AD gzh-operation-optimizer
【公众号运营优化（火焰）】火焰广告传媒出品。输入任意公众号文章（URL或全文），八维度爆款诊断：标题吸引力/开篇留存力/内容价值密度/情绪设计/结构节奏感/传播触发器/互动引导力/视觉与排版，输出0-100爆款潜力评分、火/不火归因分析、可借鉴亮点提炼、逐维度优化改法和运营增长策略。写完文章发来即诊，同行爆款发来拆解，持续提升公众号数据表现。触发词：公众号分析、文章分析、为什么火、为什么不火、爆款拆解、文章诊断、公众号优化、内容增长、运营优化。
【公众号运营优化（火焰）】火焰广告传媒出品。输入任意公众号文章（URL或全文），八维度爆款诊断：标题吸引力/开篇留存力/内容价值密度/情绪设计/结构节奏感/传播触发器/互动引导力/视觉与排版，输出0-100爆款潜力评分、火/不火归因分析、可借鉴亮点提炼、逐维度优化改法和运营增长策略。写完文章发来即诊，同行爆款发来拆解…
As a process D 41/100 · Unfinished process — 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.
- 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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 41/100
- 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
- 40Consistency. Frontmatter name (gzh-operation-optimizer) differs from the folder (gzh-content-growth-analyzer)
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 49 steps
- 100Execution cost. Instruction body is 1875 tokens
- 100Running it twice. No mutating operations
- low 14 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
- +1No license
- +2Single-language instructions
- +3Description length 224: enough signal without eating the budget
- +4Structure: 37 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.