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.
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Example: Input to the PM Discovery Agent
Command-line invocation
bash orchestrate.sh \
--research-question "Why are users abandoning the onboarding flow?" \
--interview-source notion \
--interview-count 10 \
--filter-by-segment "smb"
What the agent reads from your connector
From Notion
The agent automatically pulls from your configured Notion database:
- Most recent N interviews where Status = "Completed"
- For each interview:
- Title (interviewee name or identifier)
- Interview date
- Interviewee role and segment tags
- Full page content (notes, transcript, observations, quotes)
If you've applied a segment filter, only interviews matching that segment are included.
From Google Drive
The agent automatically pulls from your configured folder:
- Most recently modified Google Docs in the folder
- For each doc:
- Document title
- Last modified date
- Full text content
If your filenames follow the YYYY-MM-DD - Name.gdoc convention, the agent uses the date for sorting and the name for interviewee identification.
What the agent does NOT need from you
- A summary of what the interviews said — that's what the agent produces
- Pre-tagged themes — the agent finds them
- A list of which interviews are most important — the agent uses all included interviews
- Statistical analysis — this is qualitative discovery, not quantitative
What you should know before running
- 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.
- 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.
- 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.
Example: Real-world invocations
# Standard discovery synthesis from Notion
bash orchestrate.sh \
--research-question "What's blocking users from completing checkout?" \
--interview-source notion \
--interview-count 8
# Synthesis filtered to a specific segment
bash orchestrate.sh \
--research-question "How are enterprise customers using the API?" \
--interview-source notion \
--interview-count 12 \
--filter-by-segment "enterprise"
# Synthesis from Google Drive folder (all recent interviews)
bash orchestrate.sh \
--research-question "What workflows do power users have that we don't support?" \
--interview-source google-drive \
--interview-count 10
# Smaller batch with low-confidence findings excluded (cleaner stakeholder report)
bash orchestrate.sh \
--research-question "Validate our pricing hypothesis" \
--interview-source notion \
--interview-count 6 \
--include-low-confidence false
# Dry run to validate config
bash orchestrate.sh \
--research-question "Test" \
--interview-source notion \
--dry-run