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 Orkas-AI/Orkas-Awesome-AgentSkills --skill product-reviewgit clone --depth 1 https://github.com/Orkas-AI/Orkas-Awesome-AgentSkillsWrote 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/orkas-ai/orkas-awesome-agentskills/product-review)<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/product-review"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/product-review/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/orkas-ai/orkas-awesome-agentskills/product-review"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/product-review.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 | $0.00093 | $0.01490 |
| Opus 5 | $0.00046 | $0.00745 |
| Sonnet 5 | $0.00019 | $0.00298 |
| Haiku 4.5 | $0.00009 | $0.00149 |
Grade A, and why
product-review 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Review
Use this skill when a product idea has moved beyond early discovery and needs measurable review. It covers two connected moments:
- Before launch: define analytics instrumentation, event tracking, success metrics, guardrails, privacy handling, and data QA.
- After launch: review metrics, feedback, experiments, and market signals to decide whether to continue, pivot, stop, or restructure.
Do not use this skill for early product demand analysis, user research synthesis, competitor analysis, PRD writing, acceptance criteria, UI handoff, or engineering breakdown. Use this skill only when there is a concrete feature, MVP, release, experiment, funnel, or shipped product behavior to measure or review.
Route The Work
Choose the lightest useful path:
| User intent | Read |
|---|---|
| Feature, experiment, MVP, or release needs event tracking, metrics, data QA, or analytics spec | references/instrumentation.md |
| Post-release, MVP, or experiment evidence needs continue, pivot, stop, or restructure decision | references/release-decision.md |
| User asks for both launch measurement and later decision checkpoint | Read both references in sequence |
| User provides machine-readable analytics JSON and wants structure validation | references/analytics-json-schema.md and optionally scripts/validate-analytics-spec.js |
Required Inputs
Start by listing what the user provided and what is missing:
- Product, feature, MVP, release, or experiment name.
- Product goal and behavior to understand.
- Available metrics, data sources, analytics platform, or event logs.
- User feedback, customer quotes, sales/support notes, experiment results, or market signals.
- Constraints: privacy, consent, retention, platform, engineering ownership, launch date.
Ask only for missing inputs that materially change the review. If the user wants a first draft, proceed with explicit assumptions.
Instrumentation Instructions
- Start from decisions, not data volume: name the product questions this data must answer.
- Define success and guardrail metrics: include activation, conversion, retention, quality, latency, privacy, or revenue metrics when relevant.
- Create event inventory: specify event name, trigger, description, properties, required flags, examples, and owner notes.
- Use precise triggers: distinguish click, submit attempt, successful completion, failure, timeout, abandonment, and retry.
- Define user properties carefully: include only persistent segmentation attributes that are needed for analysis.
- Address privacy: flag PII, consent requirements, hashing/exclusion, retention, and regional constraints.
- Add QA checks: list how engineering/QA will verify events, properties, edge cases, and dashboards before launch.
What ships with it
4 files 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.
- 9d ago First seen · 110 lines · 0 tokens per session scan A 603f992a8e8c
product-review is a skill published in the GitHub repository Orkas-AI/Orkas-Awesome-AgentSkills (13 stars, last pushed 2mo ago), licensed MIT. It adds 93 tokens to every session and 1,490 once invoked, about $0.0005 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 2026-09-03.
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