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 Gingiris-1031/gingiris-skills --skill gingiris-opensourcegit clone --depth 1 https://github.com/Gingiris-1031/gingiris-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/gingiris-1031/gingiris-skills/gingiris-opensource)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/gingiris-opensource"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gingiris-opensource/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/gingiris-1031/gingiris-skills/gingiris-opensource"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gingiris-opensource.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 74 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 275 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00431 | $0.05118 |
| Opus 5.5 | $0.00172 | $0.02047 |
| Sonnet 5.5 | $0.00086 | $0.01024 |
| Haiku 4.5 | $0.00043 | $0.00512 |
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 6d 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 — 323 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.
2026 launch contract (run before any channel plan)
Classify every input as Go, Fix, or No-Go across six gates: positioning,
clean-machine quickstart, reproducible proof, license/security/privacy, response
ownership, and business conversion. Do not compensate for a failed product or
documentation gate with more distribution.
Build one operating row per channel:
channel | ICP | native angle | CTA | source/UTM | owner | local time + timezone |
success threshold | stop condition | evidence URL
Use the source-sheet workflow as five linked views: research, content, distribution, measurement, and launch control. Keep platform rules, prices, account status, creator metrics, and contact lists date-stamped and re-verify them in launch week.
Track the complete funnel:
impression -> repo visit -> install/quickstart -> activation -> D7 retained -> contribution/qualified lead -> paid. At 2h check only operational anomalies; at 24h
continue/change/pause; at 72h choose the second wave; at 7d decide reuse and budget.
Mark unavailable native metrics as data unavailable; never infer them from views.
Hard boundaries: never buy stars/accounts, coordinate fake or incentivized votes, hide paid relationships, scrape private data, mass-spam DMs/comments, or use AI to impersonate genuine community participation. Disclose affiliation on community posts.
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).
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.
- 6d ago Changed · +14 lines abef72f1d67d
- 1mo ago Changed · +33 lines 197e2c9728e1
- 1mo 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 (86 stars, last pushed 6d ago), licensed MIT. It adds 431 tokens to every session and 5,118 once invoked, about $0.0017 per session on Opus 5.5. 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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