granola-performance-tuning
Optimize Granola transcription accuracy, note quality, and processing speed. Use when improving transcription quality, reducing processing time, optimizing templates for better AI output, or tuning audio setup. Trigger: "granola performance", "granola accuracy", "granola quality", "improve granola", "granola transcription better".
Best use case
granola-performance-tuning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Optimize Granola transcription accuracy, note quality, and processing speed. Use when improving transcription quality, reducing processing time, optimizing templates for better AI output, or tuning audio setup. Trigger: "granola performance", "granola accuracy", "granola quality", "improve granola", "granola transcription better".
Teams using granola-performance-tuning should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/granola-performance-tuning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How granola-performance-tuning Compares
| Feature / Agent | granola-performance-tuning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Optimize Granola transcription accuracy, note quality, and processing speed. Use when improving transcription quality, reducing processing time, optimizing templates for better AI output, or tuning audio setup. Trigger: "granola performance", "granola accuracy", "granola quality", "improve granola", "granola transcription better".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Granola Performance Tuning
## Overview
Optimize Granola output quality across three dimensions: audio/transcription accuracy, AI enhancement quality, and integration speed. Granola's AI (GPT-4o/Claude) produces better output when it has clean audio, well-typed notes, and structured templates.
## Prerequisites
- Working Granola installation with meetings captured
- Willingness to improve audio setup and meeting practices
- At least 3-5 meetings captured to establish baseline quality
## Instructions
### Step 1 — Optimize Audio for Transcription
Granola captures system audio from your device. Transcription accuracy depends entirely on audio quality:
**Hardware recommendations (by priority):**
| Setup | Accuracy Impact | Recommendation |
|-------|----------------|----------------|
| Wired headset with mic | Highest | Best for solo/remote meetings |
| USB condenser mic | High | Best for in-office, multiple speakers |
| Laptop built-in mic | Medium | Acceptable for quiet environments |
| Bluetooth headset | Variable | May cause dropouts — test first |
| Speakerphone in room | Low | Echo and distance degrade accuracy |
**Audio configuration checklist:**
- [ ] Correct input device selected in System Settings > Sound > Input
- [ ] Input volume at 75-100% (not too low, not clipping)
- [ ] Audio enhancements disabled (Windows: right-click device > Properties > disable enhancements)
- [ ] No conflicting virtual audio software (Loopback, BlackHole, etc.)
- [ ] Bluetooth device stable (or switch to wired if experiencing drops)
**Room setup:**
- [ ] Minimal background noise (close doors, turn off fans)
- [ ] Soft surfaces to reduce echo (avoid glass-walled conference rooms)
- [ ] Mic within 12 inches of speaker(s)
- [ ] Meeting participants using headsets (reduces echo and crosstalk)
### Step 2 — Improve Meeting Practices
These behaviors directly improve Granola's output:
| Practice | Impact | Why It Helps |
|----------|--------|-------------|
| State names when assigning work | High | "Sarah, can you handle the API spec?" enables correct attribution |
| Use explicit action language | High | "Action item: review by Friday" — AI detects structured language |
| One speaker at a time | High | Crosstalk confuses speaker diarization |
| Summarize decisions verbally | Medium | "So we've decided to go with option B" — AI captures decisions |
| Spell technical terms first time | Medium | "We'll use Kubernetes, K-U-B-E-R-N-E-T-E-S" — improves accuracy |
| Type notes during the meeting | High | Your notes give the AI critical context for enhancement |
| Brief recap at meeting end | Medium | "To summarize, we agreed on X, Y, and Z" — improves summary |
### Step 3 — Optimize Templates for AI Quality
Template structure directly affects the quality of enhanced output:
**High-quality template design:**
```markdown
## Summary
[2-3 sentence overview of the meeting]
## Key Decisions
[Bullet list of decisions made, with reasoning]
## Action Items
[Format: - [ ] @person: task (due date)]
## Open Questions
[Items that need follow-up or weren't resolved]
## Next Steps
[What happens after this meeting]
```
**Template optimization tips:**
1. **Use 5-7 sections max** — too many sections dilute content
2. **Include format hints** — `[Format: - [ ] @person: task]` guides the AI
3. **Put Action Items near the end** — AI processes sequentially, actions at the end capture the full meeting
4. **Add "Verbatim Quotes" section for customer calls** — AI will pull exact language from the transcript
5. **Avoid generic sections** — "Notes" and "Discussion" produce vague output; be specific
### Step 4 — Post-Meeting Quality Review (5 Minutes)
After enhancing notes, spend 5 minutes on quality assurance:
- [ ] **Summary accurate?** Does it reflect what actually happened?
- [ ] **Action items complete?** Are all commitments captured with correct owners?
- [ ] **Decisions correct?** No hallucinated decisions or mixed-up attributions?
- [ ] **Sensitive content?** Remove anything that shouldn't be shared before posting
- [ ] **Missing context?** Add background the AI couldn't know
### Step 5 — Use Granola Chat to Fill Gaps
After enhancement, use Chat to improve the notes:
```
"What did Mike say about the timeline?"
→ Searches transcript for Mike's statements about timeline
"Were there any disagreements that aren't captured in the summary?"
→ Analyzes transcript for conflicting viewpoints
"Add the budget numbers that were discussed"
→ Pulls specific figures from the transcript
"Rewrite the action items with more detail"
→ Expands terse action items with transcript context
```
### Step 6 — Measure and Track Quality
| Metric | Target | How to Measure |
|--------|--------|----------------|
| Transcription accuracy | >95% word accuracy | Spot-check 2-3 min of transcript vs. audio |
| Action item detection | >90% captured | Compare enhanced notes to manual list |
| Decision accuracy | 100% correct | Verify all listed decisions actually happened |
| Processing time | <2 min for 30-min meeting | Timestamp when meeting ends vs. when notes are ready |
| Enhancement usefulness | 4+/5 team rating | Monthly survey: "How useful are Granola notes?" |
Track these monthly. If accuracy drops below target:
1. Check audio setup (most common cause)
2. Review template structure
3. Verify meeting practices are being followed
4. Contact Granola support for persistent issues
## Output
- Audio setup optimized for maximum transcription accuracy
- Meeting practices improving AI output quality
- Templates structured for effective enhancement
- Quality measurement process established
## Error Handling
| Issue | Cause | Fix |
|-------|-------|-----|
| <85% transcription accuracy | Poor microphone or noisy room | Upgrade to wired headset, reduce background noise |
| Action items missed | Vague language ("someone should...") | Use explicit format: "Action item: @person does X by Y" |
| Wrong speaker attribution | Crosstalk or no name usage | State names, avoid overlapping speech |
| Slow processing (>5 min) | Long meeting or server load | Normal for 2+ hour meetings; check status.granola.ai |
| Hallucinated decisions | AI filling template sections | Review before sharing; remove decisions that didn't happen |
## Resources
- [Get the Best from Granola](https://www.granola.ai/blog/get-the-best-from-granola)
- [How Transcription Works](https://docs.granola.ai/help-center/taking-notes/how-transcription-works)
- [Customize Templates](https://docs.granola.ai/help-center/taking-notes/customise-notes-with-templates)
## Next Steps
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