# Galaxy Query Generation

> Galaxy Query Generation (Repo Copy)

- Skill: `goeckslab/galaxy-query-generation` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add goeckslab/galaxy-query-generation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/goeckslab/galaxy-query-generation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: goeckslab (https://skillmd.com/u/goeckslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/goeckslab/galaxy-query-generation

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# Galaxy Query Generation (Repo Copy)

This is a repo-local copy of the Codex skill located at:

`~/.codex/skills/galaxy-query-generation/SKILL.md`

Use it to keep the query-writing standards versioned with the project.

## Rules (must-follow)

1. **English only** in the query line.
2. The query must ask for a **tool recommendation** (e.g., “Which Galaxy tool should I use…?”), not tool-configuration help.
3. Do **not** mention **tutorial/GTN** in the query line.
4. Do **not** mention concrete **datasets / filenames / accessions** in the query line (e.g., `SRR...`, `E-MTAB-...`, `.fastq.gz`, `.bam`, URLs). Use generic descriptions (e.g., “paired-end FASTQ”, “genome assembly FASTA”, “count matrix”).
5. Do **not** mention the tool name, tool ID, or backticked function-like names in the query (no “perform `tool_x`”).
6. Queries must be **realistic** (close to how real users ask), not “benchmarky”:
   - Write from a **Galaxy user** perspective (what you have + what you want), not a tool developer/maintainer perspective.
   - Use a brief, concrete context: the data type, the goal, and the desired output.
   - Prefer natural phrasing (“I have…”, “I’m trying to…”) over rigid patterns.
   - Avoid stilted wording like “perform X”, “execute Y”, or “for this task” without details.
7. Queries must be **non-templated** and **non-repetitive**:
   - Avoid copy/paste patterns like “Which Galaxy tool would you recommend to perform …?”
   - For the **same tool**, avoid having multiple benchmark items with the **same or near-duplicate** query text.
8. Every query bullet must be followed by **at least one** `- tool: ...` line (no orphan queries).
9. `- tool:` must be a **stable identifier**:
   - Prefer Toolshed GUIDs like `toolshed.g2.bx.psu.edu/repos/<owner>/<repo>/<tool_id>/<version>`.
   - Allowed special IDs: `upload1`, `__MERGE_COLLECTION__` (and other `__...__` Galaxy internal tools), `interactive_tool_*`.
   - Avoid plain display names or workflow-step labels (e.g., `Diamond`, `PeptideShaker`, “Extract and cluster …”) when a Toolshed GUID exists.
10. The query text must be **consistent** with the chosen `- tool:` (no “Select” in the query while `- tool:` is `Grep1`, etc.).

## Generation workflow (manual, not templated)

These benchmark queries must be **handwritten**, not produced by filling a fixed template.

When doing **fresh generation** (writing queries from a tutorial step that doesn’t already have a good query), you should read the tutorial around the step so the query reflects the real *user intent*.

When doing **rewrite/cleanup** (removing tool leakage, boilerplate, or checker-triggering phrasing while keeping the same intent/tool focus), you usually do *not* need to read the full tutorial; only spot-check the relevant step if the intent is ambiguous.

When doing **batch review** (quality pass over an existing range), you must still read the batch **line-by-line**: scripts can help *find* problems, but they are not a substitute for human review.

1. Open the tutorial: `training-material/<tutorial_id>/tutorial.md`
2. Read around each tool mention to understand the *user goal*:
   - GTN tool tags like `{% tool [Name](tool_id) %}` (usually provides a tool ID)
   - Bold tool mentions like `**ToolName** {% icon tool %}` (often only a display name)
3. Infer the real task for that step (the “why”), not just the step title.
4. Write a natural English query describing the task **with enough intent detail** (inputs/outputs/goal), but **without**:
   - mentioning the tutorial/GTN
   - mentioning datasets / filenames / accessions
   - asking how to configure parameters
   - leaking the tool name / tool id
5. Attach exactly one `- tool:` line with a stable identifier.
   - Prefer resolving via the tutorial workflow `.ga` under `training-material/<tutorial_id>/workflows/`.
   - Use Toolshed API lookups only when needed.

## Where to write the results

Write final queries directly into `data/benchmark/v1_items.jsonl` (do not rely on `tmp_stats/*` as the deliverable).

## Science-first vs tool-first queries (how they differ)

Both styles are valid; the difference is *where the user starts*:

- **Science-first (principle/goal first):** starts from a scientific question (“what is present / what differs / what is it?”). The query should still mention the data type and the intended output (e.g., “cell type labels/markers”), but does *not* need to name specific methods.
- **Tool-first (operation/workflow first):** starts from a concrete processing step (“QC / trimming / mapping / quantify”). The query should still include the data type and desired report/output, but stays at the “which tool to run” level (not parameter/config help).

### Balance target (soft)

Across a batch, it’s good to have a healthy mix of **science-first** and **tool-first**, 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.

## Examples: science-first vs tool-first user queries

Both styles are valid, as long as the query still asks for a **Galaxy tool recommendation** and includes enough intent (data type + goal + expected output) without leaking tool names or dataset identifiers.

- Science-first (principle/goal driven): “I just got my single-cell RNA-seq count matrix back. How can I figure out what cell types are present?”
- Tool-first (operation/workflow driven): “Which Galaxy tool should I use to check the quality of my paired-end sequencing data stored in FASTQ files?”

### Anti-patterns (avoid)

- Generic boilerplate with no task detail (e.g., “Which Galaxy tool would you recommend for this task?” everywhere).
- Copying workflow-step labels as the query intent.
- Parameter-centric questions (“Which inputs should I select and how do I configure …”).
- Multiple benchmark items for the same tool that differ only by a swapped adjective or punctuation.

## Checker

From repo root:

`ruby -EUTF-8 skills/galaxy-query-generation/scripts/check_v1_items.rb data/benchmark/v1_items.jsonl`

### Smell scan (catch what the checker doesn’t)

The checker enforces **hard** constraints (URLs, tutorial mentions, backticks/tool leakage, file extensions, configuration-help phrasing, etc.), but it can miss “should rewrite” cases that still pass (e.g., overly generic wording, near-duplicates).

Use the non-blocking smell scan to surface candidates for manual rewrite:

`python3 skills/galaxy-query-generation/scripts/find_query_smells.py --input data/benchmark/v1_items.jsonl --start N --count 150`

### Ground-truth integrity checks (recommended during review)

- If an item has multiple `tools[]`, mark `metadata.ground_truth_alternatives=true` and add a brief `metadata.ground_truth_alternatives_note`.
- Keep `metadata.tool_focus` consistent with `tools[]` (it should be one of the listed tools and represent the main intended ground truth).
- If a tool ID is a placeholder or non-stable, consider a manual expansion to include a stable Toolshed GUID equivalent when appropriate.

This project’s final target is `data/benchmark/v1_items.jsonl` (not `tmp_stats/*`).