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AC jetlag-planner

Scans your Google Calendar for upcoming flights and writes a personalized circadian adjustment plan back to your calendar. Trigger with phrases like "check my flights", "run jetlag planner", "plan my trip adjustment", or "am I ready for my upcoming flight".

ClawHub Agent Skills author: chadholdorf v0.1.0 MIT-0 5 files body ≈ 528 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorPersonal productivitytype 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
94/100
safety, quality, tests
Safety 60%
97
Quality 40%
89
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Exfiltration read-dotenv index.js:84
      Reads a .env file (quoted — discussed, not commanded)
      console.error('  Run:  cp .env.example .env');
      quoted
    • low Exfiltration read-dotenv README.md:23
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv README.md:51
      Reads a .env file
      cp .env.example .env

    Files scanned: 5. 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 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (jetlag-planner) differs from the folder (openclaw-jetlag)
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70Failures and branches. 6 branches
    • 100Steps. 4 steps
    • 100Execution cost. Instruction body is 528 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 257: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 4 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    The skill does what it says, but it needs careful review because it asks for durable Google Calendar access and includes unsafe credential-sharing setup guidance.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026