actor-profiling
The actor-profiling skill systematically maps a research user's background, available resources, constraints, and underlying motivations through structured dialogue to produce an ActorProfile. Use this at the beginning of any research planning process to establish a clear model of who the user is, what they can access, and what drives their work, enabling downstream decisions about feasibility and direction.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/actor-profiling && cp -r /tmp/actor-profiling/skills/actor-profiling ~/.claude/skills/actor-profilingSKILL.md
# 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:**
1. `explore-resume` first (one-time, never re-run)
2. `clarify-resources` → `ask-constraints` → `ask-intentionality`
**But you may:**
- Return to `ask-intentionality` at any point when you discover a deeper WHY to probe
- Interleave `clarify-resources` when 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.Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: 面对异常的最佳解释推理
Remove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems