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/weekly-review/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/weekly-review)<a href="https://agentmods.dev/skills/onurpolat05/alba/weekly-review"><img src="https://agentmods.dev/badge/skills/onurpolat05/alba/weekly-review/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/weekly-review"><img src="https://agentmods.dev/badge/skills/onurpolat05/alba/weekly-review.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 | $0.00111 | $0.00834 |
| Opus 5.5 | $0.00044 | $0.00334 |
| Sonnet 5.5 | $0.00022 | $0.00167 |
| Haiku 4.5 | $0.00011 | $0.00083 |
Grade A, and why
weekly-review 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/weekly-review - Weekly Performance Review
Structured weekly review: gather data, present summary, ask questions, save review.
Process
1. Gather Data (silent - don't show raw data)
Read these files:
memory/state/dashboard.md- current priorities and statusmemory/state/todo.md- task completionmemory/knowledge/learnings.md- this week's entries (by date)memory/knowledge/errors.md- this week's errors (by date)memory/daily/- scan for this week's session logs (Mon-Sun)
Extract:
- Tasks completed: count
[x]items from dailies + todo - Tasks added: count new
[ ]items - Learnings recorded this week
- Errors encountered and resolved
- Open blockers from dashboard
2. Present Draft
Show a concise draft:
Week Review: YYYY-Www (Mon DD - Sun DD)
Completed: X tasks
- [top 3-5 completed items]
Learnings: Y new
- [list if any]
Errors: Z encountered, W resolved
- [list if any]
Open Blockers:
- [from dashboard, if any]
3. Ask 3 Questions (one at a time)
Q1: "Does this look right? Anything to add or fix?" Q2: "What went well this week?" Q3: "Top 3 priorities for next week?"
Keep questions short. Accept brief answers.
4. Save Review
Write to memory/state/weekly-reviews/YYYY-Www.md:
# Week YYYY-Www
## Summary
- Days active: [count from daily logs]
- Tasks completed: [count]
- New learnings: [count]
## Completed
- [Items from draft + Q1 corrections]
## What Went Well
- [From Q2]
## Learnings
- [This week's entries]
## Blockers
- [Open items]
## Next Week Priorities
1. [From Q3]
2. [From Q3]
3. [From Q3]
5. Update Dashboard
- Replace/update priority section in
memory/state/dashboard.mdwith next week's priorities from Q3 - Keep existing project info, only update priorities
6. Archive Check
If memory/state/weekly-reviews/ has more than 4 review files:
- Suggest: "You have [N] weekly reviews. Archive older ones? (I'll move them to memory/state/weekly-reviews/archive/)"
- Only archive if user agrees
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 · 118 lines · 111 tokens per session scan A 4a2520f15fe3
weekly-review is a skill published in the GitHub repository onurpolat05/ALBA (23 stars, last pushed 3mo ago), licensed MIT. It adds 111 tokens to every session and 834 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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