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Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.

K-Dense-AI/claude-scientific-skills Agent Skills author: K-Dense-AI MIT 8 files · 1 script body ≈ 1 878 tokens Open the sourcegithub.com↗ analyzed 11 h ago

Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes.

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

ProcedureSoftware developmentData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
95
Quality 40%
88
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token references/examples.md:102
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      from pyhealth.tasks import Leng…IC3
      fixture
    • low Secrets in code secret-high-entropy-token references/examples.md:105
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      task = Leng…IC3()
      fixture
    • low Secrets in code secret-high-entropy-token references/tasks.md:17
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; documentation table row)
      | `Leng…IC3`, `Leng…IC4`, `LengthOfStayPredictioneICU`, `LengthOfStayPredictionOMOP` | Matching source | `los: multiclass`; spelling of `eICU` differs from othe
      detectortable
    • low Secrets in code secret-high-entropy-token references/tasks.md:19
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; documentation table row)
      | `Mort…IC4` | MIMIC-IV | StageNet-oriented feature preparation |
      detectortable
    • low Secrets in code secret-high-entropy-token references/tasks.md:49
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | `Ches…ion`, `Ches…ion` | One disease versus multi-disease ChestXray14 targets |
      table

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 7): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 85Steps. 14 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1878 tokens
    • 100Progress reporting. Reports progress

    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 358: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)

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