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 | $0.00000 | $0.00989 |
| Sonnet 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 6d 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.
- 6d 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 (9 stars, last pushed 3mo 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.