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