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AC ds160-autofill

Automate filling of US nonimmigrant visa DS-160 forms using CDP for element location, CSV data source for user information, LLM assistance for complex cases (captcha, missing elements), and session persistence for resume capability. Use when user needs to: (1) Fill DS-160 visa application forms automatically, (2) Resume filling an interrupted DS-160 application, (3) Handle captcha and complex form elements with LLM assistance. Supports Chinese input with automatic translation to English.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 3 411 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Automate filling of US nonimmigrant visa DS-160 forms using CDP for element location, CSV data source for user information, LLM assistance for complex cases…

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions

ProcedureAI and agentsData and analyticsWriting and documentstype 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
92/100
safety, quality, tests
Safety 60%
95
Quality 40%
87
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
50
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token references/ds160-elements.yaml:28
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - id: "ctl0…New"
      quoted
    • low Secrets in code secret-high-entropy-token references/ds160-elements.yaml:39
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - id: "ctl0…Act"
      quoted
    • low Secrets in code secret-high-entropy-token references/ds160-elements.yaml:46
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - id: "ctl0…ons"
      quoted
    • low Secrets in code secret-high-entropy-token references/ds160-elements.yaml:69
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - id: "ctl0…wer"
      quoted
    • low Secrets in code secret-high-entropy-token references/ds160-elements.yaml:76
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - id: "ctl0…nue"
      quoted

    Files scanned: 4. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 85Steps. 189 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3411 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 14 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 492: enough signal without eating the budget
    • +4Structure: 30 headings
    • +3Step-by-step instructions: 189 items
    • +4Has examples (13 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.