---
name: repository-history-analysis
description: Use when when you need to understand how a complex feature or architectural pattern was implemented in a codebase, particularly when the current README or documentation does not fully explain the control flow, decision criteria, or parameter passing between subsystems.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3096
  edam_topics:
  - http://edamontology.org/topic_0091
  tools:
  - MS2Query
  - GitHub
  - Python
derived_from:
- doi: 10.1038/s41467-023-37446-4
  title: ms2query
evidence_spans:
- MS2Query - Reliable and fast MS/MS spectral-based analogue search
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v1
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_ms2query
    doi: 10.1038/s41467-023-37446-4
    title: ms2query
  dedup_kept_from: coll_ms2query
schema_version: 0.2.0
---

# repository-history-analysis

## Summary

Analyze a software repository's commit history, pull requests, and architectural evolution to identify how design decisions were implemented, when key features were introduced, and how data flows through distinct processing branches. This skill is essential for understanding the rationale and implementation details of complex multi-branch workflows like MS2Query's split between true library matching and analogue search.

## When to use

When you need to understand how a complex feature or architectural pattern was implemented in a codebase, particularly when the current README or documentation does not fully explain the control flow, decision criteria, or parameter passing between subsystems. This skill is especially valuable when reverse-engineering the logic that routes data to different processing branches or when validating that documentation matches actual implementation.

## When NOT to use

- When the goal is only to run the software as a user—use the command-line or API documentation instead.
- When analyzing a single, monolithic function or small script without branching logic—code review or static analysis is more efficient.
- When the repository has no commit history or PR records available (e.g., no access to GitHub or project is archived without metadata).

## Inputs

- GitHub repository URL (e.g., https://github.com/iomega/ms2query)
- Pull request identifiers or commit ranges of interest
- Documentation artifacts (READMEs, workflow diagrams, architecture guides)
- Source code files (Python, configuration, test files) implementing the branching logic

## Outputs

- Documented control-flow diagram showing the decision points and branch routing paths
- Mapping of data dependencies and parameter passing between orchestrator and branch components
- List of entry and exit points for each processing branch with decision criteria
- Validation report confirming documentation matches codebase implementation

## How to apply

Clone or access the target repository (e.g., iomega/ms2query on GitHub) and review the full commit log and pull request history, paying particular attention to PRs that introduced the architectural pattern of interest (e.g., PR #72 for the two-branch workflow split). Extract control-flow logic by tracing the entry points where query spectra are routed to either the library-match branch or analogue-search branch, documenting the decision criteria (e.g., precursor m/z filtering, score thresholds, or model-based ranking). Map data dependencies and parameter passing by following how inputs flow between the orchestrator and each branch component. Finally, validate the reconstructed architecture by cross-referencing the documented control flow against the actual codebase implementation, confirming that all decision nodes, routing paths, and module interactions are accounted for and that the implementation matches the high-level workflow diagram.

## Related tools

- **GitHub** (Source repository and PR tracking for identifying architectural changes and commit history) — https://github.com/iomega/ms2query
- **Python** (Primary language for reading and tracing control-flow logic in the MS2Query codebase) — https://github.com/iomega/ms2query
- **MS2Query** (Target software system whose two-branch workflow (library match vs. analogue search) is being analyzed) — https://github.com/iomega/ms2query

## Evaluation signals

- All decision nodes in the control flow are documented with their criteria (e.g., threshold values, flag settings) and match the actual code implementation.
- Entry and exit points for each branch are identified and validated against the orchestrator code that routes spectra.
- Data dependency graph is complete: no parameters are passed between branches without being traced in the mapping.
- Workflow diagram or pseudocode can be executed or stepped through to produce the same behavior as the actual implementation.
- Cross-reference at least one PR or commit that introduced or modified the branch logic, confirming the documented changes align with the repository history.

## Limitations

- Repository history analysis depends on commit message quality and PR documentation; undocumented or poorly labeled changes may require reading raw code diffs.
- Complex refactoring or code reorganization may obscure the original design intent, requiring analysis across multiple commits or branches.
- Private or archived repositories may lack accessible history; publicly available repositories like iomega/ms2query are preferred.
- The current documented workflow may diverge from older implementations if historical branches have been deleted or squashed.

## Evidence

- [other] Review the MS2Query repository history and PR #72 to identify the architectural changes that introduced the two-branch workflow split.: "Review the MS2Query repository history and PR #72 to identify the architectural changes that introduced the two-branch workflow split."
- [other] Extract the control-flow logic that routes query spectra to either the library-match branch or the analogue-search branch, documenting decision criteria and entry/exit points for each branch.: "Extract the control-flow logic that routes query spectra to either the library-match branch or the analogue-search branch, documenting decision criteria and entry/exit points for each branch."
- [readme] MS2Query is a tool for MSMS library matching, searching both for analogues and exact matches in one run. The workflow for running MS2Query first uses MS2Deepscore to calculate spectral similarity scores between all library spectra and a query spectrum.: "MS2Query is a tool for MSMS library matching, searching both for analogues and exact matches in one run. The workflow for running MS2Query first uses MS2Deepscore to calculate spectral similarity"
- [readme] The top 2000 spectra with the highest MS2Deepscore are selected. In contrast to other analogue search methods, no preselection on precursor m/z is performed.: "The top 2000 spectra with the highest MS2Deepscore are selected. In contrast to other analogue search methods, no preselection on precursor m/z is performed."
- [readme] MS2Query optimizes re-ranking the best analogue or exact match at the top by using a random forest that combines 5 features.: "MS2Query optimizes re-ranking the best analogue or exact match at the top by using a random forest that combines 5 features."