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 gaasher/Agent-Loop-Skills --skill anomaly-investigationgit clone --depth 1 https://github.com/gaasher/Agent-Loop-SkillsWrote 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/gaasher/agent-loop-skills/anomaly-investigation)<a href="https://agentmods.dev/skills/gaasher/agent-loop-skills/anomaly-investigation"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/anomaly-investigation/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/gaasher/agent-loop-skills/anomaly-investigation"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/anomaly-investigation.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 | $0.00153 | $0.01986 |
| Opus 5 | $0.00077 | $0.00993 |
| Sonnet 5 | $0.00031 | $0.00397 |
| Haiku 4.5 | $0.00015 | $0.00199 |
Grade A, and why
anomaly-investigation 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 10d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anomaly Investigation Loop
A form → test → eliminate → confirm loop — root-cause analysis as a search. The artifact is an investigation log; the feedback signal is the count of live candidate explanations, driven down toward a single cause that is confirmed, not merely consistent. Each iteration you test one candidate against the data and drop the ones the data refutes, narrowing the field until one survives.
The discipline this enforces: a cause is "root" only when it both survives an honest attempt to refute it and makes a positive prediction that checks out (e.g. "if this is the cause, removing it restores normal" — and it does). A story that merely could explain the anomaly is a hypothesis, not a finding.
When to use
Use this when an anomaly is already in hand — you know roughly what looks wrong and want the cause diagnosed by elimination against the data. Default to a broad initial slate of mutually distinguishable causes, then test the one that splits the field fastest; if the anomaly is vague, your first job is to make it precise (iteration 0). Not for open-ended exploration of a dataset with no anomaly to chase (use data-analysis), and not for verifying an external claim against the literature (use claim-verify).
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 (or logs) to investigate; read-only ground truth | — | scan the working dir for a data/log file |
<anomaly> |
what looks wrong: the metric, where/when, and how big the deviation is | — | ask the user; make precise in iter 0 |
<analysis_cmd> |
interpreter that runs analysis snippets in the user's env | python3 |
pyproject.toml/.venv/uv in the working dir |
<log> |
output investigation log | <sandbox_root>/investigation.md |
— |
<sandbox_root> |
where snippets + ledger live | ./sandbox |
— |
<budget> |
max iterations | 8 | — |
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.
- 10d ago First seen · 133 lines · 153 tokens per session scan A ee7ce06f7d6e
anomaly-investigation is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (169 stars, last pushed 2mo ago), licensed MIT. It adds 153 tokens to every session and 1,986 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 2026-08-30.
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