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AF pharmacoeconomic-evaluation

This skill provides comprehensive guidance and tools for conducting pharmacoeconomic evaluations including cost-effectiveness analysis (CEA), cost-utility analysis (CUA), cost-benefit analysis (CBA), budget impact analysis (BIA), sensitivity analysis, and decision-analytic model construction (Markov, decision tree, DES, PSM). Follows ISPOR Good Practices for Outcomes Research Reports. Use this skill for HTA projects, drug pricing, reimbursement decisions, and health economic research.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 7 files body ≈ 4 128 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill provides comprehensive guidance and tools for conducting pharmacoeconomic evaluations including cost-effectiveness analysis (CEA), cost-utility…

As a process F 29/100 · Will not run — References files that are not bundled: references/model_methods.md, references/guidelines.md, scripts/example.py

AnalyzerData 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
F
29/100
Will not run
References files that are not bundled: references/model_methods.md, references/guidelines.md, scripts/example.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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

  • warning missing-ref reference to a missing file: references/model_methods.md
  • warning missing-ref reference to a missing file: references/guidelines.md
  • warning missing-ref reference to a missing file: scripts/example.py

Process rating: all ten parameters 29/100

Will not run. References files that are not bundled: references/model_methods.md, references/guidelines.md, scripts/example.py
  • 0Tools and files. 3 referenced file(s) missing: references/model_methods.md, references/guidelines.md, scripts/example.py
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (pharmacoeconomic-evaluation) differs from the folder (skill-pharmacoeconomic-evaluation)
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 4128 tokens
  • 85Steps. 177 steps, 3 vague phrases

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 489: enough signal without eating the budget
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 177 items
  • +4Has examples (6 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.