Feat/add string derivation detection skill#2472
Conversation
Introduce data-science/data-reduction/string-derivation skill to detect derived string columns in large datasets. The skill provides detection algorithms for identifying string columns that are computed from other columns. Also update VS Code settings.json to register additional skill locations (.agents/skills, .claude/skills, user-level directories, and Fabric extension skills).
…ve-core-all collections - Added string-derivation skill entry to data-science.collection.yml - Auto-added to hve-core-all collection via plugin generator - Regenerated affected collection .md files and plugin outputs - Updated plugin.json manifests for data-science and hve-core-all
- Added description frontmatter to string-derivation/references/algorithms.md - Applied markdown table formatting via plugin generator post-processing - All collection .md and plugin README.md files reformatted for consistency
…ation.ps1 - Removed unused ScriptDir variable - Changed packageCheck assignment to suppress output () - Renamed args variable to pythonArgs to avoid shadowing built-in
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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- Coverage 82.75% 82.74% -0.01%
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Lines 20843 20840 -3
Branches 13 13
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- Hits 17249 17245 -4
- Misses 3592 3593 +1
Partials 2 2
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Add missing copyright and SPDX-License-Identifier headers to detect-string-derivation.ps1 and detect-string-derivation.sh per repository conventions.
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Pull request overview
This pull request adds a new string-derivation data science skill (documentation + reference algorithms + wrappers) and wires it into the data-science and hve-core-all collections/plugins, plus expands VS Code skill discovery locations.
Changes:
- Added a new
string-derivationskill with documentation and reference algorithm implementations. - Registered the skill in
data-scienceandhve-core-allcollection manifests and regenerated plugin outputs. - Updated
.vscode/settings.jsonto add additional skill discovery locations.
Reviewed changes
Copilot reviewed 15 out of 15 changed files in this pull request and generated 8 comments.
Show a summary per file
| File | Description |
|---|---|
| plugins/hve-core-all/README.md | Adds string-derivation to the auto-generated skill list. |
| plugins/hve-core-all/.github/plugin/plugin.json | Includes data science data-reduction skills in the mega-collection plugin. |
| plugins/data-science/README.md | Adds string-derivation to the data-science plugin skill list. |
| plugins/data-science/.github/plugin/plugin.json | Adds the data-reduction skill path to the data-science plugin. |
| collections/hve-core-all.collection.yml | Registers the new skill in the hve-core-all collection. |
| collections/hve-core-all.collection.md | Adds string-derivation to the collection’s auto-generated docs table. |
| collections/data-science.collection.yml | Registers the new skill in the data-science collection. |
| collections/data-science.collection.md | Adds string-derivation to the collection’s auto-generated docs table. |
| .vscode/settings.json | Expands chat.agentSkillsLocations to additional local/user directories and an extension path. |
| .github/skills/data-science/data-reduction/string-derivation/SKILL.md | New skill documentation and usage examples. |
| .github/skills/data-science/data-reduction/string-derivation/references/algorithms.md | Reference implementations for derivation detection patterns and progressive sampling. |
| .github/skills/data-science/data-reduction/string-derivation/detect-string-derivation.sh | Bash wrapper intended to run the detection via Python. |
| .github/skills/data-science/data-reduction/string-derivation/detect-string-derivation.ps1 | PowerShell wrapper intended to run the detection via Python. |
| ".github/skills/security": true, | ||
| ".github/skills/shared": true | ||
| ".github/skills/shared": true, | ||
| "~/.vscode/extensions/synapsevscode.synapse-1.25.0/copilot/skills": true |
| derivations = detect_all_string_derivations_optimized(df, 'FULL_NAME', string_cols) | ||
| if derivations and derivations[0]['match_ratio'] >= 0.95: | ||
| print(f"✅ Can remove {target_col}: {derivations[0]['formula']}") |
| ## Common String Operations | ||
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| ### 1. Concatenation with Separator |
| s = series.copy() | ||
| if s.dtype == 'object': | ||
| s = s.astype(str).str.lower().strip() | ||
| return s.map(bool_map) |
| # Load the detection functions from references/algorithms.md | ||
| # NOTE: In production, these should be in a proper Python module | ||
| # For now, users must copy the functions from references/algorithms.md | ||
| try: | ||
| # Import functions - adjust path as needed | ||
| import sys | ||
| from pathlib import Path | ||
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| # Try to load from a local module if it exists | ||
| # Otherwise, provide helpful error message | ||
| print("Note: Detection functions must be copied from references/algorithms.md") | ||
| print("Creating inline implementation for demonstration...") | ||
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| # Minimal inline implementation | ||
| # (In production, source these from algorithms.md) | ||
| exec(open(Path(__file__).parent / 'references' / 'algorithms.md').read()) | ||
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| # Filter candidates once | ||
| # NOTE: This requires filter_derivation_candidates from algorithms.md | ||
| # filtered_candidates = filter_derivation_candidates( | ||
| # df, string_cols, | ||
| # max_cardinality=max_cardinality, | ||
| # max_candidates=max_candidates | ||
| # ) | ||
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| # Placeholder for demonstration | ||
| filtered_candidates = string_cols[:max_candidates] |
| #!/usr/bin/env pwsh | ||
| # Copyright (c) 2026 Microsoft Corporation. All rights reserved. | ||
| # SPDX-License-Identifier: MIT | ||
| # Detect string derivations in tabular data | ||
| # Brought to you by microsoft/hve-core | ||
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Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
| ".claude/skills": true, | ||
| "~/.agents/skills": true, | ||
| "~/.copilot/skills": true, | ||
| "~/.claude/skills": true, |
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The five new chat.agentSkillsLocations entries include user-level tilde paths (~/.agents/skills, ~/.copilot/skills, ~/.claude/skills) and a Claude-specific workspace path (.claude/skills). Tilde paths expand relative to each developer's home directory and carry no stable meaning as shared repository settings; on another machine they may silently load unrelated files. The .claude/skills and ~/.claude/skills entries also introduce an Anthropic Claude convention with no existing precedent in this repository's settings.
These five entries belong in personal user settings and should be removed from the committed file.
| - **Substring**: `EMPLOYEE_ID` contains `DEPT_CODE` | ||
| - **Character removal**: `PHONE_NUMBER` = `PHONE_DISPLAY` with formatting removed | ||
| - **Case transformation**: `email` vs `EMAIL` | ||
| - **Numeric extraction**: `ORDER_123` → `123` | ||
| - **Edit distance**: Systematic 1-2 character transformations | ||
| - **Format strings**: `{STATE}: {CITY}` patterns | ||
| - **Boolean checks**: Derived true/false flags | ||
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| ## Prerequisites | ||
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| **Python Dependencies:** |
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The "Common patterns detected" bullet list appears verbatim twice in this file. This second occurrence is a duplicate of the block already present in the Overview section and should be removed.
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The approach of building Python code inside a bash heredoc and passing it to the Python interpreter at runtime is not the established convention in this repository. Every other skill that requires Python keeps its logic in standalone .py modules under a scripts/ directory and invokes them via a managed virtual environment from the shell or PowerShell wrapper. See .github/skills/experimental/powerpoint/, .github/skills/experimental/tts-voiceover/, and .github/skills/security/vex/ for examples.
The algorithms currently embedded across the heredoc and references/algorithms.md should be extracted into proper Python module(s) under a scripts/ directory. Both wrappers should be updated to set up a venv and invoke those modules directly, following the pattern established by the skills above. A pyproject.toml declaring pandas and numpy as dependencies and a committed uv.lock are also required. Addressing this restructuring would also resolve the two existing review comments about the exec() call on the Markdown file and the non-functional PowerShell placeholder.
| --- | ||
| name: string-derivation | ||
| description: Detect derivable data columns via string operations for data reduction - Brought to you by microsoft/hve-core | ||
| user-invocable: true | ||
| --- |
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The frontmatter is missing license: MIT and the metadata block (authors, spec_version, last_updated) that all other skills in this repository include. See .github/skills/shared/pr-reference/SKILL.md, .github/skills/experimental/powerpoint/SKILL.md, or .github/skills/coding-standards/code-review/SKILL.md for examples of the expected structure.
| # Skills | ||
| - path: .github/skills/data-science/data-reduction/string-derivation |
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New skills are expected to include a maturity field. Given the open issues in both wrapper scripts, maturity: experimental is appropriate here. Both rai-planner in this same collection and python-foundational in hve-core-all provide examples of the convention.
feat(skills): add string-derivation detection skill for data science
Description
This PR introduces a new data science skill for detecting string column derivations in datasets. The skill identifies redundant string columns where one column is a deterministic transformation of another (e.g., lookup expansion, concatenation, substring extraction), enabling safe column removal for data reduction without information loss.
The skill uses progressive sampling (3→10→30 rows) to achieve 10-100x performance speedup over full-dataset testing while maintaining high confidence in pattern detection.
Key capabilities:
Additional changes:
.agents/skills,.claude/skills, user-level directories, and Fabric extension skills)data-sciencecollection manifesthve-core-allmega-collection via plugin generatorRelated Issue(s)
None
Type of Change
Select all that apply:
Code & Documentation:
Infrastructure & Configuration:
AI Artifacts:
prompt-builderagent and addressed all feedback.github/instructions/*.instructions.md).github/prompts/*.prompt.md).github/agents/*.agent.md).github/skills/*/SKILL.md).github/hooks/*/*.json)evals/)Other:
.ps1,.sh,.py)Sample Prompts (for AI Artifact Contributions)
User Request:
"Analyze my customer dataset and identify which columns are derived from others so I can reduce dimensionality without losing information."
or
"I have a dataset with 50+ string columns. Help me find redundant columns that can be safely removed."
Execution Flow:
.github/skills/data-science/data-reduction/string-derivation/SKILL.mdreferences/algorithms.mdinto their Python projectfilter_derivation_candidates()once to optimize performance by filtering high-cardinality ID columnsdetect_all_string_derivations_optimized()for each target column or in batch mode for all columnsOutput Artifacts:
Derivation findings dictionary:
{ 'type': 'lookup_expansion', 'operands': ['GENDER'], 'formula': 'GENDER_DESCRIPTION = lookup_map[GENDER]', 'match_ratio': 0.982, 'lookup_map': {'F': 'Female', 'M': 'Male', 'N': 'Non-binary'} }Or for concatenation:
{ 'type': 'concatenation', 'operands': ['FIRST_NAME', 'LAST_NAME'], 'formula': "FULL_NAME = FIRST_NAME + ' ' + LAST_NAME", 'match_ratio': 0.995, 'separator': ' ' }Success Indicators:
For detailed contribution requirements, see:
Testing
Automated validation:
Security analysis:
Diff-based assessment:
Manual testing:
Note
Manual runtime testing of the skill algorithms should be performed with sample datasets to verify pattern detection accuracy.
Checklist
Required Checks
AI Artifact Contributions
/prompt-analyzeto review contributionprompt-builderreviewRequired Automated Checks
The following validation commands must pass before merging:
npm run lint:mdnpm run spell-checknpm run lint:frontmatternpm run validate:skillsnpm run lint:md-linksnpm run lint:psnpm run eval:lint:schemanpm run plugin:generatenpm run docs:testSecurity Considerations
Additional Notes
Plugin regeneration: The
npm run plugin:generatecommand was run to update all plugin manifests and READMEs. This explains why all 13 plugin READMEs show modifications even though only the data-science and hve-core-all collections were directly affected. The generator applies markdown formatting (lint:md:fixandformat:tables) to all plugin files as post-processing.Skill architecture: The skill follows the reference implementation pattern where users copy algorithm functions from
references/algorithms.mdinto their own projects rather than importing the skill as a library. This design choice provides maximum flexibility for customization while avoiding dependency management complexity.VS Code settings rationale: The skill discovery path additions enable the skill to be loaded from multiple conventional locations including user-level directories (
~/.agents/skills,~/.copilot/skills,~/.claude/skills), alternate project locations (.agents/skills,.claude/skills), and extension-provided directories (Fabric extension). This improves developer experience by supporting different workflow preferences and tool conventions.