BD tencentcloud-aigc-recog-text
腾讯云 AI 生成文本识别 (TencentCloud Text AIGC Detection) 技能。适用于 AI 生成文本检测、文本真伪鉴别、AI 写作检测、AIGC 检测等场景。TencentCloud Text AIGC Detection is an AI-generated content detection service by Tencent Cloud, supporting aigc detection, AI-generated text identification, and text authenticity review.
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
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 · 3
✓ No critical or high findings
Medium and low: 3
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medium Dangerous commands
cmd-shell-rcSKILL.md:66Writes to a shell startup fileecho 'export TENCENTCLOUD_SECRET_ID="你的SecretId"' >> ~/.zshrc
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medium Dangerous commands
cmd-shell-rcSKILL.md:67Writes to a shell startup fileecho 'export TENCENTCLOUD_SECRET_KEY="你的SecretKey"' >> ~/.zshrc
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medium Dangerous commands
cmd-shell-rcSKILL.md:68Writes to a shell startup fileecho 'export TENCENTCLOUD_AIGC_RECOG_TEXT_BIZ_TYPE="你的BizType"' >> ~/.zshrc
Files scanned: 4. 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")
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 29 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1622 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
- -215 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 278: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 29 items
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.