plur-ai PLUR Session End
Run this at the end of a conversation to extract what is worth remembering.
PLUR stores durable knowledge — corrections, conventions, preferences, decisions. It does not store session logs, one-off status, or anything that will be irrelevant next week. This skill enforces that discipline.
When to Use
- The user says "wrap up", "end session", "we're done", or similar
- The conversation is winding down after completing a task
- You want to record what you learned before the context window closes
Procedure
Step 1 — Identify learning candidates
Scan the conversation for:
| Category | Examples |
|---|---|
| Corrections | "No, the API returns snake_case" / "Don't use X, use Y" |
| Preferences | "Always run lint before committing" / "User prefers concise explanations" |
| Codebase conventions | "This project uses repository pattern" / "Tests live in __tests__/" |
| Decisions | "We chose PostgreSQL for ACID compliance" |
| Gotchas | "The staging env needs NODE_ENV=staging, not production" |
| Terminology | "They call it a 'slot' not a 'channel'" |
Step 2 — Filter ruthlessly
Skip:
- Anything already obvious from reading the codebase
- Session-specific state ("we're working on the login page right now")
- One-off status ("the build was red this morning")
- Anything you are not confident about
- Secrets, credentials, API keys — never
Step 3 — Write each learning as a durable assertion
Format: a single clear statement that will make sense to a future agent with no session context.
Good: "The publish script requires npm 2FA — always run 'npm publish' interactively, never from CI."
Bad: "We talked about the publish issue."
Step 4 — Assign metadata
For each learning, determine:
type:correction|preference|convention|decision|gotcha|factdomain: the project, library, or topic area (e.g.,"plur","typescript","project:acme")scope: the scope of applicability (e.g.,"global","project:acme")tags: 2–5 descriptive tags
Step 5 — Save
If plur_learn is available:
plur_learn(statement, { type, domain, scope, tags })
Call once per learning. Do not batch into one call — separate engrams decay and strengthen independently.
If plur_session_end is available, pass learnings as engram_suggestions for batch review.
Quality bar
Fewer, stronger engrams beat many weak ones.
- Prefer no engram over a vague one
- One sentence per engram — if you need two sentences, split it into two engrams
- If you're unsure whether something is reusable, skip it
What a good wrap-up looks like
Session learnings saved (3 engrams):
1. [correction] The API returns timestamps in Unix seconds, not milliseconds.
domain: project:acme | tags: api, timestamps
2. [preference] User prefers TypeScript strict mode — always enable in tsconfig.
domain: typescript | tags: tsconfig, preferences
3. [gotcha] The staging deploy requires a manual cache bust at /admin/cache.
domain: project:acme | tags: deploy, staging, ops
Keep the user-facing summary short. Show what you saved; do not narrate your reasoning.
Integration with plur-memory
This skill pairs with plur-memory:
plur-memoryruns continuously — it injects relevant engrams at the start of each turnplur-session-endruns once — it extracts and saves what the session produced
Together they close the memory loop: inject at start, learn at end.