brand-probe
Skill0 starsChanged 6 days agoMITView on GitHub
Probe a brand book against a control run. Use when a brand book must be tested before acceptance or after a change, or someone asks whether it works.
In this repository6 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- brand-bookSkill
- brand-directionsSkill
- brandfileSkill
- brand-interviewSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 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.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 12
Run node brand/tools/brand.mjs status.
- Very specific: line 16
If probes/ in the book's folder already holds tasks, keep them, so versions stay comparable.
- Distinctive: line 20
Add one gap task: something real the book does not cover.
≈ “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
Brand Probe
Find out what the book changes in an agent's work, by comparing each real task done with the book against a control done without it. Done when each task has its runs and a report.md with the current book-hash.
You observe: leave the book and the runs as they are, because a run that went badly is the finding and brand-book makes the fixes.
Run node brand/tools/brand.mjs status. Test brand/work/candidate/ if it exists, otherwise brand/, and note the book-hash it prints. If the candidate has errors, hand off to brand-book; if there is no book, to brandfile.
Tasks
If probes/ in the book's folder already holds tasks, keep them, so versions stay comparable. Where a report is missing or carries another hash, rerun the with-book run and the report; the control does not depend on the book.
Otherwise take two or three real tasks from "Readers and work" in brand/work/brief.md, or from {#start} in the book once the brief is archived; if neither names one, ask the person. Word each as they would ask for it, with no brand rules in it, and pick tasks whose result is a page or a text.
Add one gap task: something real the book does not cover. It shows whether an agent says the book has no answer or fills the gap with fashion.
Save each as probes/<task-id>/task.md in the book's folder.
Runs
You have read the book and the brief, so give every run to a new subagent with its run brief. Without subagents, ask the person to paste each brief into a new session, and continue once the files exist.
Each real task runs twice: with the book and as the control. The gap task runs only with the book. The folders are not sealed, so each run replies with the files it opened: run again any run that opened a file its brief does not allow.
Report
For each task, a new subagent writes probes/<task-id>/report.md from the report brief.
Hand back
Run node brand/tools/brand.mjs build so the runs appear side by side on the page. For a candidate, hand off to brand-book. For an accepted book, tell the person what strayed and what the book changed, and name the page. Report only what you saw: a few tasks on one brand catch failures and prove nothing general.
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.

