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BB ontology-term-resolution

Resolves free-text scientific labels to ontology term IDs and validates existing CURIEs against the EBI Ontology Lookup Service (OLS4). Also looks up prefixes in Bioregistry, resolves compact identifiers via Identifiers.org, maps lab shorthand with ZOOMA, and builds Ontobee term pages. Use whenever an ontology identifier must be produced or checked - annotating tissue, cell type, disease, phenotype, assay, chemical, organism, sex, or developmental stage fields; preparing metadata for GEO, ENA, BioSamples, CELLxGENE, HCA, or ISA-Tab submission; auditing a metadata table of term IDs; checking whether a term is obsolete and what replaced it; or deciding HPO vs HP. Triggers include "ontology term", "ontology ID", "CURIE", "controlled vocabulary", "UBERON", "CL:", "MONDO", "HPO", "EFO", "ChEBI", "NCBITaxon", "GO term", "PATO", "Zooma", "Bioregistry", "Identifiers.org", "Ontobee", "annotate this tissue/cell type/disease", and any request to emit or verify an identifier shaped like PREFIX:0001234.

K-Dense-AI/claude-scientific-skills Agent Skills author: K-Dense-AI MIT 12 files · 7 scripts body ≈ 3 273 tokens Open the sourcegithub.com↗ analyzed 11 h ago

Resolves free-text scientific labels to ontology term IDs and validates existing CURIEs against the EBI Ontology Lookup Service (OLS4).

As a process B 69/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerData and analyticsAI and agentstype 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
B
90/100
safety, quality, tests
Safety 60%
91
Quality 40%
88
Run on models
none yet
Process rating
B
69/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
40
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • 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/companion-apis.md:111
      Long base64-looking blob (documentation of a security skill)
      [v2 contract](https://github.com/EBISPOT/zooma2/blob/dev/backend/src/main/java/uk/ac/ebi/zooma2/api/v2/Zoom…ava).
      security skill
    • low Obfuscation obf-base64-blob references/companion-apis.md:138
      Long base64-looking blob (documentation of a security skill)
      [annotation adapter](https://github.com/EBISPOT/zooma2/blob/dev/backend/src/main/java/uk/ac/ebi/zooma2/api/v2/dto/V2An…ava)
      security skill
    • low Obfuscation obf-base64-blob references/companion-apis.md:139
      Long base64-looking blob (documentation of a security skill)
      and [confidence logic](https://github.com/EBISPOT/zooma2/blob/dev/backend/src/main/java/uk/ac/ebi/zooma2/api/v2/dto/V2Co…ava).
      security skill
    • low Obfuscation obf-base64-blob references/ols4-api.md:5
      Long base64-looking blob (documentation of a security skill)
      [current implementation](https://github.com/EBISPOT/ols4/tree/dev/backend/src/main/java/uk/ac/ebi/spot/ols/controller/api/v1),
      security skill

    Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 81, 104, 114, 148, 162, 166): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 69/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 4 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3273 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 1005: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -33 of 7 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +5Description quotes 10 example trigger phrases
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 4 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.