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This skill should be used when importing, listing, or exporting Granola meeting recordings and transcripts. Queries Granola's Personal API to list meetings, extract transcripts, and export to Obsidian notes in Fathom-compatible format.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
This skill should be used for elimination-style research where the user wants to choose from a shortlist of products, tools, services, vendors, or other options using explicit criteria, numeric evidence, tournament-style comparison, source/domain classification, image-supported consumer reports, raw data tables, and ownership-cost estimates for replaceable parts. Use this skill whenever the user asks to compare options, buy something, shortlist candidates, rank alternatives, generate a "don't make me think" report, or produce a full audit report with raw numeric data.
This skill should be used to run a formal heuristic evaluation of a design artifact against Jakob Nielsen's 10 usability heuristics, producing an evidence-backed, severity-scored report. Use it when the user wants a "heuristic evaluation", "usability review", "Nielsen heuristics check", "UX heuristic audit", or asks whether a screenshot, live URL, HTML page, codebase UI, interface description, or JTBD/spec document holds up against usability principles. Accepts five input types (screenshot/image, live URL, codebase/HTML, interface description, JTBD/spec doc) and adapts its rigor and output honestly to what is actually observable. Can render the report as plain markdown (default) or as a Tufte-style HTML report, and can export findings above a severity threshold as Linear or Beads (bd) tasks after confirmation.
This skill should be used when editing, translating, or reviewing an Astro-style i18n string corpus (files of the form export default { en: {...}, ru: {...} } under src/i18n/strings), or when the user wants to fill in missing translations, audit coverage, accept/review translations, propagate an edit across duplicate strings, get translation candidates, bulk-edit UI microcopy, or open a visual/keyboard translation editor. Drives the standalone i18n Studio tool at ~/ai_projects/i18n-studio (AST-safe minimal-diff saves via ts-morph, acceptance review state, duplicate propagation, hot-reload, and Claude translation suggestions).
This skill should be used when taking a single software feature from intent to shipped as a solo developer — goal-first, TDD, deterministic verification, evidence only where it earns its keep, and human judgment at the two moments that matter (goal approval, merge). Trigger when the user says "let's build feature X", "ship this feature", "run this through the factory", "write a goal contract", "feature-factory", or wants a disciplined intent→merge loop that resists process bloat. NOT for whole-product planning, multi-feature roadmaps, or autonomous multi-agent swarms.
A Theory-of-Constraints diagnostic for deciding what to automate with AI agents. Before building any automation, skill, Goal, loop, or schedule, it walks Goldratt's Five Focusing Steps over the user's work system to find the real bottleneck, then recommends the single highest-leverage automation aimed at the constraint plus a what-NOT-to-automate list. Use when the user asks "what should I automate", "where do I point my agents", "prioritize my automation backlog", "which workflow should I agentify", "is this worth building", "find my bottleneck", "what's the highest-leverage thing", when they are about to build a Claude Code skill/Goal/loop/schedule and aren't sure it matters, or during a review of their automations. Guards against the common failure of automating busywork (a local optimum) instead of the constraint.
This skill should be used to set up, validate, resolve, and export design tokens following the DTCG (Design Tokens Community Group) Format Module 2025.10 standard. Use when the user wants to define a design token set globally or per project, compile tokens to CSS variables, layer a project's tokens over a global brand base, or produce an on-brand context file for other generation skills. Triggers on "set up design tokens", "create a token set", "compile tokens to CSS", "design system variables", "brand tokens".
| name | granola |
| description | This skill should be used when importing, listing, or exporting Granola meeting recordings and transcripts. Queries Granola's Personal API to list meetings, extract transcripts, and export to Obsidian notes in Fathom-compatible format. |
Query Granola via Personal API to list notes, view transcripts, and export to Obsidian vault in the same format as the Fathom skill.
~/Brains/brain/.env.granola as GRANOLA_API_KEY=grn_...python3 ~/.claude/skills/granola/scripts/granola.py <command> [options]
| Command | Description |
|---|---|
list | List notes from Personal API |
show <note_id> | Show note details (summary, attendees, optionally transcript) |
export <note_id> | Export note to Obsidian markdown (Fathom-compatible format) |
| Option | Applies to | Description |
|---|---|---|
--format text|json | list, show | Output format (default: text) |
--after <ISO date> | list | Filter notes created after date |
--all | list | Paginate through all results |
--transcript | show | Include transcript in output |
--vault <path> | export | Obsidian vault path (default: ~/Brains/brain) |
--output <path> | export | Custom output file path |
python3 ~/.claude/skills/granola/scripts/granola.py list
python3 ~/.claude/skills/granola/scripts/granola.py list --format json
python3 ~/.claude/skills/granola/scripts/granola.py list --after 2026-05-01
python3 ~/.claude/skills/granola/scripts/granola.py show not_5FkswTp4Omkpm5
python3 ~/.claude/skills/granola/scripts/granola.py show not_5FkswTp4Omkpm5 --transcript --format json
python3 ~/.claude/skills/granola/scripts/granola.py export not_5FkswTp4Omkpm5
python3 ~/.claude/skills/granola/scripts/granola.py export not_5FkswTp4Omkpm5 --vault ~/Brains/brain
Exported notes match Fathom skill format for consistency:
---
granola_id: not_xxxx
title: "Meeting Title"
date: YYYY-MM-DD
participants: ['Name 1', 'Name 2']
duration: HH:MM
source: granola
---
# Meeting Title
## Summary
{AI-generated summary}
## Transcript
**Speaker Name**: What they said...
Files saved as: Sessions/YYYYMMDD-meeting-title-slug.md
https://public-api.granola.ai/v1source (microphone vs speaker) and optional diarization_label. Export assigns meeting owner to microphone utteranceslist first to get not_xxxx IDs, then show/export