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 plur-ai/plur --skill plur-session-endgit clone --depth 1 https://github.com/plur-ai/plurWrote 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/plur-ai/plur/plur-session-end)<a href="https://agentmods.dev/skills/plur-ai/plur/plur-session-end"><img src="https://agentmods.dev/badge/skills/plur-ai/plur/plur-session-end/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/plur-ai/plur/plur-session-end"><img src="https://agentmods.dev/badge/skills/plur-ai/plur/plur-session-end.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 51 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00035 | $0.01022 |
| Opus 5.5 | $0.00014 | $0.00409 |
| Sonnet 5.5 | $0.00007 | $0.00204 |
| Haiku 4.5 | $0.00003 | $0.00102 |
Grade A, and why
plur-session-end 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 9d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:behavioral|terminological|procedural|architectural— choose the MCP schema category; correction/preference/gotcha can be tagsdomain: 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
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.
- 9d ago Changed · +2 lines 91a77d78739c
- 1mo ago First seen · 111 lines · 35 tokens per session scan A 5679a2e7e92e
plur-session-end is a skill published in the GitHub repository plur-ai/plur (304 stars, last pushed 8d ago), licensed Apache-2.0. It adds 35 tokens to every session and 1,022 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
ori-memory
Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.
honcho-integration
Integrate Honcho memory into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, and accessing Honcho's representation.
honcho-cli
Inspect and debug Honcho workspaces via the honcho CLI. Use when investigating peer representations, memory state, session context, or dialectic quality — any task that requires introspection of a Honcho deployment, including verifying that a recall/record memory loop is actually working.
verify
Build, launch, and drive a local Honcho stack to verify a change at its runtime surface (the /v3 HTTP API and the deriver queue). Use when verifying a diff or confirming a change works in the running app.
honcho-memory
Gives AI agents persistent memory across conversations using Honcho. Automatically saves and retrieves user context so the AI remembers preferences, history, and facts between sessions. Use when you need the AI to remember past conversations, recall what a user has told it, inject relevant context into prompts, or…
pre-pr
Prepare a Honcho change for a pull request to plastic-labs/honcho. Invoke before opening a PR, when drafting a PR body, when asked if a branch is PR-ready, or when filling the pull request template. Checks the linked issue, required tests and docs, then writes Description / Proofs / Fixes.