AI agent skills package open-source repo growth tactics
The `marketingskills/open-source-growth` GitHub repo provides two AI agent skills — `repo-growth-operator` and `ecosystem-inclusion-operator` — that equip an AI agent to audit, improve, and promote an open-source project the way a dedicated growth team would.
Score breakdown
The repo packages open-source growth tactics — repo auditing, ecosystem inclusion PR outreach, and trust-file scaffolding — into structured agent skills that any AI agent can load and execute, making growth work that previously required human judgment or a dedicated team directly automatable.
- 01Two agent skills are provided: `repo-growth-operator` and `ecosystem-inclusion-operator`
- 02`repo-growth-operator` covers README audits, install flow fixes, demo generation, launch packs, trust file scaffolding, monetisation design, and social proof
- 03`ecosystem-inclusion-operator` finds awesome lists and directories, scores targets, opens inclusion PRs, and tracks progress with `/loop`
The `marketingskills/open-source-growth` repository, posted to Hacker News by user 35mm, provides two AI agent skills designed to close the gap between building an open-source repo and making it discoverable, trustworthy, and installable. The project's framing is explicit: those are treated as entirely separate skill sets, and the repo packages the latter so an AI agent can act as a growth team for any GitHub project.
Agents are directed to read `AGENTS.md` first, then load the relevant `SKILL.md` file.
The two skills are `repo-growth-operator` — which covers README auditing, install flow repair, demo generation, launch pack creation, trust file scaffolding, monetisation design, and social proof building — and `ecosystem-inclusion-operator`, which locates relevant awesome lists, directories, and repos, scores them as targets, opens non-spammy inclusion PRs, and tracks outreach progress via a `/loop` command. Agents are directed to read `AGENTS.md` first, then load the relevant `SKILL.md` file. Humans can invoke the same functionality by copying a plain-language prompt into any AI assistant.
The repo includes a self-audit capability via `python skills/repo-growth-operator/scripts/self_audit.py`. Running it against the repo itself produced a score of 65/95, with the top identified blockers being the absence of an animated demo, a Roadmap file, and a release tag. A score badge embeddable in any repo's README is also available.
Key facts
- 01Two agent skills are provided: `repo-growth-operator` and `ecosystem-inclusion-operator`
- 02`repo-growth-operator` covers README audits, install flow fixes, demo generation, launch packs, trust file scaffolding, monetisation design, and social proof
- 03`ecosystem-inclusion-operator` finds awesome lists and directories, scores targets, opens inclusion PRs, and tracks progress with `/loop`
- 04Agents load skills by reading `AGENTS.md` then the relevant `SKILL.md`; humans can use a plain-language prompt with any AI
- 05A self-audit script (`skills/repo-growth-operator/scripts/self_audit.py`) scores repos out of 95
- 06The repo scored 65/95 on its own self-audit; top blockers were no animated demo, no Roadmap, and no release tag
- 07An embeddable score badge for READMEs is also available
Topics
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