actor-profiling

A user-profile building method that records a person’s background, resources, constraints, and motivations for research or project decisions.

In plain words
What is it for?
Use it at the start of a research-planning process to understand experience, resources, requirements, preferences, and reasons for pursuing the work.
Why use it?
It prevents later plans from ignoring the user’s skills, limits, goals, or available support.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/yogsoth-ai/de-anthropocentric-research-engine/actor-profiling
Any agent
npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill actor-profiling
Clone the repo
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine

Made for: Claude Code, Codex.

Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 875 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00057 $0.00875
Opus 5 $0.00028 $0.00438
Sonnet 5 $0.00011 $0.00175
Haiku 4.5 $0.00006 $0.00088

Measured yesterday against content hash 946343b4c07e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

actor-profiling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/actor-profiling/SKILL.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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-resourcesask-constraintsask-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
}

Read the full file on GitHub · 86 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 86 lines · 57 tokens per session scan A 946343b4c07e

Subscribe to this mod's changes

actor-profiling is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (405 stars, last pushed 6d ago), licensed Apache-2.0. It adds 57 tokens to every session and 875 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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