SKILLEMALL.ai
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AD haplo-donor-selection

Haploidentical donor selection for hematopoietic cell transplantation (HCT). Extracts HLA typing from images/PDFs, queries IPD-IMGT/HLA APIs (B-Leader, DPB1 TCE, KIR Ligand), applies TCE-Core reclassification (Solomon 2024), calculates 3-year DFS via local Cox model (Fuchs 2022), ranks donors, compares against MSD/MMUD reference outcomes (Mehta 2024), and generates a clinical decision report with PDF export. Use when: user sends HLA typing reports for haplo donor evaluation, asks about B-leader matching, DPB1 TCE permissiveness, KIR ligand analysis, or needs haplo vs MSD/MUD/MMUD outcome comparisons for transplant planning.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 1 294 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Haploidentical donor selection for hematopoietic cell transplantation (HCT).

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
43/100
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: 8. 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 43/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 100Steps. 43 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1294 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)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 631: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 43 items
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 3 scripts are documented

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