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 samkawsarani/sams-product-plugins --skill analyze-researchgit clone --depth 1 https://github.com/samkawsarani/sams-product-pluginsWrote 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/samkawsarani/sams-product-plugins/analyze-research)<a href="https://agentmods.dev/skills/samkawsarani/sams-product-plugins/analyze-research"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-research/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/samkawsarani/sams-product-plugins/analyze-research"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-research.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 | $0.00078 | $0.01098 |
| Opus 5 | $0.00039 | $0.00549 |
| Sonnet 5 | $0.00016 | $0.00220 |
| Haiku 4.5 | $0.00008 | $0.00110 |
Grade A, and why
analyze-research 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 11d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dependency Check
Before starting, check optional dependencies:
- qmd (optional): Run
command -v qmd.- If available: Use
qmd queryfor semantic search across knowledge bases — this gives the best results. - If missing: Fall back to
GrepandGlobfor keyword search. Note to the user thatqmdwould improve search quality, but proceed without it.
- If available: Use
User Research Analysis
Overview
Systematically analyze user interviews, research transcripts, surveys, and feedback to extract themes, identify patterns, generate actionable insights, and create comprehensive synthesis reports or personas.
Analysis Workflow
1. Understand the Request
Clarify what type of analysis is needed:
- Theme extraction: Identify recurring topics and patterns
- Insight generation: Synthesize findings into actionable recommendations
- Persona creation: Build user profiles from research data
- Quick synthesis: Summarize key findings
- Full report: Comprehensive research synthesis document
Determine the source materials:
- Location of transcripts (ask the user if not provided)
- Related files — initiatives, specs, or prior research
- Timeframe or specific interviews to analyze
Find relevant transcripts and documents using available search tools.
- Any specific questions to answer
2. Review Source Materials
Read through all relevant materials:
- Interview transcripts
- Survey responses
- Feedback forms
- Meeting notes
- Related product specs, opportunities, or initiatives
Use the analysis framework by reading references/analysis-framework.md for systematic guidance on:
- How to extract themes
- How to categorize findings
- How to quote effectively
- How to generate insights from observations
3. Perform Analysis
Based on the analysis framework, systematically:
Extract themes:
- Identify recurring pain points, feature requests, behaviors
- Quote verbatim with source attribution
- Note frequency (how many users mentioned it)
- Assess intensity (how strongly they felt about it)
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.
- 11d ago First seen · 143 lines · 78 tokens per session scan A 5f58a079fdbf
analyze-research is a skill published in the GitHub repository samkawsarani/sams-product-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 1,098 once invoked, about $0.0004 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…