actor-profiling
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Understand who the user is — background, resources, constraints, and deep motivations. Produces an ActorProfile that informs all downstream decisions. Use this tactic at the start of any crystallization process to build a model of the user's capabilities, limitations, and intent.
In this repository32 other files it gives its agents.
- and-or-decomposeSkill
- ask-constraintsSkill
- ask-decomposition-validationSkill
- ask-intentionalitySkill
- 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 36
When the user doesn't know what they want or can do, the ActorProfile captures boundaries rather than commitments:
- Distinctive: line 45
After running the SOPs you deem necessary, synthesize an ActorProfile:
≈ “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
Actor Profiling
Build a comprehensive model of the user as a research actor — who they are, what they have, what constrains them, and why they're doing this.
Available SOPs
| SOP | Purpose | Execution |
| explore-resume | Background, skills, projects, publications, research experience | dialogue (once only) |
| clarify-resources | Compute, timeline, collaboration, data, environment | dialogue |
| ask-constraints | Venue targets, methodology preferences, avoidance areas, advisor requirements | dialogue |
| ask-intentionality | Deep WHY probing — motivation, risk tolerance, innovation preference, etc. | dialogue |
Methodology Guidance
The goal is to construct an ActorProfile with enough information to inform field exploration and goal decomposition. How you get there is your decision.
Typical flow:
explore-resumefirst (one-time, never re-run)clarify-resources→ask-constraints→ask-intentionality
But you may:
- Return to
ask-intentionalityat any point when you discover a deeper WHY to probe - Interleave
clarify-resourceswhen intentionality probing reveals resource-related gaps - Skip or abbreviate SOPs when the user's initial message already provides the information
End condition: You judge that you have enough information to construct a meaningful ActorProfile. In cold-start scenarios, "enough" may mean just establishing boundaries (what the user won't do) rather than specifics.
Cold-Start Special Case
When the user doesn't know what they want or can do, the ActorProfile captures boundaries rather than commitments:
- "User has experience in NLP and GNN, won't jump to physics/chemistry"
- "Timeline is flexible, no hard deadline"
- "Motivated by interest, not graduation pressure"
This is sufficient — later tactics will help narrow within these boundaries.
Output (Tactic-Level Aggregation)
After running the SOPs you deem necessary, synthesize an ActorProfile:
ActorProfile {
background: { skills, projects, publications, researchExp }
resources: { compute, timeline, collaboration, data, environment }
constraints: { venue, methodology, avoidance, advisor }
intentionality: {
motivation, successDefinition,
riskTolerance, innovationPreference,
independencePreference, timeUrgency, learningWillingness
}
boundary: "..." // what the user definitely won't do
}This is a conceptual schema, not a JSON requirement. Express it in whatever format serves the downstream context best.
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.
- 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.

