ask-intentionality
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Deep WHY probing inspired by i* Intentionality modeling. Understand the user's motivation, success definition, risk tolerance, innovation preference, independence preference, time urgency, and learning willingness. The most important SOP in actor-profiling — understanding WHY drives everything downstream.
In this repository32 other files it gives its agents.
- actor-profilingSkill
- and-or-decomposeSkill
- ask-constraintsSkill
- ask-decomposition-validationSkill
- ask-obstacle-acceptanceSkill
- assess-obstacle-severitySkill
- broad-paper-searchSkill
- broad-web-searchSkill
- clarify-resourcesSkill
- cold-startSkill
- crystallize-north-starSkill
- deep-web-searchSkill
- direction-narrowingSkill
- explore-resumeSkill
- feasibility-checkSkill
- final-validationSkill
- formulate-top-goalSkill
- generate-candidate-fieldsSkill
- generate-research-briefSkill
- goal-decompositionSkill
- hot-startSkill
- identify-obstaclesSkill
- landscape-reconnaissanceSkill
- landscape-synthesisSkill
- north-star-crystallizationSkill
- north-star-synthesisSkill
- obstacle-analysisSkill
- present-and-askSkill
- present-candidatesSkill
- propose-mitigationsSkill
- validate-leavesSkill
- warm-startSkill
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 17
If user has a stated goal: Why this specific goal?
- Distinctive: line 30
Don't ask all dimensions at once — probe naturally based on conversation flow.
≈ “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
Ask Intentionality
Deep WHY probing (i* Intentionality). Understanding motivation shapes every downstream decision.
Execution
Dialogue — inline, no subagent.
What to Ask About
- If user has a stated goal: Why this specific goal? What's driving it?
- Motivation depth: graduation requirement / pure interest / career advancement / impact / combination
- Success definition: What does "done well" look like to you?
- Risk tolerance: safe incremental improvement vs. high-risk high-reward breakthrough
- Innovation preference: improve existing methods vs. create entirely new approaches
- Independence preference: complete solo vs. depend on collaborators
- Time urgency: deadline-driven vs. long-term accumulation
- Learning willingness: use familiar tools/domains vs. learn new ones
Key Behaviors
- User may decline to answer (privacy). Accept gracefully. Note that downstream work becomes broader and may require more iteration.
- This SOP can be re-invoked by the tactic whenever new WHY questions emerge.
- Don't ask all dimensions at once — probe naturally based on conversation flow.
Output
Intentionality section of ActorProfile.
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.