BC gingiris-opensource
🇺🇸 Open-Source Marketing & GitHub Stars Growth System — Decision framework for each growth stage. Pre-launch readiness → Show HN / Reddit / Product Hunt launch → 6-month growth loop. README rules, KOL outreach, dev-content matrix (dev.to / Zenn), anti-star-farming. Built from taking AFFiNE 0 → 60K stars. 🇨🇳 开源营销与 GitHub Star 增长系统 — 每个增长阶段的决策框架。Pre-launch 就绪 → Show HN / Reddit / Product Hunt 发布 → 6 个月增长循环。README 规则、KOL 合作、开发者内容矩阵（dev.to / Zenn）、防刷 star。提炼自 AFFiNE 0 → 60K star 实战。 🇯🇵 オープンソースマーケティング＆GitHub スター成長システム — 各成長段階の意思決定フレームワーク。Pre-launch → Show HN / Reddit / Product Hunt → 6ヶ月成長ループ。READMEルール、KOL連携、開発者コンテンツマトリックス。 🇰🇷 오픈소스 마케팅 & GitHub 스타 성장 시스템 — 각 성장 단계별 의사결정 프레임워크. 사전 출시 → Show HN / Reddit / Product Hunt → 6개월 성장 루프. README 규칙, KOL 아웃리치, 개발자 콘텐츠 매트릭스. Triggers: "open source marketing" | "GitHub stars" | "star growth" | "Show HN" | "Hacker News launch" | "OSS launch" | "developer marketing" | "Reddit launch" | "dev.to" | "open source growth" | "开源营销" | "GitHub star 增长" | "开源发布" | "Show HN 怎么发" | "开发者营销" | "オープンソース" | "오픈소스 성장"
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Shorten the description to 1024 characters.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1061 chars, limit 1024 - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 108 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2864 tokens
- high The skill tells the model to perform an irreversible action with no human approval
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1060: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 14 example trigger phrases
- +4Structure: 35 headings
- +3Step-by-step instructions: 108 items
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 49.