Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add penfieldlabs/claude-penfield --skill penfieldgit clone --depth 1 https://github.com/penfieldlabs/claude-penfieldWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/penfieldlabs/claude-penfield/penfield)<a href="https://agentmods.dev/skills/penfieldlabs/claude-penfield/penfield"><img src="https://agentmods.dev/badge/skills/penfieldlabs/claude-penfield/penfield/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/penfieldlabs/claude-penfield/penfield"><img src="https://agentmods.dev/badge/skills/penfieldlabs/claude-penfield/penfield.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.02297 |
| Opus 5 | $0.00039 | $0.01149 |
| Sonnet 5 | $0.00015 | $0.00459 |
| Haiku 4.5 | $0.00008 | $0.00230 |
Grade B, and why
penfield scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
cat ~/.claude/projects/*/*.jsonl | jq -r 'select(.message.content) | .message.content' | grep -i "keyword" How it starts
The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Penfield Memory System
You have access to Penfield, a persistent memory system with hybrid search (BM25 + vector + graph), knowledge graphs, and context management. Memory persists across all sessions and platforms.
Session Lifecycle
Starting a Session
- Call
awaken()to load personality configuration and user preferences - Call
reflect({ time_window: "recent" })to see what you've been working on lately - Let this context shape your first response — don't greet cold
During a Session
- Before answering questions about past work: Call
recall()first — check what you already know - When important things happen: Store decisions, corrections, breakthroughs, and user preferences
- After storing: Connect related memories using
connect()— no orphan memories - When the user says "remember this": Always store immediately
Ending a Session
- If substantive work was done, call
save_context()with a detailed cognitive handoff:- What you were investigating
- Key discoveries and decisions
- Current hypotheses and open questions
- Suggested next steps
- References to specific memory IDs
Memory Types (11)
Memory type is auto-detected from content — you don't pass it as a parameter. Write descriptively so the system classifies correctly. Here's what each type represents:
Core Types (8)
| Type | When to Use |
|---|---|
fact |
Verified, durable information — preferences, specs, organizational data, stable reference material |
insight |
Patterns, realizations, conclusions drawn from observations — discovered patterns, analysis findings, non-obvious connections |
correction |
Fixes to prior understanding — misconceptions corrected, outdated assumptions updated, "we thought X but actually Y" |
conversation |
Session summaries, notable exchanges — important discussions, decision-making contexts, how conclusions were reached |
reference |
Source material, citations, external documentation — RFCs, spec links, documentation URLs, quoted external sources |
task |
Work items, action items, todos — pending work, follow-ups, investigation needs, deferred decisions |
strategy |
Approaches, methods, plans — problem-solving approaches, workflow preferences, codebase mental models, repeatable processes |
checkpoint |
Milestone states, progress markers — project progress snapshots, phase completions, handoff points |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 244 lines · 77 tokens per session scan B 437ffb6b27b0
penfield is a skill published in the GitHub repository penfieldlabs/claude-penfield (2 stars, last pushed 7mo ago), licensed MIT. It adds 77 tokens to every session and 2,297 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
memory-protocol
A shared set of rules for using a persistent memory service across coding-agent sessions.
knowledge-wiki
A tool that turns useful lessons from coding sessions into general articles for a team wiki. A wiki is a collection of linked reference pages, and this one can be kept locally or shared through a Git repository.