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BB ehr-semantic-compressor

AI-powered EHR summarization using Transformer architecture to extract key clinical information from lengthy medical records

ClawHub Hermes author: AIpoch v0.1.0 MIT-0 8 files · 1 script body ≈ 1 339 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

AI-powered EHR summarization using Transformer architecture to extract key clinical information from lengthy medical records

As a process B 67/100 · Nearly there — weak spots: when it triggers, progress reporting

GeneratorSoftware developmentWriting and documentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
B
67/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
50
the three weakest of ten parameters · all ten

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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

  • warning description-long-hermes description is 124 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "status"
  • note frontmatter-key unknown frontmatter key "risk_level"
  • note frontmatter-key unknown frontmatter key "skill_type"
  • note frontmatter-key unknown frontmatter key "owner"
  • note frontmatter-key unknown frontmatter key "reviewer"
  • note frontmatter-key unknown frontmatter key "last_updated"

Process rating: all ten parameters 67/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 56 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1339 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 top-level sections: this looks like several domains in one skill

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)
  • +2Single-language instructions
  • +3Description length 124: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 56 items
  • +3Output format is stated explicitly
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

External checks

ClawHub: suspicious
This skill stays local, but it is marked for Review because it handles sensitive medical records while overstating its clinical AI capability and requiring an unrelated unpinned package install.
LLM: suspicious (high) · VirusTotal: · 12 Sept 2026