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/gaasher/agent-loop-skills/data-analysisnpx skills add gaasher/Agent-Loop-Skills --skill data-analysisgit clone --depth 1 https://github.com/gaasher/Agent-Loop-SkillsWhat 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 | $0.00140 | $0.01814 |
| Opus 5 | $0.00070 | $0.00907 |
| Sonnet 5 | $0.00028 | $0.00363 |
| Haiku 4.5 | $0.00014 | $0.00181 |
Grade A, and why
data-analysis 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 2d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis Loop
A hypothesis → verify reflection loop over a dataset. The artifact is a findings report; the feedback signal is verification — a finding only counts if re-running the computation confirms it at a meaningful effect size. The discipline this enforces: no insight without a number behind it. A plausible claim the data does not support is discarded, not softened; every line in the report can be reproduced from the dataset.
When to use
Use this for open-ended, self-checking exploration of a bound dataset where each finding must survive an independent re-computation. Default to broad exploration across the columns; if the user gives a focus question, let it steer the hypotheses. Not for diagnosing one known anomaly or for checking an external claim against the literature.
Setup
Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the
values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is
available) infer a likely value for each binding and present it as the recommended option; on other
hosts ask each as a quoted plain-text prompt. Then write loop.run.yaml (format:
examples/run.example.yaml) and confirm the values before creating any other files.
| binding | meaning | default | how to infer |
|---|---|---|---|
<dataset> |
data file to analyze (CSV/TSV/Parquet/…); read-only ground truth | — | scan the working dir for a data file |
<question> |
optional analysis focus; omit to explore broadly | — | ask the user; else leave unbound |
<report> |
output findings file | <sandbox_root>/findings.md |
— |
<analysis_cmd> |
interpreter that runs analysis snippets in the user's env | python3 |
pyproject.toml/.venv/uv in the working dir |
<sandbox_root> |
where snippets + ledger live | ./sandbox |
— |
<budget> |
max iterations | 8 | — |
<patience> |
stop after N consecutive iters with no new verified finding | 2 | — |
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.
- 2d ago First seen · 122 lines · 140 tokens per session scan A f8392011370d
data-analysis is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (161 stars, last pushed 2mo ago), licensed MIT. It adds 140 tokens to every session and 1,814 once invoked, about $0.0007 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 2026-08-30.
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