mohitagw15856
59c4510055
This release adds three new agent templates to the library, bringing the total to four. New templates: - PM Discovery Agent: synthesises customer interviews from Notion or Google Drive, identifies cross-interview themes, scores assumption confidence, generates follow-up questions - PM Stakeholder Comms Agent: detects audience type (executive/investor/stakeholder/board), pulls activity from Linear/Jira/Drive, drafts in audience-appropriate format - PM Launch Agent: end-to-end launch coordination with channel-specific content, calendar, success metrics, and launch checklist Each template follows the established pattern: README, AGENT.md, orchestrate.sh, 2 subagents, connectors with example configs, examples, smoke test. Total file count: 37 new files across 3 templates. Updated README to position library as 4-template collection. Bumped marketplace.json from v8.0.0 to v9.0.0.
88 lines
3.1 KiB
Markdown
88 lines
3.1 KiB
Markdown
# Example: Input to the PM Discovery Agent
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## Command-line invocation
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```bash
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bash orchestrate.sh \
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--research-question "Why are users abandoning the onboarding flow?" \
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--interview-source notion \
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--interview-count 10 \
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--filter-by-segment "smb"
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```
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## What the agent reads from your connector
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### From Notion
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The agent automatically pulls from your configured Notion database:
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- Most recent N interviews where Status = "Completed"
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- For each interview:
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- Title (interviewee name or identifier)
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- Interview date
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- Interviewee role and segment tags
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- Full page content (notes, transcript, observations, quotes)
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If you've applied a segment filter, only interviews matching that segment are included.
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### From Google Drive
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The agent automatically pulls from your configured folder:
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- Most recently modified Google Docs in the folder
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- For each doc:
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- Document title
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- Last modified date
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- Full text content
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If your filenames follow the `YYYY-MM-DD - Name.gdoc` convention, the agent uses the date for sorting and the name for interviewee identification.
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## What the agent does NOT need from you
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- A summary of what the interviews said — that's what the agent produces
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- Pre-tagged themes — the agent finds them
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- A list of which interviews are most important — the agent uses all included interviews
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- Statistical analysis — this is qualitative discovery, not quantitative
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## What you should know before running
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- **Have at least 5 interviews completed.** The agent works best with 5+ interviews. With fewer, themes will be tagged as "Emerging" rather than "Strong" — directional insights only.
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- **Have a specific research question.** Vague questions produce vague synthesis. "What do users think?" is too broad. "Why are users abandoning the onboarding flow at step 3?" is specific enough to drive useful synthesis.
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- **Check your interview notes are accessible.** The agent can only read what your connector has access to. If notes are in a different database/folder than configured, results will be empty.
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## Example: Real-world invocations
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```bash
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# Standard discovery synthesis from Notion
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bash orchestrate.sh \
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--research-question "What's blocking users from completing checkout?" \
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--interview-source notion \
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--interview-count 8
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# Synthesis filtered to a specific segment
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bash orchestrate.sh \
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--research-question "How are enterprise customers using the API?" \
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--interview-source notion \
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--interview-count 12 \
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--filter-by-segment "enterprise"
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# Synthesis from Google Drive folder (all recent interviews)
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bash orchestrate.sh \
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--research-question "What workflows do power users have that we don't support?" \
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--interview-source google-drive \
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--interview-count 10
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# Smaller batch with low-confidence findings excluded (cleaner stakeholder report)
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bash orchestrate.sh \
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--research-question "Validate our pricing hypothesis" \
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--interview-source notion \
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--interview-count 6 \
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--include-low-confidence false
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# Dry run to validate config
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bash orchestrate.sh \
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--research-question "Test" \
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--interview-source notion \
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--dry-run
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```
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