plur-ai/plur/plur-session-end

Claude Code and agent skill by plur-ai/plur

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Checking that the SKILL.md file is still in the repository.

Extract durable learnings at the end of a session. Saves corrections, preferences, and codebase patterns as engrams — nothing ephemeral, nothing sensitive.

Install

npx vskill@latest install plur-ai/plur/plur-session-end

SKILL.md

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:

CategoryExamples
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: behavioral | terminological | procedural | architectural — choose the MCP schema category; correction/preference/gotcha can be tags
  • domain: 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

plur_learn and plur_session_end are direct tools in both lean and full MCP profiles. For less-common operations, use plur_admin with { "action": "help" } to discover the current actions and schemas.

Save with plur_learn:

plur_learn({ statement, type, domain, scope, tags })

Call once per learning. Do not batch into one call — separate engrams decay and strengthen independently.

Alternatively, pass unsaved learnings as engram_suggestions to plur_session_end, following its live schema. Do not submit the same learnings through both paths.

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-memory guides recall and learning; automatic injection depends on the installed editor adapter
  • plur-session-end runs once — it extracts and saves what the session produced

Together they close the memory loop: inject at start, learn at end.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
304
Repository forks
37
7-day trend score
27

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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