FD vectcut-skill
VectCut 全能剪辑聚合技能。用于剪口播、混剪、电商广告等场景,统一编排流光剪辑能力(字幕、音频、特效、画中画、抠像、AI补镜、AI配音、预设、平台视频抓取、渲染导出),也支持把既有视频制作流程整理为飞书多维表格自动化工作流。当用户提出“做成片/自动剪辑/口播包装/广告剪辑/抓平台视频后再剪”,或想把视频制作流程固化为飞书多维表格 AI Agent 工作流时,必须优先使用本技能。
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Remove the critical guard findings (secrets, dangerous commands, hidden instructions): while they stand the skill is blocked and cannot grade above F.
- 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 · 3
-
critical Exfiltration
exfil-send-secrets-to-urlrules/sts-upload.md:40Instruction to send secrets/history to an external endpoint2. **Upload to OSS** — Signs a PUT request using HMAC-SHA1 over the STS credentials and uploads the file directly to `https://{bucket_name}.{oss_endpoint}/{object_key}`.
Medium and low: 2
-
low Obfuscation
obf-hex-escape-chainreferences/bgms.json:13Escaped/char-code string obfuscation (quoted — discussed, not commanded)"https://player.install-ai-guider.top/koubo/bgm/%E5%BF%99%E7%A2%8C%E7%9A%84%E7%94%9F%E6%B4%BB%E4%BA%BA%E5%B1%B1%E4%BA%BA%E6%B5%B7.MP3",
quoted -
low Secrets in code
secret-high-entropy-tokenrules/query-draft.md:64High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"id": "91E0…A2B",
quoted
Files scanned: 69. 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 "homepage" - note
frontmatter-keyunknown frontmatter key "dependency"
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 (vectcut-skill) differs from the folder (vectcut)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 103 steps
- 100Execution cost. Instruction body is 1240 tokens
- 100Running it twice. No mutating operations
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
- +4No input/output examples
- -330 of 31 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 193: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 103 items
- +4Reference files are cited in the instructions (31 of 32)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.