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 samkawsarani/sams-product-plugins --skill wrap-upgit clone --depth 1 https://github.com/samkawsarani/sams-product-pluginsWrote 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/samkawsarani/sams-product-plugins/wrap-up)<a href="https://agentmods.dev/skills/samkawsarani/sams-product-plugins/wrap-up"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/wrap-up/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/samkawsarani/sams-product-plugins/wrap-up"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/wrap-up.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 | $0.00062 | $0.00674 |
| Opus 5 | $0.00031 | $0.00337 |
| Sonnet 5 | $0.00012 | $0.00135 |
| Haiku 4.5 | $0.00006 | $0.00067 |
Grade A, and why
wrap-up 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 12d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your Task
Run the session wrap-up for this product OS workspace. Takes 2-3 minutes. Ensures learnings persist.
Announce at start: "Running wrap-up."
Step 1: Identify touched domains
Based on this session's work, identify which domains under knowledge/domains/ were relevant. If unclear, infer from session context. List the domains identified before proceeding.
Step 2: Hypothesis scan
For each touched domain, read hypotheses.md. For each non-retired hypothesis:
- Confirming evidence this session? → Increment
Confirmationscount, add inline dated note:*(YYYY-MM-DD: [source/reason])* - Contradicting evidence? → Increment
Contradictionscount, add inline dated note - 3+ confirmations? → Surface to Sam: "H[N] in [domain] has 3 confirmations. Proposed move to
knowledge.md: [draft text]. Approve?"
Never auto-promote. Sam approves all promotions.
Step 3: New knowledge
Did this session surface new confirmed facts or rules not already in knowledge.md?
Rule of thumb — fact vs hypothesis: If it requires future validation to know if it's true, it's a hypothesis (goes to hypotheses.md). If it's already evidenced in this session — a decision was made, a number was stated, a process was confirmed — it's a fact (goes to knowledge.md).
- New fact → append under
## What we know (facts)with*(added YYYY-MM-DD)*tag if time-sensitive - New confirmed rule → append under
## Rules (apply by default)with Confirmed by + Apply when - Cross-domain rule? → Check if it belongs in
knowledge/domains/shared.mdinstead
Step 4: Corrections
Did Sam correct the agent on anything this session?
- Agent behavior correction → update relevant
AGENTS.md(root orknowledge/) with the corrected behavior - Domain fact correction → update the relevant
knowledge.mdentry - Fact about Sam → update
knowledge/about-me/about-me.md
Step 5: Housekeeping
- Update
*Last updated: YYYY-MM-DD*header on any modified files - Flag (don't fix) any domain where
*Last updated:*is older than 90 days: "Note: [domain]/knowledge.md last updated YYYY-MM-DD — may be worth a review"
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.
- 12d ago First seen · 71 lines · 62 tokens per session scan A 8a586d99f6f7
wrap-up is a skill published in the GitHub repository samkawsarani/sams-product-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 674 once invoked, about $0.0003 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 2026-08-31.
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