PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill user-research-synthesisgit clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills/user-research-synthesis)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/user-research-synthesis/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/mohitagw15856/pm-claude-skills/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/user-research-synthesis.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 | $0.00114 | $0.02361 |
| Opus 5.5 | $0.00046 | $0.00944 |
| Sonnet 5.5 | $0.00023 | $0.00472 |
| Haiku 4.5 | $0.00011 | $0.00236 |
Grade A, and why
user-research-synthesis 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 2d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Habilidad de Síntesis de Investigación de Usuarios
Esta habilidad ayuda a analizar datos de investigación de usuarios y transformarlos en insights accionables siguiendo una metodología estructurada.
Entradas Requeridas
Solicita al usuario estos elementos si no se proporcionan:
- Datos de investigación (transcripciones, notas, resultados de encuestas o puntos de resumen)
- Método de investigación (entrevistas, encuestas, pruebas de usabilidad, etc.)
- Número de participantes y sus perfiles (rol, contexto)
- Preguntas de investigación que el estudio buscaba responder
Lee desde / Escribe en el Brain
Si existe un professional-brain (brain/), úsalo antes de preguntar:
- Lee primero: abre
hypotheses/(qué suposiciones esta investigación puede validar o invalidar) ycontext.md(quiénes son los usuarios). - Escribe después: actualiza el estado de cada hipótesis que hayas tocado, añade insights duraderos a
knowledge/users.md, y mantén las notas brutas ensource/. Etiqueta las afirmaciones derivadas de entrevistas como[interview]— nunca las conviertas en[data].
Marco de Síntesis
1. Resumen de Recopilación de Datos
- Tipo de Investigación: Entrevistas, encuestas, pruebas de usabilidad, etc.
- Perfil de Participantes: Demografía, segmentos, tamaño de muestra
- Preguntas de Investigación: Qué buscábamos aprender
- Metodología: Cómo se recopilaron los datos
2. Identificación de Temas Clave
Organiza hallazgos en temas usando esta estructura:
Nombre del Tema
- Descripción: Qué representa este tema
- Prevalencia: Cuántos participantes lo mencionaron (p. ej., "8 de 12 participantes")
- Citas de Apoyo: 2-3 citas representativas
- Implicación: Qué significa esto para nuestro producto
Apunta a 4-8 temas mayores por esfuerzo de investigación.
3. Análisis de Puntos de Dolor
Para cada punto de dolor identificado:
- Punto de Dolor: Descripción clara
- Severidad: Alta/Media/Baja (basada en impacto y frecuencia)
- Solución Actual: Cómo los usuarios lo manejan hoy
- Evidencia: Ejemplos específicos de la investigación
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.
- 2d ago First seen · 246 lines · 114 tokens per session scan A 046a9817ed06
user-research-synthesis is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 114 tokens to every session and 2,361 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-10-12.
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