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 agentmods add skills/gingiris-1031/gingiris-skills/gingiris-opensourcenpx skills add Gingiris-1031/gingiris-skills --skill gingiris-opensourcegit clone --depth 1 https://github.com/Gingiris-1031/gingiris-skillsWhat 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 | $0.00431 | $0.04596 |
| Opus 5 | $0.00216 | $0.02298 |
| Sonnet 5 | $0.00086 | $0.00919 |
| Haiku 4.5 | $0.00043 | $0.00460 |
Grade A, and why
gingiris-opensource 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open-Source Marketing — GitHub Stars Growth System
Stars don't come from a great repo — they come from a great repo placed in front of the right developers, in the right order. This is the staged decision framework used to take AFFiNE from 0 to 60K stars.
Case-number and citation boundaries: references/podcast-evidence.md.
English Version
The 3-stage framework
Stage 1 — Pre-launch (T-30 → T-0)
- README in English-first; the first screen must be understandable in <3 seconds.
- Demo video ≤ 60s, captioned.
- License chosen deliberately (MIT / Apache-2 / AGPL each carry traps).
- 3–5 early maintainers / contributors lined up.
- Community channel live (Discord or Telegram, English-first).
Stage 2 — Launch (T-0 → T+14)
- Product Hunt (run the dedicated launch sequence).
- Hacker News Show HN — Tuesday 9am ET, or Saturday.
- Reddit — pick 3 relevant subs (r/selfhosted, r/programming, one niche tech sub).
- Deep technical posts on dev.to / Zenn / CSDN.
- 3–5 KOLs to amplify (activity > follower count).
Stage 3 — Growth (T+14 → T+180)
- One technical blog per week.
- Turn GitHub Issues into content (user questions → FAQ → posts).
- Monthly update (mailing list + Discord).
- Localize for going global (Japan/Korea first).
Star Region Distribution (GitHub Trending unlock)
- No single country/region should exceed 20% of your total stars.
- Global healthy split reference: China 19-21%, US 19-21%, rest scattered across Europe / Russia / Canada / Indonesia.
- Finer benchmark (from studying VSCode / Vue / AppFlowy the week before AFFiNE's launch): US 19-21%, China ~19-21%, FR+DE+IT+UK combined 10-15%; Russia and Brazil usually appear in the Top-10 countries. Week-1 stars matching this curve = healthy global cold start.
- Tools: star-history.com (trend chart) · oss.cool / OSS Insight (per-country breakdown, built by Chinese devs)
- Launch sequence: Week 1 → overseas-only outreach. Week 2 → domestic (Chinese) outreach. Reversing this breaks the distribution and kills Trending eligibility.
- AFFiNE real case: deliberately skipped WeChat Moments / Chinese community posts in week 1.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 276 lines · 431 tokens per session scan A 328f91a5e4c2
gingiris-opensource is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (74 stars, last pushed 15d ago), licensed MIT. It adds 431 tokens to every session and 4,596 once invoked, about $0.0022 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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