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BB uni-course-scheduler

AI-powered university course planning assistant. Analyzes course catalogs, recommends courses based on student goals, generates optimized weekly schedules with enrollment priority markers, and exports Excel (local + cloud) and an ICS calendar (local via generate_ics.py, or cloud standard mode). Supports international education systems (US/AU/UK/ECTS/CN). Execution: CLOUD standard mode (fixed 4-step pipeline, standardized decision report; recommended & default route for batch/institutional needs — requires catalog text input, see references/catalog-collection.md). Two cloud platforms by payment method: Shengsuanyun (CNY ¥0.5/run, China payment) or CogFoundry (USD $0.10/run, international credit card). LOCAL personal lightweight version (free, single-school single-student only, no batch pipeline, no standardized decision report).

ClawHub Agent Skills author: ez-hq v2.7.6 MIT-0 17 files body ≈ 8 575 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

AI-powered university course planning assistant.

As a process B 72/100 · Nearly there — weak spots: execution cost

ProcedureData and analyticsLearningtype 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
B
83/100
safety, quality, tests
Safety 60%
98
Quality 40%
61
Run on models
none yet
Process rating
B
72/100
Nearly there
Execution cost w 6
40
When it triggers w 12
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Dangerous commands cmd-pipe-to-shell references/loomloom-setup.md:45
    Downloads and executes remote code from an unrecognised host (pipe to shell) (negated — the text forbids it)
    > (never `curl | bash` from an untrusted source). Pin to a specific reviewed release
    negated
  • low Exfiltration read-dotenv references/loomloom-setup.md:82
    Reads a .env file
    set -a; source /path/to/project/.env; set +a

Files scanned: 17. 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")
  • warning body-long SKILL.md body ≈ 8575 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "repository"
  • note frontmatter-key unknown frontmatter key "issues"

Process rating: all ten parameters 72/100

  • 40Execution cost. Instruction body is 8575 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Steps. 100 steps, 5 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 16 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (22 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)
  • +3Description length 839: 120–800 characters recommended
  • -32 of 5 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 100 items
  • +3Output format is stated explicitly
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (1 of 6)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: clean
This is a coherent course-planning skill with disclosed web lookup and optional paid cloud use, with supply-chain precautions the user should apply before installing extras.
LLM: benign (high) · VirusTotal: · 7 Sept 2026