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.
git clone --depth 1 https://github.com/hollandkevint/data-product-operatorWrote 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/hollandkevint/data-product-operator/run-discovery)<a href="https://agentmods.dev/commands/hollandkevint/data-product-operator/run-discovery"><img src="https://agentmods.dev/badge/commands/hollandkevint/data-product-operator/run-discovery/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/hollandkevint/data-product-operator/run-discovery"><img src="https://agentmods.dev/badge/commands/hollandkevint/data-product-operator/run-discovery.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 | $0.00025 | $0.01117 |
| Opus 5 | $0.00013 | $0.00558 |
| Sonnet 5 | $0.00005 | $0.00223 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
run-discovery 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 9d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run a Data Product Discovery Session
Guide a structured discovery conversation for a new data product idea. This command combines consumer discovery questions with a validation scorecard to produce a go/investigate/kill recommendation.
Gather Context
If $ARGUMENTS provides a product idea or problem area, use it as the starting point. Otherwise, ask:
Question 1: What problem area are you exploring? Describe it in terms of the decision someone needs to make, not the data they want.
Run Discovery Questions
Ask these questions one at a time. Wait for each answer before proceeding. Each question builds on the prior answer.
Question 2: Who are the consumers of this data? What decisions do they make with it? (Reference data-consumer-discovery consumer segments: Explorers, Reporters, Decision-makers, Builders.)
Question 3: Walk me through the last time someone needed this data. Where did it come from? How long did it take? What did they trust or distrust about it?
Question 4: What workarounds exist today? Describe any manual processes, Excel files, scripts, or tools people have built to solve this problem. For each workaround, capture: tool used, frequency, time cost per use, trust level, downstream dependents.
Question 5: What would make consumers NOT trust this output? List specific concerns: source opacity, staleness, conflicting numbers, past incidents, no validation path.
Question 6: If consumers could get this answer in under a minute, what changes? Describe the business impact in specific terms: decisions made faster, costs avoided, risks caught earlier.
Score the Validation Scorecard
After collecting discovery answers, score each dimension 1-5 based on the evidence gathered. Reference data-product-validation for scoring criteria.
Explain your reasoning for each score in 1-2 sentences.
- Demand Frequency (1-5): How often do consumers need this answer?
- Decision Impact (1-5): What happens when they don't have it?
- Workaround Effort (1-5): What are they doing today instead?
- Data Feasibility (1-5): Can we build this with available data?
- Schema Risk (1-5): How locked-in are consumers once we ship?
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.
- 9d ago First seen · 116 lines · 25 tokens per session scan A 78e20dbe8e37
run-discovery is a command published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 1,117 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
full-pm-cycle
A command that coordinates product management from researching a market and users through planning, design, delivery, and measuring results.
octo-schedule
Manage scheduled workflow jobs for the Claude Octopus scheduler.
octo-scheduler
Manage the Claude Octopus scheduled workflow runner daemon.
workflow-foundation-sprint
Run the Foundation Sprint workflow (2-day strategic-alignment arc producing a Founding Hypothesis).
discover
Run a full product discovery cycle — from outcome definition through opportunity mapping, prioritisation, and experiment design. Use when the team isn't sure what to build next, or before writing a PRD for a complex feature space.
next
Read the active sprint’s current status. Refresh stale compilation using grainulator compile --dir or grainulator.compile. Use the resulting nextactions with the user’s current scope and authorization. Output only the two Auto and Manual lists below, with no recap, status counters or offer to continue. Continue useful…