Situation
Your #product-feedback, #customer-success, and #support Slack channels are full of signal. Nobody has time to read every thread, and important requests get buried. Specky reads all of them and surfaces what matters.
What you need
Steps
1. Connect Slack and select channels
In Settings → Integrations → Slack, authenticate with OAuth and select which channels to index. Start with:
#product-feedback or #feedback#customer-success or #cs-escalations#support or #helpAvoid indexing general channels like #general — signal-to-noise ratio is too low.
2. Wait for initial sync (5–20 minutes)
Specky pulls the last 90 days of messages from selected channels and indexes them into your Product Graph as SLACK nodes.
3. Ask for themes
Open AI Chat:
``
What are the top 5 recurring themes in my Slack channels from the last 30 days?
For each theme, show me: how many threads mentioned it, a representative quote,
and the names of the customers who raised it.
`
4. Drill into a specific theme
`
Tell me more about the [theme] theme.
Who mentioned it most recently?
What exactly are they asking for?
Is there anything in my Gong calls or Jira tickets that relates to this?
`
Specky cross-references across Slack, Gong, and Jira simultaneously.
5. Create an opportunity
If a theme is strong enough to act on:
`
Create an opportunity for the [theme] theme.
Use the Slack evidence as the signal.
Score it against our current OKRs.
``
The opportunity is logged in your Product Graph with the Slack threads as evidence nodes.
Output
Variations
Specific customer? Ask: *"What has [Customer Name] mentioned in Slack in the last 90 days?"* Specky finds all messages from that customer across channels.
Enterprise deal context? Ask: *"What are [Company] employees saying in Slack? I'm working on their renewal."* Specky pulls all mentions of that company and synthesises the sentiment.
Triage before a sprint? Ask: *"Which Slack threads from the last 2 weeks haven't been addressed in Jira yet?"* Specky cross-references to find gaps.