| name | github-stars-monitor |
| description | Cron-triggered: monitor GitHub stars and forks on [YOUR_PRODUCT] repos, research new stargazers by profile, alert CEO when someone interesting stars/forks. Runs automatically. |
| user-invocable | false |
GitHub Stars & Forks Monitor
Track who stars and forks [YOUR_PRODUCT] repos. Research their profile. Alert when someone from a notable company or with a large following engages.
Why
A GitHub star from a VP Eng at Stripe is worth more than 100 random stars. Forks are even stronger — they indicate intent to evaluate. Cross-referencing GitHub profiles with company data surfaces enterprise prospects hiding in OSS engagement.
Setup
Requires two env vars in ~/.openclaw/gateway.env (or your agent's secrets store):
GITHUB_TOKEN=<fine-grained token with read access to Starring + Contents for the org>
MONITORED_REPOS=owner/repo-1,owner/repo-2,owner/repo-3
Repos to Monitor
Set via the MONITORED_REPOS env var (comma-separated owner/repo entries). Examples:
MONITORED_REPOS=acme-org/acme-cli,acme-org/acme-mcp,acme-org/acme-skills
Workflow
Step 1: Collect New Stars & Forks
Run the collector script. It handles pagination (per_page=100), deduplication against state, and profile enrichment automatically:
python3 /home/openclaw/.openclaw/workspace/skills/github-stars-monitor/collect.py
CRITICAL: Use the exact absolute path above. Do NOT use cd /path && python3 script.py (triggers exec preflight block). Do NOT append 2>&1. Direct form only.
Output is JSON:
{
"new_stargazers": [{"repo": "...", "username": "...", "starred_at": "...", "profile": {"name": "...", "company": "...", ...}}],
"new_forkers": [{"repo": "...", "username": "...", "forked_at": "...", "profile": {...}}],
"repo_stats": {"owner/repo-1": {"total_stars": N, "new_stars": N, ...}},
"total_new": N
}
State is saved to memory/github-stars.json automatically — processed users won't appear again on next run.
If total_new == 0 → reply HEARTBEAT_OK, done.
Step 2: Qualify
For each new stargazer/forker, score as HOT / WARM / SKIP using profile data from the collector output:
HOT (alert immediately):
company field mentions a known tech company (>100 employees)followers > 500 (influential developer)bio contains leadership keywords: VP, CTO, Director, Head, Lead, Principal, Staff- Profile shows they match your ICP profile (active repos, relevant tech stack, contribution patterns)
WARM (include in summary):
- Business company in
company field but small/unknown
- Moderate activity (20+ public repos)
- Forked (stronger signal than star)
SKIP:
- Empty profile, no company, <5 repos, bot-like
Step 3: Research HOT Candidates
For HOT candidates, run Exa to find more about their company:
exa-search__web_search_advanced_exa \
query="COMPANY_NAME company" \
category=company \
numResults=3 \
type=auto
If stargazer has no company but has a personal site in blog, check it via Exa for affiliation.
Step 4: Alert
For HOT candidates:
GitHub: [username] starred [repo]
[Real name] — [bio/title]
[Company] — [one-line from Exa research]
[followers] followers | [public_repos] repos
Profile: github.com/USERNAME
Summary for WARM:
Also starred: [N] others ([username1] @ [company], ...)
Repo stats summary:
Stars: [repo1] N (+new), [repo2] N (+new)
Notes
- GitHub API rate limit: 5000 req/hour with token. Collector uses ~10-50 requests per run depending on star count.
- Fork is a stronger signal than star — forkers appear first in output.
- State file keeps up to 2000 processed users per repo. Old entries trimmed automatically.
- First run will report ALL existing stargazers as "new". Review and send HEARTBEAT_OK if no HOT candidates.
Related Skills
- signup-monitor — if a stargazer later signs up, connect the dots
- exa-company-research — deep company research for HOT leads
- exa-people-research — deep people research
- pipeline-manager — add HOT stargazers as prospects
