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 agentmods add skills/plastic-labs/cursor-honcho/integratenpx skills add plastic-labs/cursor-honcho --skill integrategit clone --depth 1 https://github.com/plastic-labs/cursor-honchoWrote 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/plastic-labs/cursor-honcho/integrate)<a href="https://agentmods.dev/skills/plastic-labs/cursor-honcho/integrate"><img src="https://agentmods.dev/badge/skills/plastic-labs/cursor-honcho/integrate.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.04214 |
| Opus 5 | $0.00031 | $0.02107 |
| Sonnet 5 | $0.00012 | $0.00843 |
| Haiku 4.5 | $0.00006 | $0.00421 |
Grade A, and why
integrate scanned grade A with 0 findings 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 4d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 541 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Honcho Integration Guide
What is Honcho
Honcho is an open source memory library for building stateful agents. It works with any model, framework, or architecture. You send Honcho the messages from your conversations, and custom reasoning models process them in the background — extracting premises, drawing conclusions, and building rich representations of each participant over time. Your agent can then query those representations on-demand ("What does this user care about?", "How technical is this person?") and get grounded, reasoned answers.
The key mental model: Peers are any participant — human or AI. Both are represented the same way. Observation settings (observe_me, observe_others) control which peers Honcho reasons about. Typically you want Honcho to model your users (observe_me=True) but not your AI assistant (observe_me=False). Sessions scope conversations between peers. Messages are the raw data you feed in — Honcho reasons about them asynchronously and stores the results as the peer's representation. No messages means no reasoning means no memory.
Your agent accesses this memory through peer.chat(query) (ask a natural language question, get a reasoned answer), session.context() (get formatted conversation history + representations), or both.
Integration Workflow
Follow these phases in order:
Phase 1: Codebase Exploration
Before asking the user anything, explore the codebase to understand:
- Language & Framework: Is this Python or TypeScript? What frameworks are used (FastAPI, Express, Next.js, etc.)?
- Existing AI/LLM code: Search for existing LLM integrations (OpenAI, Anthropic, LangChain, etc.)
- Entity structure: Identify users, agents, bots, or other entities that interact
- Session/conversation handling: How does the app currently manage conversations?
- Message flow: Where are messages sent/received? What's the request/response cycle?
Use Glob and Grep to find:
**/*.pyor**/*.tsfiles with "openai", "anthropic", "llm", "chat", "message"- User/session models or types
- API routes handling chat or conversation endpoints
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 541 lines · 62 tokens per session scan A abfa83def04b
integrate is a skill published in the GitHub repository plastic-labs/cursor-honcho (6 stars, last pushed 3d ago), licensed MIT. It adds 62 tokens to every session and 4,214 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…