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AD lobster-use

Runs bioinformatics analysis with Lobster AI -- single-cell RNA-seq, bulk RNA-seq, genomics (VCF/GWAS), proteomics (mass spec/affinity), metabolomics (LC-MS/GC-MS/NMR), machine learning (feature selection, survival analysis), drug discovery, literature search, dataset discovery, and visualization. Use when working with biological data, omics analysis, or bioinformatics tasks. Covers: H5AD, CSV, VCF, PLINK, 10X, mzML formats, GEO/SRA/PRIDE/MetaboLights accessions. TRIGGER PHRASES: "analyze cells", "search PubMed", "download GEO", "run QC", "cluster", "find markers", "differential expression", "UMAP", "volcano plot", "single-cell", "RNA-seq", "VCF", "GWAS", "proteomics", "mass spec", "metabolomics", "MetaboLights", "LC-MS", "metabolite", "feature selection", "survival analysis", "biomarker", "bioinformatics", "drug discovery", "pharmacogenomics", "variant annotation" ASSUMES: Lobster is installed and configured. For setup issues, tell user to run `lobster config-test` and fix any errors before proceeding.

ClawHub Agent Skills author: cewinharhar v1.1.406 4 files body ≈ 1 723 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

AnalyzerData and analyticsInfrastructuretype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "required_binaries"
    • note frontmatter-key unknown frontmatter key "primary_credential"
    • note frontmatter-key unknown frontmatter key "required_env_vars"
    • note frontmatter-key unknown frontmatter key "credential_note"
    • note frontmatter-key unknown frontmatter key "declared_writes"
    • note frontmatter-key unknown frontmatter key "network_access"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "always"

    Process rating: all ten parameters 49/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (lobster-use) differs from the folder (lobsterbio-use)
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 18 steps
    • 100Execution cost. Instruction body is 1723 tokens
    • 100Running it twice. No mutating operations

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 1020: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 22 example trigger phrases
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is a disclosed bioinformatics helper for Lobster AI, with expected network, workspace, and credential use, but users should handle API keys and sensitive data carefully.
    LLM: benign (medium) · VirusTotal: · 29 May 2026