Borrowing it
Nothing to install: this file belongs to motorway-sandbox/product-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/motorway-sandbox/product-os/main/.claude/commands/investigate.mdgit clone --depth 1 https://github.com/motorway-sandbox/product-osWrote 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/commands/motorway-sandbox/product-os/investigate)<a href="https://agentmods.dev/commands/motorway-sandbox/product-os/investigate"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/investigate/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/commands/motorway-sandbox/product-os/investigate"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/investigate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.01979 |
| Opus 5.5 | $0.00000 | $0.00792 |
| Sonnet 5.5 | $0.00000 | $0.00396 |
| Haiku 4.5 | $0.00000 | $0.00198 |
Grade A, and why
investigate 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.
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigate
You have been given a signal or question to investigate: $ARGUMENTS
Run a structured investigation combining quantitative data, qualitative evidence, and counter-evidence. Produce a hypothesis and recommended next steps.
Important: Do NOT ask any clarifying questions. Do NOT pause for user input. Run the entire investigation in one pass.
Step 1: Parse the signal
Extract from the user's input:
- Metric: What is being measured (e.g. step completion rate, sign-up-to-activation, churn rate)
- Segment: Any filters (e.g. Android, new users, enterprise accounts)
- Timeframe: When (e.g. this week, since app release, Q1). Default to "recent" if not specified
- Direction: What changed (e.g. dropped, increased, anomaly)
State these clearly before proceeding.
Step 2: Quantitative investigation
Run these searches in parallel:
2a. Metric definitions and baselines
- Read
data/funnel-context.mdfor metric definitions, baselines, and how to interpret the metric (leading vs lagging) - Read
context/current-priorities.mdto check if this metric relates to a current goal or target
2b. Data warehouse query (if MCP available)
- Use dbt Semantic Layer tools to search for relevant metrics (
list_metrics,query_metrics) - Use
get_all_modelsorget_mart_modelsto find relevant dbt models - Query BigQuery directly if the right table is identified
- Follow the conventions in your data warehouse conventions
2c. Saved data files (always — as fallback or supplement)
- Search
data/folder for CSV files, saved reports, and metric files matching the signal keywords - Search for any weekly business review (WBR) data or funnel detail files
- Grep across the repo for the specific metric values or trend references
Record: what the data says, the source, the time period, and confidence level (High / Medium / Low).
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 · 201 lines · 0 tokens per session scan A 6db858e575dc
investigate is a command published in the GitHub repository motorway-sandbox/product-os (11 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,979 tokens. 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 2026-09-04.
investigate compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| investigatemotorway-sandbox/product-os | Command | 11 | 5mo ago | MIT | A | 0 | Claude Code |
| checklistgithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| analyzegithub/spec-kit | Command | 148k | yesterday | MIT | A | 26 | Claude Code |
| convergegithub/spec-kit | Command | 148k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 148k | yesterday | MIT | A | 14 | Claude Code |