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-interview-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-interview-synthesis)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/user-interview-synthesis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/user-interview-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-interview-synthesis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/user-interview-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 | $0.00113 | $0.00853 |
| Opus 5.5 | $0.00045 | $0.00341 |
| Sonnet 5.5 | $0.00023 | $0.00171 |
| Haiku 4.5 | $0.00011 | $0.00085 |
Grade A, and why
user-interview-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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Habilidad de Síntesis de Entrevistas con Usuarios
Transforma transcripciones de entrevistas brutes en un documento de síntesis estructurado que destaque temas, puntos de dolor e insights accionables.
Inputs Requeridos
Solicita al usuario estos datos si no están disponibles:
- Transcripciones o notas de entrevistas (incluso notas aproximadas sirven)
- Número de participantes y sus perfiles (rol, tamaño de empresa, contexto)
- Preguntas de investigación (¿qué pretendía responder el estudio?)
- Rango de fechas de la investigación (para contexto)
Proceso
- Lee todas las transcripciones proporcionadas en su totalidad antes de extraer conclusiones
- Identifica temas recurrentes (mínimo 3 menciones para calificar como tema)
- Categoriza hallazgos en: Puntos de Dolor, Insights de Flujo de Trabajo, Solicitudes de Features, Momentos de Satisfacción
- Selecciona 2-3 citas textuales por tema que mejor representen el patrón
- Redacta implicaciones "y qué significa esto" para cada tema — ¿qué significa esto para el producto?
- Valida — Confirma que cada tema tiene citas de al menos 3 participantes. Marca como baja confianza cualquier insight basado en menos participantes.
Estructura del Output
Síntesis de Investigación: [Nombre del Estudio]
Participantes: [n] Rango de Fechas: [fechas] Preguntas de Investigación: [lista]
Tema 1: [Nombre del Tema]
- Resumen (2-3 oraciones)
- Citas de apoyo (de al menos 3 participantes)
- Implicación para el producto
[Repite para cada tema]
Señales de Baja Confianza (1-2 participantes solamente)
[Hallazgos que vale la pena monitorear pero aún no actuar — nota qué investigación adicional confirmaría o negaría]
Pasos Recomendados
[Recomendaciones específicas y accionables basadas en hallazgos]
Controles de Calidad
- Cada tema está respaldado por citas de al menos 3 participantes
- Las implicaciones conectan con decisiones específicas de producto, no solo observaciones
- Verificación de sesgos del investigador: sin lenguaje tendencioso, hallazgos no todos favorecen una hipótesis
- Las señales de fuente única están marcadas por separado, no mezcladas en temas principales
- Cada pregunta de investigación del brief del estudio está respondida (incluso si la respuesta es "inconclusivo")
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 · 62 lines · 113 tokens per session scan A 61f6a6962a88
user-interview-synthesis is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 113 tokens to every session and 853 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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