SKILLEMALL.ai

BC gingiris-go-global

🇺🇸 AI Product / SaaS Go-Global Complete SOP — From competitor research to launch to monetization. A full-cycle playbook covering Phase 0-5 (market validation, positioning, first 100 users, user interviews, beta-to-growth) plus open-source launch, Product Hunt, Reddit, SEO/GEO, conversion, and org principles. **Defaults to dev tools / OSS / B2B SaaS; for 2C products (education, apps, games) the launch channels & metrics differ — see gingiris-seo-geo/references/2c-adaptation.md.** 🇨🇳 AI 产品 / SaaS 企业出海完整 SOP — 从竞品调研到 Launch 到商业化的全流程操作手册。覆盖 Phase 0-5(市场验证、定位、前100用户、用户访谈、Beta转增长)+ 开源发布、Product Hunt、Reddit、SEO/GEO、转化与组织原则。**默认面向开发者工具/开源/B2B SaaS;2C 产品(教育/应用/游戏)的启动渠道与指标不同,见 gingiris-seo-geo/references/2c-adaptation.md。** 🇯🇵 AI製品/SaaS海外展開フルサイクルSOP — 競合調査からローンチ、マネタイズまで。Phase 0-5(市場検証、ポジショニング、最初の100ユーザー、ユーザーインタビュー、ベータから成長)+オープンソース、Product Hunt、Reddit、SEO/GEO、コンバージョン、組織原則。 🇰🇷 AI 제품/SaaS 글로벌 진출 완전 SOP — 경쟁사 조사부터 런칭, 수익화까지 전 주기 플레이북. Phase 0-5(시장 검증, 포지셔닝, 첫 100명 사용자, 사용자 인터뷰, 베타→성장) + 오픈소스 런칭, Product Hunt, Reddit, SEO/GEO, 전환, 조직 원칙. Triggers: "go global" | "出海" | "overseas expansion" | "GTM" | "cold start" | "launch strategy" | "international expansion" | "海外增长" | "出海SOP" | "product launch overseas" | "海外进出" | "グローバル展開" | "글로벌 진출" | "go-to-market" | "出海打法"

ClawHub Agent Skills author: Iris Wei v1.1.0 MIT-0 7 files body ≈ 2 640 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
52
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. Shorten the description to 1024 characters.
For the model run — optional
  • 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: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1279 chars, limit 1024
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "source"

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. 2 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 64 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2640 tokens

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 1278: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 64 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 52.

External checks

ClawHub: clean
This is a documentation-only go-to-market playbook with no code execution, persistence, credential handling, or hidden data access.
LLM: benign (high) · VirusTotal: · 17 Jul 2026