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BC twitter-viral-optimizer

社交媒体内容传播优化工具，提供推文重写，发布时机规划，话题标签策略与内容分析功能。支持A/B测试方案设计，互动预测，账号诊断与发布日历制定。适用于产品发布推广，个人品牌建设，社媒矩阵运营等社交媒体营销场景。涵盖算法评分，评分卡制作，钩子优化，互动诱导，媒体建议等内容运营能力。内置多平台算法因子分析引擎，含标签策略组合，发布时间窗口推荐。

ClawHub Hermes author: 天轰穿 v1.0.1 MIT-0 2 files body ≈ 3 407 tokens Open the sourceclawhub.ai↗ analyzed 36 h ago

社交媒体内容传播优化工具，提供推文重写，发布时机规划，话题标签策略与内容分析功能。支持A/B测试方案设计，互动预测，账号诊断与发布日历制定。适用于产品发布推广，个人品牌建设，社媒矩阵运营等社交媒体营销场景。涵盖算法评分，评分卡制作，钩子优化，互动诱导，媒体建议等内容运营能力。内置多平台算法因子分析引擎，含标签策略组合…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationGitHubSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
C
53/100
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

The same skill appears in 1 more place: ClawHub

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. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 169 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "pricing_tier"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "displayName"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 69 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3407 tokens
  • 100Running it twice. No mutating operations
  • low 12 top-level sections: this looks like several domains in one skill

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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -223 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 169: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 69 items
  • +4Has examples (22 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
The skill is a social-media writing assistant, but it asks for command execution without clear need or enforceable limits.
LLM: suspicious (high) · 15 Aug 2026