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AB sf-scraper

Scrape employee data from a logged-in SAP SuccessFactors browser session using browser automation. Use when: user provides an employee ID and wants employee details (name, email, department, manager, etc.) scraped directly from the SuccessFactors UI — NOT via OData/API. Requires the user to have SuccessFactors open and logged in via Chrome with the OpenClaw Browser Relay extension attached. Triggers on: "get employee name", "look up employee", "scrape SF", "find employee in SuccessFactors", or any request combining an employee ID with SuccessFactors data lookup.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 3 080 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Scrape employee data from a logged-in SAP SuccessFactors browser session using browser automation.

As a process B 73/100 · Nearly there — weak spots: result and completion

ProcedureSoftware developmentData 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
73/100
Nearly there
Result and completion w 14
0
Tools and files w 18
60
Inputs and preconditions w 11
70
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: 1. 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 73/100

    • 0Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 134 steps, 2 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 6 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3080 tokens
    • 100Running it twice. No mutating operations
    • 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 (3 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

    • +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 568: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 134 items
    • +4Has examples (11 code blocks)

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