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 yuusakuri/agent-skills --skill analyze-feature-requestsgit clone --depth 1 https://github.com/yuusakuri/agent-skillsWrote 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/yuusakuri/agent-skills/analyze-feature-requests)<a href="https://agentmods.dev/skills/yuusakuri/agent-skills/analyze-feature-requests"><img src="https://agentmods.dev/badge/skills/yuusakuri/agent-skills/analyze-feature-requests/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/yuusakuri/agent-skills/analyze-feature-requests"><img src="https://agentmods.dev/badge/skills/yuusakuri/agent-skills/analyze-feature-requests.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 | $0.00045 | $0.00476 |
| Opus 5 | $0.00023 | $0.00238 |
| Sonnet 5 | $0.00009 | $0.00095 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
analyze-feature-requests 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 10d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Feature Requests
Categorize, evaluate, and prioritize customer feature requests against product goals.
Context
You are analyzing feature requests for $ARGUMENTS.
If the user provides files (spreadsheets, CSVs, or documents with feature requests), read and analyze them directly. If data is in a structured format, consider creating a summary table.
Domain Context
Never allow customers to design solutions. Prioritize opportunities (problems), not features. Use Opportunity Score (Dan Olsen) to evaluate customer-reported problems: Opportunity Score = Importance × (1 − Satisfaction), normalized to 0–1. See the prioritization-frameworks skill for full details and templates.
Instructions
The user will describe their product goal and provide feature requests. Work through these steps:
-
Understand the goal: Confirm the product objective and desired outcomes that will guide prioritization.
-
Categorize requests into themes: Group related requests together and name each theme.
-
Assess strategic alignment: For each theme, evaluate how well it aligns with the stated goals.
-
Prioritize the top 3 features based on:
- Impact: Customer value and number of users affected
- Effort: Development and design resources required
- Risk: Technical and market uncertainty
- Strategic alignment: Fit with product vision and goals
-
For each top feature, provide:
- Rationale (customer needs, strategic alignment)
- Alternative solutions worth considering
- High-risk assumptions
- How to test those assumptions with minimal effort
Think step by step. Save as markdown or create a structured output document.
Further Reading
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.
- 10d ago First seen · 50 lines · 45 tokens per session scan A 7615db7687a6
analyze-feature-requests is a skill published in the GitHub repository yuusakuri/agent-skills (2 stars, last pushed 4d ago), licensed MIT. It adds 45 tokens to every session and 476 once invoked, about $0.0002 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.
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