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CC cancer-genomics-analysis

Computational cancer genomics workflows. Somatic mutation detection and annotation, structural variation characterization, copy number analysis, tumor purity/ploidy estimation, NMF metagene extraction, and DNA damage response network analysis. For cancer mutation databases use cosmic-database; for variant clinical significance use clinvar-database.

synthetic-sciences/OpenScience Agent Skills author: synthetic-sciences Apache-2.0 5 files · 4 scripts body ≈ 5 488 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Computational cancer genomics workflows.

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5488 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 5488 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 26 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • 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
  • -34 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 350: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (14 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.