How do I install the Penfield skill?

Run npx skillmds@latest add modbender/penfield in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Penfield skill do?

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. It is listed under Coding & Dev Tools on SkillMD.

Is Penfield safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Penfield?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Penfield free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Penfield?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Penfield by modbender — Agent Skill

Name: Penfield
Rating: 4.666666666666667 (3 reviews)
Author: modbender

Penfield Memory

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.

Tools

Memory

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

Knowledge Graph

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

Context & Analysis

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

Artifacts

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

Writing Memories That Actually Work

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:

Context prefix in brackets: [Preferences], [Project: API Redesign], [Investigation: Payment Bug], [Decision]
The "why" behind the "what" — rationale matters more than the fact itself
Specific details — names, numbers, dates, versions, not vague summaries
References to related memories — "This builds on [earlier finding about X]" or "Contradicts previous assumption that Y"

Memory Types

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"

Importance Scores

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

Connecting Memories

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.

Relationship Types (24)

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.

Recall Strategy

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 corrections
importance_threshold: 0.7 to skip noise
enable_graph_expansion: true to follow connections (default, usually leave on)

Workflows

User shares a preference

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"]
})

Investigation tracking

// 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"
})

Session handoff

penfield_save_context({
  name: "deployment-investigation-2026-02",
  description: "Investigated deployment timeout issues. memory_id: " + discovery.id,
  memory_ids: [discovery.id, correction.id, initial_report.id]
})

Next session or different agent:

penfield_restore_context({
  name: "deployment-investigation-2026-02"
})

What NOT to Store

Verbatim conversation transcripts (too verbose, low signal)
Easily googled facts (use web search instead)
Ephemeral task state (use working memory)
Anything the user hasn't consented to store about themselves
Every minor exchange (be selective — quality over quantity)

Also Available Outside OpenClaw

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.

Penfield

File contents

Penfield Memory

Tools

Memory

Knowledge Graph

Context & Analysis

Artifacts

Writing Memories That Actually Work

Memory Types

Importance Scores

Connecting Memories

Relationship Types (24)

Recall Strategy

Workflows

User shares a preference

Investigation tracking

Session handoff

What NOT to Store

Tags

Also Available Outside OpenClaw

Links

Frequently asked questions

Penfield

File contents

Penfield Memory

Tools

Memory

Knowledge Graph

Context & Analysis

Artifacts

Writing Memories That Actually Work

Memory Types

Importance Scores

Connecting Memories

Relationship Types (24)

Recall Strategy

Workflows

User shares a preference

Investigation tracking

Session handoff

What NOT to Store

Tags

Also Available Outside OpenClaw

Links

Related skills

Frequently asked questions

How do I install the Penfield skill?

What does the Penfield skill do?

Is Penfield safe to use?

Which AI agents work with Penfield?

Is Penfield free to use?

Who published Penfield?