65 lines
3.3 KiB
Markdown
65 lines
3.3 KiB
Markdown
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## Prompt: Quick Capture Templates
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**Job:** Five copy-paste sentence starters optimized for clean metadata extraction. Each one is designed to trigger the right classification in your AMCS's processing pipeline.
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**When to use:** Keep these handy as a reference. After a week of capturing, you won't need them — you'll develop your own natural patterns. But they're useful for building the habit early.
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**What you'll get:** Five starter patterns with explanations of why each one works.
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> **Why does formatting matter?** Your AMCS's edge function uses an LLM to extract metadata from each capture — people, topics, action items, type. These templates are structured to give that LLM clear signals, which means better tagging, better search, better retrieval.
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**Output feeds into:** N/A — reference tool.
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> **No prompt block below.** Unlike the other four, this isn't something you paste into AI. These are templates for what *you* type into your capture channel or say directly to any MCP-connected AI using "save this" or "remember this."
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### 1. Decision Capture
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```text
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Decision: [what was decided]. Context: [why]. Owner: [who].
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```
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Example: `Decision: Moving the launch to March 15. Context: QA found three blockers in the payment flow. Owner: Rachel.`
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Why it works: "Decision" triggers the `task` type. Naming an owner triggers people extraction. The context gives the embedding meaningful content to match against later.
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### 2. Person Note
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```text
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[Name] — [what happened or what you learned about them].
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```
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Example: `Marcus — mentioned he's overwhelmed since the reorg. Wants to move to the platform team. His wife just had a baby.`
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Why it works: Leading with a name triggers `person_note` classification and people extraction. Everything after the dash becomes searchable context about that person.
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### 3. Insight Capture
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```text
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Insight: [the thing you realized]. Triggered by: [what made you think of it].
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```
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Example: `Insight: Our onboarding flow assumes users already understand permissions. Triggered by: watching a new hire struggle for 20 minutes with role setup.`
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Why it works: "Insight" triggers `idea` type. Including the trigger gives the embedding richer semantic content and helps you remember the original context months later.
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### 4. Meeting Debrief
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```text
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Meeting with [who] about [topic]. Key points: [the important stuff]. Action items: [what happens next].
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```
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Example: `Meeting with design team about the dashboard redesign. Key points: they want to cut three panels, keep the revenue chart, add a trend line. Action items: I send them the API spec by Thursday, they send revised mocks by Monday.`
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Why it works: Hits multiple extraction targets at once — people, topics, action items, dates. Dense captures like this are the highest-value entries in your brain.
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### 5. The AI Save
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```text
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Saving from [AI tool]: [the key takeaway or output worth keeping].
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```
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Example: `Saving from Claude: Framework for evaluating vendor proposals — score on integration effort (40%), maintenance burden (30%), and switching cost (30%). Weight integration highest because that's where every past vendor has surprised us.`
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Why it works: "Saving from [tool]" creates a natural `reference` classification. The content itself becomes searchable across every AI you use. This is how you stop losing good AI output to chat history graveyards.
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