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.
git clone --depth 1 https://github.com/rianvdm/product-ai-publicWrote 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/commands/rianvdm/product-ai-public/session-end)<a href="https://agentmods.dev/commands/rianvdm/product-ai-public/session-end"><img src="https://agentmods.dev/badge/commands/rianvdm/product-ai-public/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/commands/rianvdm/product-ai-public/session-end"><img src="https://agentmods.dev/badge/commands/rianvdm/product-ai-public/session-end.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00012 | $0.01931 |
| Opus 5 | $0.00006 | $0.00966 |
| Sonnet 5 | $0.00002 | $0.00386 |
| Haiku 4.5 | $0.00001 | $0.00193 |
Grade A, and why
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 2d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session End
Write a handoff note to the appropriate session log so the next session can pick up where this one left off.
Two session logs exist:
01-context/session-log-work.md— Amazon work (Data Platform, DevTools, escalations, Exponent, internal tools)01-context/session-log-personal.md— Personal/side projects (Discrobble, discogs-mcp, ListenToMore, tldl, blog posts, gaming)
Your Task
$ARGUMENTS
Instructions
1. Assess the Session
Review what happened in this conversation. Only write a handoff note if the session involved substantive work — decisions made, problems investigated, documents created, or significant context built up. Skip if the session was just a quick question or trivial task.
If the session wasn't substantive enough for a handoff note, say so and end.
Admission quality check: For each piece of information you'd include in the handoff note, ask:
- Novel? Does this extend or contradict something already in the system (skills, commands, stable-facts)? If it's already baked in, don't re-store it.
- Actionable? Can a future session use this? Debugging dead-ends and one-off lookups usually aren't worth recording.
- Durable? Will this matter in 2 weeks? Open threads and decisions yes; transient investigation details usually no.
Use judgment — these are quality filters, not hard gates. The goal is to keep the session log high-signal.
2. Draft the Entry
Determine which session log this entry belongs in based on the session topic:
- Work →
01-context/session-log-work.md - Personal →
01-context/session-log-personal.md
Write a new H2 entry to prepend to the chosen file (newest first). Use this exact format:
## YYYY-MM-DD — [Brief topic description]
**What happened:** [1-3 sentences summarizing what was done. Be specific — mention file paths, ticket IDs, project names.]
**Decisions:** [Any decisions made during the session. Reference where they were documented if applicable.]
**Learned:** [Anything discovered that should persist — debugging insights, preferences expressed, corrections needed, approach that worked/didn't work. If nothing notable, omit this field.]
**Open threads:** [What's unfinished or needs follow-up. If nothing, omit this field.]
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.
- 2d ago First seen · 145 lines · 12 tokens per session scan A e6ed9a201ae4
session-end is a command published in the GitHub repository rianvdm/product-ai-public (15 stars, last pushed 2d ago), licensed MIT. It adds 12 tokens to every session and 1,931 once invoked, about $0.0001 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 2026-09-09.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.