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AD web-autopilot

Record any web app operation once, AI turns it into a reusable automation tool. Use when: (1) automating repetitive tasks on any web application (reports, submissions, data extraction), (2) creating no-code automation for any logged-in web app, (3) building callable tools from recorded browser sessions. Supports REST, GraphQL, form submissions, file uploads, any login method. Task types: query/export (data extraction) and submit (form submissions like expense reports, travel requests, payment requests).

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 10 files body ≈ 4 333 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Record any web app operation once, AI turns it into a reusable automation tool.

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

ProcedureData and analyticstype 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%
100
Quality 40%
85
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
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 · 0

    ✓ No critical or high findings

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 65): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 49/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4333 tokens
    • 85Steps. 68 steps, 3 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (5 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)
    • +3Output format is not stated: the model decides each time
    • -218 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 508: enough signal without eating the budget
    • +4Structure: 33 headings
    • +3Step-by-step instructions: 68 items
    • +4Has examples (16 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.