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tooluniverse-peptide-target-deorphanization

Find the real protein target(s) of a peptide from its sequence — peptide target deorphanization / off-target identification, for ANY target class (GPCR, ion channel, protease, cytokine/growth-factor receptor, enzyme, integrin), not only GPCRs. Use when a peptide has a phenotype but does not bind its hypothesized target, when a peptide binds a target in one species or assay but not another, or to screen candidate targets for an orphan peptide. A target-class router steers a multi-route keyless pipeline (PROSITE/ELM motif, BLAST homology, HGNC/InterPro/GPCRdb/GtoPdb target-family enumeration, OpenTargets phenotype anchor, EnsemblCompara/Alliance cross-species reconciliation) plus optional NVIDIA-NIM co-folding (Boltz2, AlphaFold2-Multimer, OpenFold3) for structural confirmation.

Quality

92%

Does it follow best practices?

Run evals on this skill

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong, operationally dense body: executable script fast paths, a clear six-phase workflow with explicit validation checkpoints and fallback guidance, and well-signaled deep reference material. The main deductions are a dangling evals/evals.json reference, a bash block whose inline comments break copy-paste runnability, and some repetition of the validation result and target-class premise.

Suggestions

Create the referenced evals/evals.json (or remove/correct the reference) — the body directs readers to it for regression checks, but it is absent from the bundle.

Move the '#' flag comments in the deorphanize_peptide.py bash block outside the code fence, or restructure as a runnable command plus a separate flag table, so the command is copy-paste executable.

State the validated exendin-4 control result once (either in the script section or the 'Validation & test' section) and trim the repeated 'not only GPCRs' restatements to recover tokens.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious operational content (CLI flags, modes, gotchas like DPP4 liability, Hemolytik2/PEPlife2 enum limits, the live-broken ConoServer), assuming Claude's competence throughout. Minor trimming is possible: the validated-control result is stated twice (in the script section and again under 'Validation & test'), and the 'not just GPCRs' premise is restated several times. This fits 'efficient; minor instances of over-explanation' better than the level-3 anchor, since most tokens earn their place.

4 / 5

Actionability

Both scripts have full bash invocations with all flags explained, a runnable validated control command with a real sequence, and clearly defined modes and output tiers. It falls short of fully copy-paste-ready, though: the main deorphanize_peptide.py block embeds '#' comments on backslash-continuation lines (invalid bash if pasted), and the per-phase manual calls are deferred to references/phases.md rather than shown. Anchor 4 ('mostly executable guidance; concrete code or commands with minor gaps') is the best fit.

4 / 5

Workflow Clarity

The six-phase pipeline is clearly sequenced in a table mapped to scripts and key tools, with explicit validation checkpoints (Phase 0 tool verification, key-gated dry-run mode, eval assertions for regression) and feedback loops for error recovery (fallback chains when a tool fails, 'degrades to phenotype-only if that resolver is transiently down', insufficient-data handling for protists missing from UniProt). Batch mode has no destructive risk and is covered by the eval checks. This matches the top anchor: clear sequence, explicit validation, error-recovery guidance.

5 / 5

Progressive Disclosure

Good structure: SKILL.md is a genuine overview with clearly signaled, well-motivated one-level-deep references ('read it when a script step fails, when you want to drive a phase manually...') to references/phases.md, and both referenced scripts exist in the bundle. The gap keeping it below the top anchor: the body points to 'evals/evals.json' for reproducible test prompts, but no evals/ directory exists in the bundle — a dangling reference — and the ~25-line supplementary database table is borderline inline material that could live in a reference file.

4 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: concrete actions, an explicit 'Use when' clause with three realistic trigger scenarios, comprehensive natural trigger terms, and a highly distinct niche. The only cost is density — the tool-chain enumeration is long — but every clause is informational rather than padded.

DimensionReasoningScore

Specificity

"Find the real protein target(s) of a peptide from its sequence — peptide target deorphanization / off-target identification" plus "screen candidate targets for an orphan peptide" lists multiple concrete actions, and the pipeline enumeration (PROSITE/ELM motif, BLAST homology, target-family enumeration, phenotype anchor, cross-species reconciliation, co-folding) gives comprehensive, explicitly stated coverage. This matches the top anchor; the level-4 anchor ('minor gaps in coverage') does not apply since every major capability of the pipeline is named.

5 / 5

Completeness

What is answered explicitly ("Find the real protein target(s) of a peptide from its sequence", for "ANY target class (GPCR, ion channel, protease, cytokine/growth-factor receptor, enzyme, integrin), not only GPCRs") and when is answered with an explicit "Use when..." clause listing three concrete trigger scenarios. This matches the top anchor exactly; anchor 4 ('when could be more explicit or specific') is clearly below what is present.

5 / 5

Trigger Term Quality

Natural phrases a user would actually say are present verbatim: "peptide has a phenotype but does not bind its hypothesized target", "binds a target in one species or assay but not another", "orphan peptide", "off-target identification", "deorphanization". These cover the common synonyms for this need; the score-4 anchor ('a few natural terms missing') is not met — even species-mismatch and negative-assay phrasings are included.

5 / 5

Distinctiveness Conflict Risk

"Peptide target deorphanization / off-target identification" is a clear niche with distinct triggers (orphan peptide, hypothesized-target negative, species-mismatch binding) and a named tool pipeline; minimal overlap risk with any generic bioinformatics or peptide-property skill. No neighboring anchor fits better.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

mims-harvard/ToolUniverse

Path

plugin/skills/tooluniverse-peptide-target-deorphanization/SKILL.md

Reviewed

Table of Contents

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