repository-research

Trace local code, dependencies, and tests to produce reviewable research evidence before planning.

Updated Aug 5, 2026
One-click install
npx skills add https://github.com/ahsanghalib/ai-workflow --skill repository-research-ahsanghalib
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: repository-research
Source: https://github.com/ahsanghalib/ai-workflow/tree/main/skills/repository-research
Command: npx skills add https://github.com/ahsanghalib/ai-workflow --skill repository-research-ahsanghalib

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Before making a substantial code change or technical plan, teams need grounded evidence about how the existing repository actually works. This Skill traces local code, behavior, dependencies, seams, and tests, then produces a sanitized, reviewable Markdown research artifact instead of guesses. ## Core Features & Use Cases - Evidence Tracing: Follows symbols through callers, data flow, error paths, configuration, and tests, labeling each claim as fact, inference, assumption, or unknown with path and line references. - Approval-Gated Artifacts: Proposes the research document path and outline first, writes only after explicit approval, and never creates files implicitly. - Secret-Safe Output: Excludes credentials, tokens, .env contents, and sensitive logs, using typed placeholders like [REDACTED_TOKEN] only when necessary. - Use Case: Before refactoring a payment module, ask the assistant to trace its callers, dependencies, and test seams, then review the resulting research document before handing off to technical design. ## Quick Start Ask the assistant to use the repository-research skill to trace how a specific feature works and produce a reviewable research summary before any changes.

Frequently Asked Questions about repository-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I research a codebase before making a large change?▼

Use repository research to trace relevant symbols through callers, data flow, dependencies, and tests before planning. The workflow produces a reviewable Markdown artifact with facts, inferences, and unknowns, each backed by path and line references.

How to trace code dependencies and test seams in a repository?▼

Start from entry points and configuration, then follow symbols through callers, error paths, and tests. Dependencies are classified as in-process, locally substitutable, remote-but-owned, or external, and observable seams are identified for future testing.

Does repository research modify or write files automatically?▼

No. The default workflow is read-only, and it proposes the artifact path, outline, and sensitivity assessment first. A Markdown research file is written only once after explicit user approval.

Can repository research handle secrets and sensitive data safely?▼

Yes. It never inspects, quotes, or reproduces credentials, tokens, .env contents, auth data, or sensitive logs. Artifacts contain only sanitized evidence, using typed placeholders like [REDACTED_TOKEN] only when omission itself needs explanation.

When should I not use repository research?▼

Do not use it for implementation, refactoring, open-ended web research, technical proposals, or delivery planning. Those belong to companion skills such as technical-design and project-init, which run after the research is reviewed.