A research role that explains the real-world background behind a dataset. It identifies the subject, data source, collection purpose, established knowledge, related studies, and why the data matters.

Use it to research a dataset’s domain, origin, history, related research, and relevant background, then save the findings with source links in a structured file.

Numbers are difficult to interpret without knowing what they measure and where they came from. This role supplies that context before analysis begins.

detective — Skill for Claude Code by QinghongLin

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.

Any agent

npx skills add QinghongLin/data2story-skill --skill detective

Clone the repo

git clone --depth 1 https://github.com/QinghongLin/data2story-skill

Made for: Claude Code.

Or install data2story-pro, the plugin that ships this one along with the rest of its 25 skills.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/detective/github.svg)](https://agentmods.dev/skills/qinghonglin/data2story-skill/detective)

Your own site

<a href="https://agentmods.dev/skills/qinghonglin/data2story-skill/detective"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/detective/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/qinghonglin/data2story-skill/detective"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/detective.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,378 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00041	$0.03378
Opus 5	$0.00020	$0.01689
Sonnet 5	$0.00008	$0.00676
Haiku 4.5	$0.00004	$0.00338

Measured 10d ago against content hash cffb5363ee5b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

detective scanned grade A with 1 finding 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 10d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (examples/fetch_hle_images.py, examples/fetch_venue_weather.py, scripts/fetch_flags.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

**Mandatory for `spotify` and `youtube` instances:** opaque IDs cannot be recalled reliably. Source every ID from a real page (never from memory) and verify it with oEmbed before writing — see **[`references/instance_ver

skills/data2story-pro/detective/SKILL.md · 154 lines

How it starts

The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Detective

Your job is context. Before anyone touches the numbers, you find out what world those numbers live in.

You are not analyzing the data. You are answering: what does a smart, curious reader need to know to make sense of this data? What happened in the real world that explains what's in this dataset?

Setup

DATA_DIR = first argument
PROJECT_DIR = second argument
Quickly read the data files in DATA_DIR to understand the topic (column names, a few rows) — do not analyze
Output: PROJECT_DIR/detective.json

Steps

1. Identify the Domain

From a quick scan of the data, determine:

What subject area is this? (psychology, sports, ecology, economics, etc.)
Who collected this data and why?
What real-world phenomenon is being measured?

2. Research Background

Search for external context relevant to this dataset. Look for:

Origin: Who created this data, when, and for what purpose? Link to the original study or source.
Domain knowledge: What does the field already know about this topic? What are the established findings?
Related work: Are there other studies, datasets, or analyses on the same topic? What did they find?
Why it matters: What is the real-world significance? Why would a general reader care?
Controversies or debates: Are there contested interpretations, known limitations, or ongoing debates in this area?

3. Identify Interpretive Hooks

Flag anything from your research that could:

Provide surprising context for what the data shows
Explain an anomaly the analyst might find
Connect the data to something readers already know about
Change how a finding should be interpreted

4. Collect Reference Media (a default step for visual/geographic/event/sports datasets)

Real-world media helps the Designer build a multimedia-rich page, so collecting it is a default part of your job, not optional, for visual, geographic, event-based, cultural, historical, product, animal, art, place, food, fashion, sports, and scientific datasets. Use the helper scripts in this skill's scripts/ folder — fetch_images.py, fetch_logos.py, fetch_flags.py — to pull real Wikimedia/Commons photos, crests and flags, and record each in reference_media (prefer real photos over AI for concrete subjects). For music/sport/art/event datasets, also collect 3-8 embeddable instances (verified per references/instance_verification.json). Only for abstract, text-only, technical, privacy-sensitive, or purely statistical datasets may you collect little or none — and then record why, so the Designer knows the omission is intentional.

Read the full file on GitHub · 154 lines

Files

What ships with it

12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

10d ago First seen · 154 lines · 41 tokens per session scan A cffb5363ee5b

Subscribe to this mod's changes

detective is a skill published in the GitHub repository QinghongLin/data2story-skill (155 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 3,378 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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