ai-explore

Name: ai-explore
Author: arcasilesgroup/ai-engineering

$npx mdskill add arcasilesgroup/ai-engineering/ai-explore

Solves codebase-only research and exploration tasks for architecture mapping, dependency tracing, pattern identification, and risk surfacing.

Identifies where specific code elements live within the project structure.
Traces import chains to understand dependencies and their flow through the codebase.
Detects patterns in the codebase that can be used for further analysis or optimization.
Surfaces potential risks associated with certain code paths based on its current state.

SKILL.md

.github/skills/ai-exploreView on GitHub ↗

---
name: ai-explore
description: Codebase-only read-only research dispatcher. Thin wrapper around the ai-explore agent for architecture mapping, dependency tracing, pattern identification, and risk surfacing. Trigger for 'explore the codebase', 'where does X live', 'map this module', 'what depends on Y', 'trace this import chain'. Not for external evidence with citations; use /ai-research instead.
effort: cheap
argument-hint: "[question]"
mode: agent
tags: [exploration, research, codebase, architecture, mapping]
model_tier: haiku
mirror_family: copilot-skills
generated_by: ai-eng sync
canonical_source: .claude/skills/ai-explore/SKILL.md
edit_policy: generated-do-not-edit
---


# Explore

## Quick start

```
/ai-explore "where does the install pipeline run hooks?"
/ai-explore "trace the import chain from cli_factory to the durable repository"
/ai-explore "what files reference the legacy ai_providers schema?"
```

## When to Use

- Architecture mapping: "How is the X module structured?"
- Dependency tracing: "What imports Y? What does Y import?"
- Pattern identification: "How do we typically handle Z?"
- Risk surfacing: "What's load-bearing in this code path?"

## When NOT to Use

- External evidence + citations needed -> use `/ai-research` instead.
- Code change needed -> use `/ai-build` or `/ai-simplify` instead.
- LLM-style code review -> use `/ai-review` instead.

## Process

Per D-133-09 this is a thin wrapper: dispatches the existing
`ai-explore` agent (`.github/agents/ai-explore.agent.md`) with the user's
question. The agent owns the heavy lifting (file-reading + grep tools
structured findings output).

1. **Capture the question.** Take the entire argument as the question.
2. **Dispatch agent.** Invoke `ai-explore` agent with the question.
3. **Report.** Return the agent's structured findings to the user.

## Output Contract

The agent emits structured Findings / Dependencies / Risks /
Recommendations sections. Wrapper passes them through unchanged.

## Common Mistakes

- Treating this skill as an external-research tool. It is codebase-only.
- Asking it to make changes. Pure read-only.

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ai-board	Operates the project board (GitHub Projects v2 or Azure DevOps): discovers configuration after install (fields, state mappings, process templates) and syncs work-item state at lifecycle transitions. Trigger for 'set up the board', 'configure our ADO board', 'discover board fields', 'move this issue to in-review', 'update the board', 'mark as in progress', 'sync the work item state'. Two subcommands: `discover` (post-install configuration write) and `sync` (lifecycle state transitions). Auto-invoked via `sync` by /ai-brainstorm, /ai-build, and /ai-pr; fail-open. Not for backlog execution; use /ai-autopilot --backlog instead.
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