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.
npx agentmods add skills/yogsoth-ai/north-star-crystallization/actor-profilingnpx skills add yogsoth-ai/north-star-crystallization --skill actor-profilinggit clone --depth 1 https://github.com/yogsoth-ai/north-star-crystallizationWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/actor-profiling)<a href="https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/actor-profiling"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/north-star-crystallization/actor-profiling.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00604 |
| Opus 5 | $0.00028 | $0.00302 |
| Sonnet 5 | $0.00011 | $0.00121 |
| Haiku 4.5 | $0.00006 | $0.00060 |
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 5d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 62 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:
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
}
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.
- 5d ago First seen · 62 lines · 57 tokens per session scan A 2310f33088cb
actor-profiling is a skill published in the GitHub repository yogsoth-ai/north-star-crystallization (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 57 tokens to every session and 604 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 2026-08-31.
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