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AB didit-database-validation

Integrate Didit Database Validation API to verify personal data against government databases. Use when the user wants to validate identity against government records, verify national ID numbers, check CPF/CURP/DNI/cedula numbers, perform identity database lookups, validate identity documents against official sources, or implement database verification for Latin American or Spanish identity documents using Didit. Supports 18 countries with 1x1 and 2x2 matching methods.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 745 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Integrate Didit Database Validation API to verify personal data against government databases.

As a process B 78/100 · Nearly there — weak spots: running it twice, progress reporting

IntegrationSoftware developmenttype 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%
100
Quality 40%
87
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Failures and branches w 10
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 · 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 78/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 4 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1745 tokens

    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 472: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 4 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

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