BC narrator-ai-cli
AI电影解说视频自动生成技能（AI解说大师 CLI Skill）。当用户需要创建电影解说视频、短剧解说、影视二创、AI配音旁白视频、film commentary、video narration、drama dubbing、movie narration时触发。内置93部电影素材、146首BGM、63种配音音色（11种语言）、90+解说模板。通过narrator-ai-cli命令行工具实现：搜片选片→选择模板→选BGM→选配音→生成文案→合成视频的全流程自动化。CLI client for Narrator AI (AI解说大师) video narration API. Use when user needs to create AI narration videos, manage narration tasks, browse dubbing/BGM/material resources, or automate video production.
As a process C 60/100 · Has gaps — weak spots: result and completion, consistency, execution cost
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:539High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"task_result": "{\"agent_unique_code\": \"narr…OXO\"}"quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:544High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)→ `learning_model_id = "narr…OXO"`
quoted
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 8814 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (narrator-ai-cli) differs from the folder (narrator-ai-cli-skill)
- 40Execution cost. Instruction body is 8814 tokens: crowds the task out of the window
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 100Steps. 33 steps
- 100Inputs and preconditions. Inputs and preconditions are listed
- 100Failures and branches. 7 branches, has a failure section
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 17 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (10 tags): a typed call is more reliable
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
- +2Single-language instructions
- +3Description length 433: enough signal without eating the budget
- +4Structure: 37 headings
- +3Step-by-step instructions: 33 items
- +4Has examples (36 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.