Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add shawnpang/startup-founder-skills --skill community-discoverygit clone --depth 1 https://github.com/shawnpang/startup-founder-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shawnpang/startup-founder-skills/community-discovery)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/community-discovery"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/community-discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/community-discovery"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/community-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.01539 |
| Opus 5 | $0.00028 | $0.00770 |
| Sonnet 5 | $0.00011 | $0.00308 |
| Haiku 4.5 | $0.00006 | $0.00154 |
Grade A, and why
community-discovery scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- community-discovery — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Community Discovery
When to Use
- Founder wants to find online communities where their target customers are active
- Founder wants to identify channels for organic promotion and community-led growth
- Founder wants to build relationships in relevant communities before launching
- Founder wants to find beta testers, early adopters, or design partners
- Founder wants distribution beyond paid ads and cold outreach
Context Required
- Target customer profile (role, industry, interests, seniority)
- Product category and the problem it solves
- What the founder wants from communities (feedback, users, partnerships, awareness)
- Founder's bandwidth for community engagement (lurk and post vs. become a regular)
Workflow
- Map the community landscape — identify where your target audience spends time online:
- Reddit: find subreddits by searching for the problem you solve, competitor names, and industry terms. Check subscriber count, post frequency, and moderation rules.
- Slack: search Slofile, Slack directories, and Google "[industry] slack community" to find relevant workspaces.
- Discord: search Disboard, Discord.me, and Google "[topic] discord server" for relevant servers.
- Forums & others: Indie Hackers, Hacker News, Stack Overflow, niche forums, Facebook Groups, LinkedIn Groups.
- Qualify each community — not all communities are equal. Score each on:
- Relevance: does your target customer actually hang out here?
- Activity: are there regular posts and discussions (not a ghost town)?
- Size: big enough to matter, small enough to stand out (sweet spot: 1K-50K members)
- Promotion tolerance: does the community allow product mentions, or is it strictly no self-promotion?
- Quality of discussion: are conversations substantive or just spam and memes?
- Categorize by engagement type — sort communities into:
- Promote: explicitly allows product sharing, launch announcements, or "Show X" posts
- Contribute-first: allows organic mentions if you're a genuine, helpful member first
- Listen-only: valuable for research and voice-of-customer, but no promotion allowed
- Build the engagement plan — for each community:
- Join and observe for 1-2 weeks before posting
- Identify the norms (how do regulars communicate? what gets upvoted/praised?)
- Plan your first 5 contributions (helpful answers, not product pitches)
- Plan when and how to introduce your product (if appropriate)
- Create the community map — output a prioritized list with engagement strategy for each.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 122 lines · 55 tokens per session scan A afbde436fdc7
community-discovery is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 5mo ago), licensed MIT. It adds 55 tokens to every session and 1,539 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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