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Processes mass spectrometry data with pyOpenMS. Supports proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.

K-Dense-AI/claude-scientific-skills Agent Skills author: K-Dense-AI MIT 24 files · 17 scripts body ≈ 2 896 tokens Open the sourcegithub.com↗ analyzed 13 h ago

Processes mass spectrometry data with pyOpenMS.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubData and analyticsAI and agentsInfrastructuretype 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
88/100
safety, quality, tests
Safety 60%
90
Quality 40%
86
Run on models
none yet
Process rating
C
53/100
Has gaps
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Read Write Edit Bash
    • low Obfuscation obf-base64-blob references/identification.md:99
      Long base64-looking blob (detector / deny-list definition)
      Sources: [FDR release implementation](https://github.com/OpenMS/OpenMS/blob/5d5c…fdf/src/openms/source/ANALYSIS/ID/FalseDiscoveryRate.cpp),
      detector
    • low Obfuscation obf-base64-blob references/metabolomics.md:106
      Long base64-looking blob (detector / deny-list definition)
      Sources: [AccurateMassSearch release implementation](https://github.com/OpenMS/OpenMS/blob/5d5c…fdf/src/openms/source/ANALYSIS/ID/AccurateMassSearchEngine.cpp),
      detector
    • low Obfuscation obf-base64-blob references/metabolomics.md:107
      Long base64-looking blob (detector / deny-list definition)
      [GNPS exporter source](https://github.com/OpenMS/OpenMS/blob/5d5c…fdf/src/openms/source/FORMAT/GNPSMGFFile.cpp),
      detector
    • low Obfuscation obf-base64-blob references/metabolomics.md:108
      Long base64-looking blob (detector / deny-list definition)
      [SIRIUS exporter source](https://github.com/OpenMS/OpenMS/blob/5d5c…fdf/src/openms/source/ANALYSIS/ID/SiriusExportAlgorithm.cpp).
      detector
    • low Obfuscation obf-base64-blob references/signal_processing.md:111
      Long base64-looking blob (detector / deny-list definition)
      Sources: [InternalCalibration release source](https://github.com/OpenMS/OpenMS/blob/5d5c…fdf/src/openms/include/OpenMS/PROCESSING/CALIBRATION/InternalCalibration.h),
      detector

    Files scanned: 24. 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 53/100

    • 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. 7 mutating operations with no state check
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 27 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2896 tokens
    • 100Progress reporting. Reports progress
    • low 12 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)
    • +3Output format is not stated: the model decides each time
    • -31 of 17 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 365: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 27 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +1License stated

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