Borrowing it
Nothing to install: this file belongs to onurpolat05/ALBA. 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/onurpolat05/ALBA/main/.claude/skills/end/SKILL.mdgit clone --depth 1 https://github.com/onurpolat05/ALBAWrote 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/onurpolat05/alba/end)<a href="https://agentmods.dev/skills/onurpolat05/alba/end"><img src="https://agentmods.dev/badge/skills/onurpolat05/alba/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/onurpolat05/alba/end"><img src="https://agentmods.dev/badge/skills/onurpolat05/alba/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 | $0.00097 | $0.00787 |
| Opus 5.5 | $0.00039 | $0.00315 |
| Sonnet 5.5 | $0.00019 | $0.00157 |
| Haiku 4.5 | $0.00010 | $0.00079 |
Grade A, and why
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 1mo 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/end - Session End
Close the session with a structured 3-question protocol. Save state for next session.
Steps
1. Ask 3 Questions (one at a time, keep it short)
Q1: "What was completed this session?" (max 3 bullets) Q2: "Any learnings or insights?" (optional - user can skip) Q3: "What's the priority for next session?" (1 sentence)
2. Auto-Update (after answers)
Dashboard (memory/state/dashboard.md):
- Mark completed items as
[x] - Add next priority if mentioned
- Update any status changes
Todo (memory/state/todo.md):
- Mark completed tasks as
[x]
Learnings (memory/knowledge/learnings.md):
- If Q2 had content, append in format:
## [Topic] - YYYY-MM-DD
**Context:** [What prompted this learning]
**Insight:** [The learning itself]
**Application:** [When to apply this]
Errors (memory/knowledge/errors.md):
- Check
memory/knowledge/errors_raw.logfor new entries from this session - If errors were resolved during the session, consolidate them into
errors.md:
## [Error Type] - YYYY-MM-DD
**Pattern:** [What triggers this error]
**Solution:** [How it was resolved]
**Prevention:** [How to avoid it]
Daily Log (memory/daily/YYYY-MM-DD.md):
- Create with session summary:
# YYYY-MM-DD
## Completed
- [From Q1 answers]
## Learnings
- [From Q2, if any]
## Next
- [From Q3]
3. Confirm
Output a 3-line summary:
Session saved.
Completed: [count] items | Learnings: [count]
Next: [priority from Q3]
Rules
- Keep questions SHORT - this is CLI, not a form
- If user says "nothing" or skips Q2, that's fine - don't push
- Don't read the entire dashboard - just update what changed
- If daily log already exists (multiple sessions per day), append to it
- Create memory/daily/ directory if it doesn't exist
- Never fail silently - confirm what was saved
- Respect user's language (follow dashboard/CLAUDE.md language)
Note on Auto-Memory vs ALBA Memory
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.
- 1mo ago First seen · 89 lines · 97 tokens per session scan A 86cd2ec6660a
end is a skill published in the GitHub repository onurpolat05/ALBA (23 stars, last pushed 3mo ago), licensed MIT. It adds 97 tokens to every session and 787 once invoked, about $0.0004 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.
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