# Bullpen Learn

> Use this skill after a bullpen agent completes a task to extract 0-3 durable learnings (decisions, patterns, preferences, failures, or code snippets) and write them to the project's local memory store (`<project>/.bullpen/memory.json`) under that agent's namespace. The matching Intern agent runs this. Keep each learning self-contained and project-scoped.

- Skill: `manavarya09/bullpen-learn` (Agent Skill)
- Install (CLI): `npx skillmds@latest add manavarya09/bullpen-learn`
- Raw SKILL.md: https://api.skillmd.com/api/skills/manavarya09/bullpen-learn/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: manavarya09 (https://skillmd.com/u/manavarya09)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/manavarya09/bullpen-learn

---


# bullpen-learn

This skill is invoked by the matching Intern after a senior bullpen agent finishes a task. It extracts durable learnings and persists them so future sessions can recall them via `bullpen-memory`.

## When to use

After every senior agent task completes successfully OR fails informatively. Triggered by the `post-agent.sh` hook enqueueing the (`agent_id`, `timestamp`, `status`) tuple.

Skip if:
- The senior agent's output was trivially short (under ~200 chars) — nothing durable to learn.
- The task was a pure read (`/bullpen-knowledge`, etc.) — no new learning.
- An identical learning already exists in the namespace (deduplicate by exact text).

## Intern routing

Pick the right Intern based on the senior agent's department:

| Senior Department | Logging Intern | Namespace |
|---|---|---|
| Frontend, Mobile, CSS, UI Designer, Brand Designer | Pip (frontend-intern) | senior's role |
| Backend, API, Node, Python, Microservices, Infra (all), QA, Test Auto, Performance, Game Dev, Graphics, Hardware, IoT, AR/VR, Blockchain | Ash (backend-intern) | senior's role |
| Product Designer, UX Designer, UX Researcher, Game Designer | Wren (design-intern) | senior's role |
| DBA, Data Engineer, Data Scientist, ML, AI/LLM | Skye (data-intern) | senior's role |
| Marketing (all), Sales (all) | Lex (marketing-intern) | senior's role |
| Leadership, PM, EM, CEO, CTO, Operations, Legal, Finance, HR, R&D, Solutions Architect, Tech Writer, Code Reviewer, Coach | (no intern persona) | `bullpen-shared` |

## What to extract

Read the senior's task summary + output. Identify 0-3 **durable** facts that would help the same agent next time. Each gets a `type`:

- **decision** — a choice the user made between alternatives. *"User chose Postgres over MySQL because of JSONB."*
- **pattern** — a code or design convention now established in the project. *"All forms use `useFormHook` wrapper."*
- **preference** — a personal preference of the user. *"User prefers Tailwind over CSS modules."*
- **failure** — a thing that didn't work and should be avoided. *"Tried Drizzle migrations — user reverted, prefers Prisma."*
- **snippet** — a short, reusable code excerpt (≤500 chars) tied to context.

**Skip non-durable noise**: one-off task descriptions, conversational chatter, questions the user asked, generic best-practice advice not specific to this project.

## How to write

Memory lives at `<project root>/.bullpen/memory.json` — per-project, plain JSON, no daemons. Use the bundled memory module:

```bash
node ${CLAUDE_PLUGIN_ROOT}/scripts/memory.js upsert <namespace> '<json-record>'
```

Record shape:

```json
{
  "id": "<uuid>",
  "text": "<the learning, natural language, complete sentence>",
  "type": "decision | pattern | preference | failure | snippet",
  "agent_role": "<senior_role>",
  "project_path": "<absolute path>",
  "created_at": "<ISO 8601>",
  "session_id": "<session id>",
  "ref_files": "<comma-separated list of files referenced, may be empty>"
}
```

The store auto-creates `<project>/.bullpen/memory.json` on first write and adds `.bullpen/` to `.gitignore` so memory stays private by default. Users can manually `git add .bullpen/memory.json` if they want team-shared learnings.

## After writing

If at least one learning was written, emit a short stderr line that the status-line script can pick up briefly:

```
(•) Pip logged 2 learnings
```

(Replace `Pip` with the intern's persona name and the count with the actual number.)

## Rules

- Each learning is one complete sentence. No bullet lists, no nested structure.
- Project-scope every learning by setting `project_path`. The reader filters on this first.
- Idempotency: if the new learning's text is already present (case-insensitive exact match) in the namespace, skip the upsert.
- Never log secrets, API keys, full file contents, or PII. The Intern's system prompt enforces this.
- Cap output at 3 learnings per task even if more are tempting — quality over quantity keeps retrieval signal high.