big-review
Skill84 starsChanged 14 days agoMITView on GitHub
Use once a month, or when the platform feels slow, heavy or cluttered, to review the whole platform from the query logs and the repo, and propose improvements with effort and risk.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- martin2844/skillstheir own version, first seen Jun 10, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- martin2844/skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 37
Never changes anything: it proposes, the data engineer decides.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Big review
The job: one review of the whole platform, from what the consumers use to what nobody uses, every proposal with its effort and risk. It changes nothing.
Input: the period, the last month by default, with at least 90 days of query logs for the unused data.
Steps
- Usage: the most read tables, the slowest queries, and the rows scanned against the rows needed.
- Unused data: the tables and columns nobody read in the last 90 days, checked against the month-end, quarter-end and year-end jobs, the stale tables, and the storage they take.
- Dead code: the functions, parameters and SQL that nothing uses.
- Code health: duplicated logic, outdated documents and the longest functions.
- Data quality: the checks per layer, and the layers without any.
- Proposals: each one with the effort and the risk, in order of priority. A table is deprecated first and dropped only after its owner agrees.
- Give the result as one web page.
Output
One HTML file, like references/big-review.html: the numbers on top, one section per step with a chart or a table, the proposals last. Read it for the layout before you write the page.
Reads
- the query logs, the catalog, the pipeline code, the documents
Writes
- the web page only
Never
- Never changes anything: it proposes, the data engineer decides.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.