Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/matteotitta/genesys-skills/session-wrap)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/session-wrap"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/session-wrap/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/matteotitta/genesys-skills/session-wrap"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/session-wrap.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 | $0.00118 | $0.02438 |
| Opus 5 | $0.00059 | $0.01219 |
| Sonnet 5 | $0.00024 | $0.00488 |
| Haiku 4.5 | $0.00012 | $0.00244 |
Grade A, and why
session-wrap 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 11d 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/session-wrap — Extract learnings and close the feedback loop
Run at the end of a work session to capture what was learned, decided, and built. Prevents knowledge loss between sessions and creates a compounding improvement loop for the skill system.
Claude Code Triggers
Invoke this skill when user says:
- "/session-wrap"
- "Wrap up"
- "Session done"
- "Log learnings"
- "What did we learn today?"
- "Close out this session"
Do NOT invoke when:
- User wants to search past sessions (use
/recall) - User wants a daily summary (use
/today) - Mid-session — this is an end-of-session skill
Process
Step 1: Scan for corrections
Review the conversation for moments where the user corrected you or redirected your approach. These are the highest-value learnings.
Look for patterns like:
- "No, that's wrong" / "Actually..." / "Don't do that"
- User rejecting a tool call or approach
- User providing a better way to do something
- Repeated mistakes that suggest a missing rule
Output format:
### Corrections found
- [CORRECTION] {what was wrong} → {what's correct}
Suggested rule: {rule to prevent recurrence}
Scope: [LOCAL] or [GLOBAL]
Step 2: Scan for decisions
Extract decisions made during the session — direction changes, approach selections, scope choices.
Look for patterns like:
- "Let's go with..." / "We'll use..." / "The approach is..."
- Choosing between alternatives
- Committing to a specific implementation
- Client-facing decisions (positioning choices, messaging direction, etc.)
Output format:
### Decisions made
- [DECISION] {what was decided}
Context: {why it was decided}
Client: {client name if applicable}
Step 3: Scan for patterns
Identify workflow patterns that emerged — things that worked well, processes that could become skills or rules.
Look for patterns like:
- Multi-step processes that were repeated
- Tool combinations that proved effective
- Prompting strategies that produced good results
- Workflow shortcuts discovered
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.
- 11d ago First seen · 272 lines · 118 tokens per session scan A 554579957a83
session-wrap is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 2,438 once invoked, about $0.0006 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 2026-08-30.
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