SKILLEMALL.ai
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AD torah-scholar

Search and explore Jewish texts (Torah, Tanach, Talmud, Midrash, commentaries) via Sefaria API. Use when researching Torah sources, looking up verses, finding commentaries, cross-references, or preparing divrei Torah. Supports Hebrew and English. Handles references like "Genesis 1:1", "Berakhot 2a", "Mishnah Avot 1:1".

modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files body ≈ 1 211 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Search and explore Jewish texts (Torah, Tanach, Talmud, Midrash, commentaries) via Sefaria API.

As a process D 40/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
D
40/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "keywords"

    Process rating: all ten parameters 40/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Steps. 26 steps, 4 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1211 tokens
    • 100Running it twice. No mutating operations
    • low The response is described with custom markup (6 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
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 320: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.