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DC ai-course-agent

Auto-generates AI education courses from natural language requests in Chinese. Detects patterns like "帮我生成6年级数学分数乘除法的课程" and calls Edustem API to create and return a course link.

Not recommendedcritical or high security findings · low grade D
modbender/skill-library-mcp Agent Skills author: modbender MIT 16 files body ≈ 480 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Auto-generates AI education courses from natural language requests in Chinese.

As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

GeneratorLearningAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
D
59/100
safety, quality, tests
Safety 60%
49
Quality 40%
75
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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.

Exfiltration
If you install

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".

For the author

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

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 9

  • high Exfiltration exfil-webhook-url README.md:26
    Webhook / callback URL commonly used for exfiltration (verify the destination)
    4. Returns → https://6bb9…app/ai-lesson/{lesson_ref}
  • high Exfiltration exfil-webhook-url src/main-session-integration.md:58
    Webhook / callback URL commonly used for exfiltration (verify the destination)
    https://6bb9…app/ai-lesson/3569…659
Medium and low: 7
  • medium Exfiltration exfil-webhook-url src/edustem-api.ts:4
    Webhook / callback URL commonly used for exfiltration (verify the destination) (quoted — discussed, not commanded)
    const API_BASE_URL = "https://6bb9…app/api/v1";
    quoted
  • medium Exfiltration exfil-webhook-url src/edustem-api.ts:5
    Webhook / callback URL commonly used for exfiltration (verify the destination) (quoted — discussed, not commanded)
    const LESSON_BASE_URL = "https://6bb9…app/ai-lesson";
    quoted
  • low Secrets in code secret-high-entropy-token package-lock.json:88
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…cjV+2Z+GK+EEY7…R4x+N3TA…nIr+TMcC…z6Q==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:179
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha512-oGB+Uxlg…BKZ+GTy0…lih/NSHS…cSg==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:198
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…xCA+ORZv…wO5/ywWF…Tag==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:264
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…JbG+vnnQ…1TK+nxAp…hhm+kzE4…g4g==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:304
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…MfZ+71RA…HvA==",
    detector

Files scanned: 16. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 59/100

  • 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
  • 30Running it twice. 3 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 4 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 480 tokens

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 178: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 4 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.