Borrowing it
Nothing to install: this file belongs to strikersam/autonomous-ai-agency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/strikersam/autonomous-ai-agency/master/.agents/skills/wrap-up/SKILL.mdgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/wrap-up)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/wrap-up"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/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/strikersam/autonomous-ai-agency/wrap-up"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/wrap-up.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.00972 |
| Opus 5 | $0.00019 | $0.00486 |
| Sonnet 5 | $0.00008 | $0.00194 |
| Haiku 4.5 | $0.00004 | $0.00097 |
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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: wrap-up
When to Use
Run at the end of every coding session — whether work is complete or paused. This ritual ensures nothing is lost, lessons are captured, and the next session can start immediately without archaeology.
The 5-Step Wrap-Up Ritual
Step 1 — Changes Audit
Review everything that happened this session:
git status # uncommitted changes
git diff --stat HEAD # what changed vs last commit
git log --oneline -10 # commits made this session
Check for:
- Uncommitted changes that should be committed (use
smart-commit) - Uncommitted changes that should be discarded
- TODOs left in code (
grep -r "TODO\|FIXME\|HACK" --include="*.py" .) - Any
.claude/state/files that need updating
Step 2 — Quality Check
pytest -x # tests must pass
python -m py_compile proxy.py # syntax check main file
If tests fail: either fix now or document the failure in NEXT_ACTION.md as a blocker.
Do not leave the session with silently broken tests.
Step 3 — Learning Capture
For each meaningful correction, mistake, or discovery this session, append to
.claude/state/learnings.md:
## <date> — <short title>
**Context:** What was being worked on.
**Mistake / Discovery:** What went wrong or what was learned.
**Correction:** What the right approach is.
**Pattern:** One-line rule to remember (e.g., "Always read router/CLAUDE.md before touching model_router.py").
Create the file if it doesn't exist. Even 1 learning per session compounds over time.
Step 4 — Next Session Planning
Update .claude/state/NEXT_ACTION.md with a clear resume guide:
# Next Action
**Objective:** <what are we building?>
**Status:** <COMPLETE | IN PROGRESS | BLOCKED>
**Last completed:** <step or task just finished>
## If Resuming: Start Here
1. Read: <file to read first>
2. Run: `pytest -x` to confirm baseline
3. Then: <exact next action>
## Blockers
- <any known blockers or dependencies>
## Optional Next Steps
- <lower priority work to do after the main task>
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 · 142 lines · 38 tokens per session scan A 31fc93ae02b3
wrap-up is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 972 once invoked, about $0.0002 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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