mittuled community-signal-extractor
Agent: Community Manager
L2 community manager responsible for extracting community signals, designing community-led growth, building the early community, and maintaining community health.
Department ethos: ideal-marketing.md
Skill Description
Extracts product and market signals from community conversations to surface feature requests, sentiment shifts, and competitive intelligence.
When to Use
- When the product team needs qualitative user feedback beyond structured surveys and support tickets.
- When community conversation volume has grown and manual monitoring no longer catches important signals.
- When preparing for a product planning cycle and needing community-sourced input.
- When a competitor announcement triggers community discussion that contains strategic intelligence.
Workflow
- Define signal categories: Use the signal taxonomy in framework.md to select the categories relevant to this extraction cycle (feature requests, pain points, competitor mentions, praise, churn indicators, use-case discoveries). Deliverable: signal taxonomy document.
- Select data sources: Inventory all relevant channels per the source inventory model in the framework (public channels, DMs with consent, event feedback, social mentions). Deliverable: source inventory with access status.
- Extract and tag signals: Review conversations and tag each signal using the tagging protocol in the framework (category, severity P0–P3, frequency, source attribution, verbatim quote). Deliverable: tagged signal database or spreadsheet.
- Cluster and prioritize: Group signals with > 60% semantic overlap into themes per the clustering method in the framework. Rank P0/P1 signals for immediate routing. Deliverable: prioritized signal report with theme clusters.
- Synthesize insights: Write a narrative summary using signal-report-template.md connecting signal themes to product and market implications. Deliverable: signal report for product and leadership teams.
- Distribute and close loop: Route signals to the appropriate team per the routing matrix in the framework. Post community acknowledgments for signals that led to action per the closing-the-loop protocol. Deliverable: routing log and community acknowledgment posts.
Anti-Patterns
- Cherry-picking signals: Selecting only signals that confirm existing product direction while ignoring contradictory feedback. Why: Confirmation bias wastes the unique value of community data, which is its ability to surface surprises and blind spots.
- Extracting without attribution: Reporting signal themes without linking back to specific conversations or members. Why: Stakeholders cannot verify signal validity or follow up for deeper understanding without source attribution.
- Signal hoarding: Collecting signals but never distributing them to the teams that can act on them. Why: Unreported signals have zero value and erode community trust when members see their feedback disappear without response.
Output
On success: Produces a prioritized signal report and insight memo containing tagged signals, theme clusters, and strategic implications. Delivered to product, marketing, and community leadership.
On failure: Report which data sources were inaccessible, what time periods could not be covered, and recommend alternative extraction methods. Every error must be actionable.
Related Skills
community-health-grower— Sentiment signals extracted here feed into health metric tracking.community-led-growth-designer— Signals about what motivates members inform growth programme design.developer-community-signal-extractor— Parallel skill for developer-specific community signal extraction.