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 oxbshw/LLM-Agents-Ecosystem-Handbook --skill dataset-profilergit clone --depth 1 https://github.com/oxbshw/LLM-Agents-Ecosystem-HandbookWrote 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/oxbshw/llm-agents-ecosystem-handbook/dataset-profiler)<a href="https://agentmods.dev/skills/oxbshw/llm-agents-ecosystem-handbook/dataset-profiler"><img src="https://agentmods.dev/badge/skills/oxbshw/llm-agents-ecosystem-handbook/dataset-profiler/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/oxbshw/llm-agents-ecosystem-handbook/dataset-profiler"><img src="https://agentmods.dev/badge/skills/oxbshw/llm-agents-ecosystem-handbook/dataset-profiler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.00483 |
| Opus 5.5 | $0.00014 | $0.00193 |
| Sonnet 5.5 | $0.00007 | $0.00097 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
dataset-profiler 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 2mo 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.
What it actually says
Dataset Profiler
When to use
- A new dataset arrives and you need to understand it before using it
- Before reproducing an analysis that referenced a dataset
- When data quality is suspect ("the chart looked wrong")
When NOT to use
- Streaming / online data (this is point-in-time)
- Sensitive PII without an explicit allow-list
Inputs
| Name | Type | Required | Notes |
|---|---|---|---|
path |
path | yes | CSV / Parquet / JSONL |
target |
string | no | column of interest (gets extra distribution detail) |
Outputs
profile.md with: Source, Schema, Missingness, Distributions, Outliers, Joins / keys, Gotchas, Open questions.
Workflow
- Load with the right reader (extension-detected); record row count, file size
- Schema: column → dtype → nullable → example value
- Missingness: % per column, top columns by missingness
- Distributions: numeric (min, p50, p95, max, std), categorical (top-k, cardinality)
- Outliers: flag rows beyond p99 + 3·IQR for numerics
- Identify potential keys (unique columns) and join candidates
- Gotchas: timezone columns, mixed encodings, suspicious all-zero rows, magic values (
-1,9999-12-31) - Open questions: ambiguous columns / values that need owner input
References
Success criteria
- Every column appears in Schema + Missingness
- Outliers section includes example rows
- Gotchas section is non-empty (real datasets always have some)
Failure modes
- File too large to read in memory → switch to streaming + sampled stats; flag prominently
- Encoding fails → try common alternatives; if all fail, surface and stop
What ships with it
1 file 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.
- 2mo ago First seen · 51 lines · 34 tokens per session scan A 38d559a0bbda
dataset-profiler is a skill published in the GitHub repository oxbshw/LLM-Agents-Ecosystem-Handbook (558 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 483 once invoked, about $0.0001 per session on Opus 5.5. 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.
dataset-profiler compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dataset-profileroxbshw/LLM-Agents-Ecosystem-Handbook | Skill | 558 | 4mo ago | MIT | A | 34 | Claude Code, Codex |
| tao-finetune-anomalygennextNVIDIA-TAO/tao-skill-bank | Skill | 99 | yesterday | Apache-2.0 | A | 58 | Claude Code |
| Trainingagentic-in/elephant-agent | Skill | 584 | 2mo ago | none | A | 23 | Claude Code, Codex |
| apify-youtube-transcript-scrapersickn33/agentic-awesome-skills | Skill | 49k | yesterday | MIT | A | 48 | Claude Code |
| data-reportnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 18 | Claude Code, Codex |
| tinker-training-costsynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 55 | Claude Code, Codex |
| colab-finetuningsynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 67 | Claude Code, Codex |