intent-modeling
Skill2 starsChanged 24 days agoApache-2.0View on GitHub
Use before acting on human instructions: separate what they said from what they meant.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- Abdullokh-N/proza-rutheir own version, first seen Aug 27, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- Abdullokh-N/proza-ru★ 0same 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.
In this repository33 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
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 8
Read for the outcome the human wants, not only the words they used.
- Distinctive: line 20
When intent and output diverge, scan other artifacts for the same pattern.
≈ “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
Intent Modeling
Read for the outcome the human wants, not only the words they used. Use conversation context and prior turns to infer underlying goal. When the goal is ambiguous, ask.
Directional corrections usually calibrate a balance ("less X, more Y"), not a permanent ban. Encode the target balance in what you do next.
Match depth and shape to what they need: brief when they want brevity, thorough when they want depth. A vague request may be a rough draft of the real need. Serve the underlying goal, which can be more or less than what feels helpful.
When intent and output diverge, scan other artifacts for the same pattern. One misread often repeats.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
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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.