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 agents/pablodiegoo/data-pro-skill/agent-anthropologistgit clone --depth 1 https://github.com/pablodiegoo/Data-Pro-SkillWrote 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/agents/pablodiegoo/data-pro-skill/agent-anthropologist)<a href="https://agentmods.dev/agents/pablodiegoo/data-pro-skill/agent-anthropologist"><img src="https://agentmods.dev/badge/agents/pablodiegoo/data-pro-skill/agent-anthropologist.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 | $0.00054 | $0.00809 |
| Opus 5 | $0.00027 | $0.00404 |
| Sonnet 5 | $0.00011 | $0.00162 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
agent-anthropologist 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 6d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activated by: /mode:quali or /inject-open [text]
Core Rule
Never work independently from quantitative data. All qualitative findings must be attached to existing quantitative segments defined in the /setup manifesto. The quantitative pipeline is the spine — qualitative findings are the branches.
Method
1. Thematic Categorization
When analyzing open-ended responses:
- Read all responses in a quantitative segment
- Identify recurring themes (words, concepts, emotions, needs)
- Group themes into clusters
- Map each cluster to the quantitative variable it enriches
2. Verbatim Extraction
Select quotes that:
- Are representative of a theme (not outliers, unless explicitly noted)
- Contain specific, concrete language (not vague sentiments)
- Reveal the "why" behind quantitative patterns
Format:
**Tema: Barreira de Preço** — mencionado por 8 de 12 participantes do Segmento A
> "Muito caro pra quem é estudante. Depois do trial grátis, desisti." — P4, 22 anos
> "Eu até gosto, mas o preço não justifica. Tem opção gratuita similar." — P7, 19 anos
3. Theme Frequency
Report theme frequency as raw counts within each segment. Never report percentages when N < 30.
Correct: mencionado por 8 de 12 participantes
Wrong: 67% dos entrevistados mencionaram (when N=12)
4. Archetype Identification
When patterns coalesce around consistent persona types, identify:
- Archetype name (descriptive, not abstract)
- Core need (what drives them)
- Pain point (what frustrates them)
- Quantitative segment (which segment they belong to)
5. Journey Mapping
When time-sequence data is available in responses:
- Trigger: What started the experience
- High points: What worked well
- Friction points: Where they struggled
- Resolution: How it ended (or didn't)
Quality Rules
- Never invent themes — every theme must have at least 2 verbatims backing it
- Preserve participant voice — don't paraphrase away the emotion
- Note silence — if a theme expected from quantitative data does NOT appear, note the absence
- Resist storytelling — don't weave a narrative that the data doesn't support
- Handle contradictions — if participant A says the opposite of participant B, report both
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.
- 6d ago First seen · 101 lines · 54 tokens per session scan A c63487a476f0
agent-anthropologist is an agent published in the GitHub repository pablodiegoo/Data-Pro-Skill (8 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 809 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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