Galaxy Query Rewrite + Review (Repo Skill)

Use this skill when you want an agent to review all benchmark queries and rewrite any that are:

• tool-leaking (e.g., “perform tool_x”)
• templated / repetitive (especially repeated under the same ground-truth tool)
• dataset-leaking (filenames/accessions/URLs)
• inconsistent with the gold tool’s real purpose

This skill assumes the benchmark source of truth is:

• data/benchmark/v1_items.jsonl

What to produce

1. A clean, updated data/benchmark/v1_items.jsonl (in-place).
2. A refreshed data/benchmark/v1_items_readable.md for human review.

Hard rules (rewrite must satisfy)

1. English only for the query string.
2. Must ask for a Galaxy tool recommendation.
3. Must not mention tutorial/GTN.
4. Must not include dataset identifiers (SRR/ENA IDs, file extensions, URLs, etc.).
5. Must not mention tool IDs, tool names, or backticked “function/tool” strings.
6. Must be close to real user queries:
   • Write from a Galaxy user perspective (what you have + what you want), not a tool developer/maintainer perspective.
   • Include a short, plausible context (data type + goal + expected output).
   • Avoid “benchmarky” language (e.g., “perform X”, “for this task”) with no detail.
   • Avoid sounding like the tool help page; write as a user describing what they need.
7. For the same tool (base id), queries must not be repeated or near-duplicates.

Science-first vs tool-first queries (rewrite guidance)

When rewriting, preserve the starting point style unless the item is too vague:

• Science-first (principle/goal first): user starts from a scientific question (“identify cell types”, “find differentially expressed genes”, “infer variants”). Rewrite by adding the minimal missing “data type + expected output” while keeping it question-driven.
• Tool-first (operation/workflow first): user starts from a concrete step (“QC paired-end FASTQ”, “trim adapters”, “map reads”). Rewrite by making the step goal and output explicit (report/metrics/output files) without drifting into parameter/config instructions.

Balance target (soft)

Across a batch, it’s good to keep science-first and tool-first reasonably mixed, but do not force an exact split (no “make it 75/75 just to match a quota”). Prefer to preserve each item’s existing metadata.query_type, and only change the label when a rewrite would otherwise make the wording inconsistent with the label.

Review workflow (agent checklist)

1. Run the checker:
   • ruby -EUTF-8 skills/galaxy-query-generation/scripts/check_v1_items.rb data/benchmark/v1_items.jsonl
2. Read the batch line-by-line (no skipping):
   • Even if a query passes the checker, rewrite it if it’s still “benchmarky”, too generic, or near-duplicate.
   • Use scripts only to surface candidates; do not rely on scripts as the only filter.
3. Scan for anti-patterns in v1:
   • Tool leakage: backticks, “perform ...”, “run ...”.
   • Copy/paste templates (identical/similar sentences).
   • Dataset leakage (file extensions, accessions, URLs).
4. Enforce within-tool diversity:
   • Group items by tools[0] base id (strip toolshed version).
   • If any group contains duplicated or near-duplicated query text, rewrite them to be clearly different.
5. Ground-truth integrity + expansion checks:
   • If tools[] has multiple entries, ensure it is intentional:
     • metadata.ground_truth_alternatives should be true, and a short metadata.ground_truth_alternatives_note should explain why multiple tools are acceptable.
   • Ensure metadata.tool_focus matches one of the tools[] entries (and reflects the main intended ground truth).
   • If tools[0] is a placeholder/non-stable ID or not runnable on the target server snapshot, consider a manual ground-truth expansion:
     • Add an acceptable Toolshed GUID alternative to tools[] and set metadata.ground_truth_alternatives=true.
     • Do not expand unless you can justify that the alternative is genuinely equivalent for the user intent.
6. Rewrite strategy:
   • Keep the user intent the same, but change perspective/constraints.
   • Add a small realistic constraint when helpful (runtime, reproducibility, “probabilities not labels”, “avoid data leakage”, “save metrics”, etc.).
   • Avoid parameter/config questions.
7. Regenerate readable markdown:
   • python3 -m scripts.benchmark.export_readable --input data/benchmark/v1_items.jsonl --output data/benchmark/v1_items_readable.md

10 example rewrites (patterns to imitate)

These are examples of good queries (no tool leakage, specific intent, and not templated).

• Query: I'm working with a labeled image dataset (handwritten digits) for multi-class classification. My labels are a single column of class IDs, but the model expects one-hot targets. Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_to_categorical/sklearn_to_categorical/1.0.11.0

• Query: I'm working with a labeled image dataset (handwritten digits) for multi-class classification. I want to specify the neural network architecture (layers/activations/input shape) in a config file. Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/1.0.11.0

• Query: I'm working with a labeled image dataset (handwritten digits) for multi-class classification. I already have a saved architecture/config and want to instantiate the actual model object. Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_builder/keras_model_builder/1.0.11.0

• Query: I'm working with a labeled image dataset (handwritten digits) for multi-class classification. I want to train a neural network and evaluate it (e.g., accuracy/loss on validation data). Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.11.0

• Query: I'm working with a labeled image dataset (handwritten digits) for multi-class classification. I’ve trained a model and now want predictions for a new dataset (labels or probabilities). Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/model_prediction/model_prediction/1.0.11.0

• Query: I'm working with a high-dimensional biomarker feature table (e.g., RNA-seq or DNA methylation) to predict chronological age (regression). I want to do cross-validated hyperparameter tuning (grid/random search) and pick the best settings. Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_searchcv/sklearn_searchcv/1.0.11.0

• Query: I'm working with a high-dimensional biomarker feature table (e.g., RNA-seq or DNA methylation) to predict chronological age (regression). I want to train a tree-based ensemble (random forest / boosting) and evaluate it. Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_ensemble/sklearn_ensemble/1.0.11.0

• Query: I have a chemical dataset where you want to classify samples from molecular descriptors (QSAR-style). I need to compare hyperparameter combinations with CV and select the best-performing model. Also, I care about picking a scoring metric that matches my goal. What Galaxy tool should I run for this step?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_searchcv/sklearn_searchcv/1.0.11.0

• Query: I'm working with a numeric feature matrix where you want to discover groups (unsupervised clustering). I want to cluster samples based on numeric features and get cluster assignments. Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_numeric_clustering/sklearn_numeric_clustering/1.0.11.0

• Query: I'm working with a multi-omics dataset to predict breast cancer subtypes and interpret learned features. I want to try multiple models automatically on tabular data and see which performs best. Which tool in Galaxy can do this?
• Tool: toolshed.g2.bx.psu.edu/repos/goeckslab/tabular_learner/tabular_learner/0.1.4