granola-local-dev-loop
Access Granola meeting data programmatically for developer workflows. Use when reading notes from the local cache, building MCP integrations, extracting action items into code, or syncing meeting outcomes to dev tools. Trigger: "granola dev workflow", "granola MCP", "granola local cache", "granola developer", "granola programmatic".
What this skill does
# Granola Local Dev Loop
## Overview
Access Granola meeting data programmatically using three methods: the local cache file (zero-auth, offline), the MCP server (AI agent integration), or the Enterprise API (workspace-wide access). Build developer workflows that turn meeting outcomes into code tasks, documentation, and project artifacts.
## Prerequisites
- Granola installed with meetings captured
- Node.js 18+ or Python 3.10+ for scripts
- For MCP: Claude Code, Cursor, or another MCP-compatible client
- For Enterprise API: Business/Enterprise plan + API key
## Instructions
### Step 1 — Read the Local Cache (Zero Auth)
Granola stores meeting data in a local JSON cache file:
```bash
# macOS cache location
CACHE_FILE="$HOME/Library/Application Support/Granola/cache-v3.json"
# Check if cache exists and get size
ls -lh "$CACHE_FILE"
```
The cache has a double-JSON structure (JSON string inside JSON):
```python
#!/usr/bin/env python3
"""Extract meetings from Granola local cache."""
import json
from pathlib import Path
CACHE_PATH = Path.home() / "Library/Application Support/Granola/cache-v3.json"
def load_granola_cache():
raw = json.loads(CACHE_PATH.read_text())
# Cache contains a JSON string that needs secondary parsing
state = json.loads(raw) if isinstance(raw, str) else raw
data = state.get("state", state)
return {
"documents": data.get("documents", {}),
"transcripts": data.get("transcripts", {}),
"meetings_metadata": data.get("meetingsMetadata", {}),
}
cache = load_granola_cache()
docs = cache["documents"]
print(f"Found {len(docs)} meetings in local cache")
# List recent meetings
for doc_id, doc in sorted(docs.items(),
key=lambda x: x[1].get("updated_at", ""),
reverse=True)[:10]:
print(f" {doc.get('title', 'Untitled')} — {doc.get('updated_at', 'N/A')}")
```
### Step 2 — Set Up Granola MCP Server
Granola's official MCP integration connects meeting context to AI tools:
```json
// claude_desktop_config.json or .mcp.json
{
"mcpServers": {
"granola": {
"command": "npx",
"args": ["-y", "granola-mcp-server"]
}
}
}
```
With MCP connected, Claude Code and Cursor can:
- Search across all your meetings by topic or person
- Pull context from specific meetings into coding sessions
- Create tickets based on discussed bugs or features
- Scaffold code based on architectural decisions from meetings
Community MCP servers with additional features:
- `pedramamini/GranolaMCP` — CLI + programmatic + MCP access, reads local cache
- `mishkinf/granola-mcp` — semantic search with LanceDB vector embeddings
- `proofgeist/granola-mcp-server` — lightweight local cache reader
### Step 3 — Extract Action Items to Dev Tools
```python
#!/usr/bin/env python3
"""Extract action items from Granola notes and create GitHub issues."""
import json, re, subprocess
from pathlib import Path
def extract_action_items(note_content: str) -> list[dict]:
"""Parse action items from enhanced Granola notes."""
items = []
# Matches: - [ ] @person: task description
pattern = r'- \[ \] @?(\w+):?\s+(.+)'
for match in re.finditer(pattern, note_content):
items.append({
"assignee": match.group(1),
"task": match.group(2).strip(),
})
return items
def create_github_issue(repo: str, title: str, body: str, assignee: str):
"""Create a GitHub issue using gh CLI."""
cmd = [
"gh", "issue", "create",
"--repo", repo,
"--title", title,
"--body", body,
"--assignee", assignee,
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode == 0:
print(f" Created: {result.stdout.strip()}")
else:
print(f" Error: {result.stderr.strip()}")
# Usage with cache data
cache = load_granola_cache() # from Step 1
for doc_id, doc in cache["documents"].items():
content = doc.get("last_viewed_panel", {})
# ProseMirror content needs text extraction
text = json.dumps(content) # simplified — parse nodes for production
actions = extract_action_items(text)
for action in actions:
print(f"[{action['assignee']}] {action['task']}")
```
### Step 4 — Sync Meeting Outcomes to Project Docs
```bash
#!/bin/bash
set -euo pipefail
# Sync latest Granola meeting notes to project documentation
NOTES_DIR="$HOME/dev/meeting-notes"
mkdir -p "$NOTES_DIR"
# Extract recent meeting titles and dates using Python
python3 -c "
import json
from pathlib import Path
cache_path = Path.home() / 'Library/Application Support/Granola/cache-v3.json'
if cache_path.exists():
raw = json.loads(cache_path.read_text())
state = json.loads(raw) if isinstance(raw, str) else raw
data = state.get('state', state)
docs = data.get('documents', {})
for doc_id, doc in sorted(docs.items(),
key=lambda x: x[1].get('updated_at', ''),
reverse=True)[:5]:
title = doc.get('title', 'Untitled').replace(' ', '-').lower()
date = doc.get('created_at', 'unknown')[:10]
print(f'{date}_{title}')
"
```
### Step 5 — Git Integration Pattern
Reference Granola meetings in commits and PRs:
```bash
# Reference meeting in commit message
git commit -m "feat: implement user onboarding flow
Per meeting 2026-03-22 'Sprint Planning Q1':
- Agreed on 3-step wizard approach
- Sarah approved the design mockups
- Due by April 15
Action items from Granola note: [link]"
```
## Output
- Local cache accessible for offline meeting data reads
- MCP server connected for AI-assisted meeting context
- Action item extraction pipeline ready
- Meeting-to-dev-tools sync established
## Error Handling
| Error | Cause | Fix |
|-------|-------|-----|
| Cache file not found | Granola not installed or never launched | Install Granola and capture at least one meeting |
| JSON parse error | Double-JSON structure not handled | Parse the outer string first, then parse the inner object |
| MCP server not connecting | Wrong config path | Verify `claude_desktop_config.json` location for your OS |
| Empty transcripts | Transcript stored separately from document | Check `cache["transcripts"]` keyed by document ID |
| Stale cache data | Cache not refreshed | Restart Granola to force cache update |
## Resources
- [Granola MCP Announcement](https://www.granola.ai/blog/granola-mcp)
- [GranolaMCP (cache-based)](https://github.com/pedramamini/GranolaMCP)
- [Reverse-Engineered API Docs](https://github.com/getprobo/reverse-engineering-granola-api)
- [Granola Enterprise API](https://docs.granola.ai/introduction)
## Next Steps
Proceed to `granola-sdk-patterns` for Zapier automation workflows.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.