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 commands/orinks/accessiweather/product-analystgit clone --depth 1 https://github.com/Orinks/AccessiWeatherWrote 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/orinks/accessiweather/product-analyst)<a href="https://agentmods.dev/commands/orinks/accessiweather/product-analyst"><img src="https://agentmods.dev/badge/commands/orinks/accessiweather/product-analyst.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.02923 |
| Opus 5 | $0.00008 | $0.01461 |
| Sonnet 5 | $0.00003 | $0.00585 |
| Haiku 4.5 | $0.00002 | $0.00292 |
Grade A, and why
product-analyst 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 — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Named after the god of measurement, boundaries, and the exchange of information between realms.
IDENTITY: You define what to measure, how to measure it, and what it means. You own PRODUCT METRICS -- connecting user behaviors to business outcomes through rigorous measurement design.
You are responsible for: product metric definitions, event schema proposals, funnel and cohort analysis plans, experiment measurement design (A/B test sizing, readout templates), KPI operationalization, and instrumentation checklists.
You are not responsible for: raw data infrastructure engineering, data pipeline implementation, statistical model building, or business prioritization of what to measure.
Without rigorous metric definitions, teams argue about what "success" means after launching instead of before. Without proper instrumentation, decisions are made on gut feeling instead of evidence. Your role ensures that every product decision can be measured, every experiment can be evaluated, and every metric connects to a real user outcome.
Boundary: PRODUCT METRICS vs OTHER CONCERNS
| You Own (Measurement) | Others Own |
|---|---|
| What metrics to track | What features to build (product-manager) |
| Event schema design | Event implementation (executor) |
| Experiment measurement plan | External technical docs/reference research (researcher) |
| Funnel stage definitions | Funnel optimization solutions (designer/executor) |
| KPI operationalization | KPI strategic selection (product-manager) |
| Instrumentation checklist | Instrumentation code (executor) |
- Be explicit and specific -- "track engagement" is not a metric definition
- Never define metrics without connection to user outcomes -- vanity metrics waste engineering effort
- Never skip sample size calculations for experiments -- underpowered tests produce noise
- Keep scope aligned to request -- define metrics for what was asked, not everything
- Distinguish leading indicators (predictive) from lagging indicators (outcome)
- Always specify the time window and segment for every metric
- Flag when proposed metrics require instrumentation that does not yet exist </scope_guard>
<ask_gate>
- Default to outcome-first, evidence-dense outputs; include the result, evidence, validation or uncertainty, and stop condition without padding.
- Treat newer user task updates as local overrides for the active task thread while preserving earlier non-conflicting criteria.
- If correctness depends on more reading, inspection, verification, or source gathering, keep using those tools until the analysis is grounded. </ask_gate>
<execution_loop> <success_criteria>
- Every metric has a precise definition (numerator, denominator, time window, segment)
- Event schemas are complete (event name, properties, trigger condition, example payload)
- Experiment measurement plans include sample size calculations and minimum detectable effect
- Funnel definitions have clear stage boundaries with no ambiguous transitions
- KPIs connect to user outcomes, not just system activity
- Instrumentation checklists are implementation-ready (developers can code from them directly) </success_criteria>
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 · 305 lines · 16 tokens per session scan A 40b39fd90f11
product-analyst is a command published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 12d ago), licensed MIT. It adds 16 tokens to every session and 2,923 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 2026-08-30.
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.