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AC china-travel-planner

Plan and optimize travel within China using flyai / Fliggy search capabilities plus public metro-network data when needed. Use when the user wants a domestic China trip plan, weekend getaway, city itinerary, family trip, holiday route, hotel recommendation, flight comparison, attraction shortlist, budget-based plan, or a practical travel guide that combines transportation, hotel, POI, and day-by-day scheduling for destinations inside mainland China. Also use when the trip has transit constraints such as covering every metro line at least once, choosing hotels by metro convenience, anchoring plans around a fixed hotel, or mixing city travel with nearby side trips.

ClawHub Agent Skills author: gushuaialan1 v1.0.0 MIT-0 30 files · 1 script body ≈ 3 612 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Plan and optimize travel within China using flyai / Fliggy search capabilities plus public metro-network data when needed.

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureAI and agentsInfrastructuretype 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
A
95/100
safety, quality, tests
Safety 60%
96
Quality 40%
94
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Obfuscation obf-hex-escape-chain page-generator/examples/guangdong-2026-04-04/data/trip-data.json:182
      Escaped/char-code string obfuscation (detector / deny-list definition; test fixture / example file)
      "image": "https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/%E4%BD%9B%E5%B1%B1%E7%A5%96%E5%BA%99%E7%9A%84%E9%86%92%E7%8B%AE%E8%A1%A8%E6%BC%94_%28Li…ple%29.jpg/12
      detectorfixture
    • low Secrets in code secret-high-entropy-token page-generator/examples/guangdong-2026-04-04/data/trip-data.json:182
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "image": "https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/%E4%BD%9B%E5%B1%B1%E7%A5%96%E5%BA%99%E7%9A%84%E9%86%92%E7%8B%AE%E8%A1%A8%E6%BC%94_%28Li…ple%29.jpg/12
      fixturequoted
    • low Obfuscation obf-hex-escape-chain page-generator/examples/guangdong-2026-04-04/dist/trip-data.json:182
      Escaped/char-code string obfuscation (detector / deny-list definition; test fixture / example file)
      "image": "https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/%E4%BD%9B%E5%B1%B1%E7%A5%96%E5%BA%99%E7%9A%84%E9%86%92%E7%8B%AE%E8%A1%A8%E6%BC%94_%28Li…ple%29.jpg/12
      detectorfixture
    • low Secrets in code secret-high-entropy-token page-generator/examples/guangdong-2026-04-04/dist/trip-data.json:182
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "image": "https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/%E4%BD%9B%E5%B1%B1%E7%A5%96%E5%BA%99%E7%9A%84%E9%86%92%E7%8B%AE%E8%A1%A8%E6%BC%94_%28Li…ple%29.jpg/12
      fixturequoted

    Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 4 branches
    • 85Steps. 164 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3612 tokens
    • low 10 top-level sections: this looks like several domains in one skill

    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 671: enough signal without eating the budget
    • +4Structure: 41 headings
    • +3Step-by-step instructions: 164 items
    • +3Output format is stated explicitly
    • +4Has examples (17 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    This is a real China travel-planning skill, but its optional publishing flow can change and push a GitHub Pages branch without enough safeguards.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026