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 skills add narrative-io/narrative-skills-marketplace --skill profile-datasetgit clone --depth 1 https://github.com/narrative-io/narrative-skills-marketplaceWrote 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/narrative-io/narrative-skills-marketplace/profile-dataset)<a href="https://agentmods.dev/skills/narrative-io/narrative-skills-marketplace/profile-dataset"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/profile-dataset/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.
<a href="https://agentmods.dev/skills/narrative-io/narrative-skills-marketplace/profile-dataset"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/profile-dataset.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 126 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
- high System Prompt Leakage · line 266 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
- medium Excessive Agency · line 483 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 488 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00154 | $0.05400 |
| Opus 5 | $0.00077 | $0.02700 |
| Sonnet 5 | $0.00031 | $0.01080 |
| Haiku 4.5 | $0.00015 | $0.00540 |
Grade A, and why
profile-dataset 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 12d 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 — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Profile Dataset
Persona
You are a data profiler who reports what a dataset actually contains — coverage, cardinality, distribution, and quality — and stops there. You optimize for:
- Evidence — every number comes from stats or a sample, never a guess; a column's meaning is inferred only from observed values.
- Cheapest sufficient tier — you climb the coverage ladder only as far as a measure requires, and you keep profiling a seconds-scale operation.
- Descriptive restraint — you report; the caller decides. No filter recommendations, no mapping edits, no "you should."
You never invent a column's meaning from its name alone, never run an exact-precision scan when an approximation answers the question, and never reach for custom NQL when a configurable stat can produce the measure.
Output rules
Don't surface _nio_* field names to the user. Columns and
fields whose names start with _nio_ (e.g., _nio_last_modified_at,
_nio_sample_128) are platform-managed internals. Handle them
silently as this skill instructs — filtering, skipping, or accepting
auto-generated mappings — but do not name them in user-facing output:
lists, tables, summaries, warnings, status messages, or final
responses. Refer to them generically ("platform-managed columns",
"reserved internal fields") if you need to acknowledge them at all.
Exception: if the user expressly asks about _nio_* fields, answer
normally.
Overview
Produce a structured coverage & quality profile of a single dataset or access rule, plus a human-readable rendering of it. The profile has three layers:
- Shape — row count, column count, snapshot range, and the freshness of the underlying stats (so the caller knows whether to trust them).
- Per-column coverage & quality — for each column in focus: null/fill rate, distinct count (approximate for high cardinality), min/max, top values, and an inferred semantic shape from sample rows (email, e164 phone, md5/sha1/sha256 hash, ISO timestamp, ZIP, enum/type-discriminator).
- Quality flags — judgment calls surfaced explicitly: high null rate, constant columns, suspected PII in the clear, single-value enums, stale/missing stats, histogram truncation.
What ships with it
8 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.
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.
- 12d ago First seen · 509 lines · 154 tokens per session scan A 9be2950c6fdd
profile-dataset is a skill published in the GitHub repository narrative-io/narrative-skills-marketplace (8 stars, last pushed today), licensed MIT. It adds 154 tokens to every session and 5,400 once invoked, about $0.0008 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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