| name | predictleads-dashboard |
| description | Use when a teammate wants to visually browse PredictLeads signals already cached in local SQLite — triggers include "dashboard for [domains]", "visualize signals for [list]", "show signals as a dashboard", "HTML view of [client lookalikes]", or any request to scan many companies' signals at a glance. |
PredictLeads Dashboard (HTML viz)
Generates a single self-contained HTML page from cached signals in ~/.gtm-os/gtm-os.db. Cards per company with signal-count badges, top-signal callout, expandable detail (recent jobs, news, funding, tech stack, similar companies). Filter by vertical, sort by signal density or recency. Auto dark/light. Zero API calls.
When to use
- After running
prospect-discovery-pipeline to scan all 10 finalists in one view
- After bulk-enriching a campaign result set (
signals:enrich --result-set) for a visual sanity check before outreach
- Sharing signal context with a non-technical teammate (open the HTML, no CLI knowledge needed)
Don't use when: you only have signals for 1–2 companies (just use signals:show); signals haven't been pulled yet (run signals:fetch first).
How to invoke
The dashboard is built by a small Python script. Pass a list of domains and an optional list of pre-built lead cards (name + title + LinkedIn URL).
Inputs the skill needs
- List of domains (must already be in
company_signals table)
- Optional per-domain lead metadata:
{ company, vertical, geo, lead_name, lead_title, linkedin }
Build steps
- Read the lead metadata into a Python dict (see existing template at
~/Desktop/predictleads-dashboard.html for shape).
- Query SQLite for each domain:
SELECT signal_type, COUNT(*) for badge counts- Top 8 jobs by
event_date DESC
- Top 8 news by
event_date DESC
- Top 5 financing events
- Top 12 technologies
- Top 10 similar_companies sorted by
payload.score
- Render the HTML template (see
Implementation below) with embedded JSON.
- Write to
~/Desktop/predictleads-dashboard-{client_or_topic}-{date}.html and open it.
Implementation
A Python generator script lives at scripts/predictleads-dashboard.py (when committed). It reads from ~/.gtm-os/gtm-os.db, accepts a JSON config of leads, and emits a self-contained HTML file.
If the script is missing, model the new one on the prior run captured at ~/Desktop/predictleads-dashboard.html (Apr 30 2026). Key visual elements to keep:
- Per-company card with company name + vertical tag (color-coded) + domain
- Marketing lead pinned at top of each card with LinkedIn link
- 5 signal-type badges with counts (
jobs / funding / news / tech / similar)
- "Top signal" callout with the most recent dated signal across types
- Expandable detail section (jobs/news/financing/tech/similar lists)
- Filter pills (All / vertical) + sort pills (density / recency / vertical)
Quick reference
python3 scripts/predictleads-dashboard.py \
--domains personio.com,oysterhr.com,...,mirakl.com \
--leads-json /tmp/leads.json \
--out ~/Desktop/predictleads-dashboard.html
open ~/Desktop/predictleads-dashboard.html
Common pitfalls
- Empty cards: signals haven't been fetched yet. Run
signals:fetch --domain X first.
- News headlines blank: PredictLeads news payloads use
summary not title. The template's display logic falls through payload.title || payload.headline || payload.summary.
- Tech stack shows blanks: technology names live in JSON:API
relationships.technology.data.id resolved via included[]. The normalizer in predictleads-enrichment.ts already promotes payload.technology to a top-level string. Older signals fetched before the normalizer fix may have empty tech rows; re-fetch with --no-cache.
- Similar companies show only score: same root cause — re-fetch with
--no-cache to populate the similar_company field with the resolved domain.
Required env
None for generation (it's local-only). The signals must already be cached, which means PREDICTLEADS_API_KEY + PREDICTLEADS_API_TOKEN had to be set when the cache was populated.
