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AB familysearch

Search, explore, and analyze family history using the FamilySearch API and offline GEDCOM files. Use when the user asks about genealogy, ancestors, family trees, family history research, pedigree charts, or wants to look up relatives. Supports live FamilySearch API queries (person search, ancestry/descendancy, relationships, historical records) and offline GEDCOM file parsing (search, profiles, narrative biographies, statistics). Trigger on: "search my family tree", "who are my ancestors", "tell me about [ancestor name]", "family tree", "GEDCOM", "genealogy", "FamilySearch".

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 1 240 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Search, explore, and analyze family history using the FamilySearch API and offline GEDCOM files.

As a process B 65/100 · Nearly there — weak spots: result and completion, failures and branches, progress reporting

IntegrationData and analyticsAI and agentsSoftware 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%
88
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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: 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 65/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 24 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1240 tokens
    • 100Running it twice. No mutating operations
    • low The response is described with custom markup (4 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
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 581: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 24 items
    • +4Has examples (5 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.