analyzing-options
Analyzing different approaches for a task or problem with structured comparisons, effort…
Exploring a codebase across phases: scopes the target, detects architecture, components, and layers, deep-dives each discovered perspective, then synthesizes findings into actionable guidance with file:line evidence. Use to understand how a feature or system works before
$ npx -y skills add LerianStudio/ring --skill exploring-codebases --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/exploring-codebasesContext preview
The summary Claude sees to decide when to auto-load this skill.
Exploring a codebase across phases: scopes the target, detects architecture, components, and layers, deep-dives each discovered perspective, then synthesizes findings into actionable guidance with file:line evidence. Use to understand how a feature or system works before
name: ring:exploring-codebases description: "Exploring a codebase across phases: scopes the target, detects architecture, components, and layers, deep-dives each discovered perspective, then synthesizes findings into actionable guidance with file:line evidence. Use to understand how a feature or system works before planning changes, or to orient on an unfamiliar codebase. Skip for a single signature lookup, a file-exists check, or reading an error from a known file."
**Runs before:** ring:writing-plans
**Similar:** dispatching-parallel-agents
Multi-phase approach: **Phase 0 scopes** the target, **Phase 1 discovers** the natural structure of the codebase, **Phase 2 deep-dives** into each discovered area in parallel, **Phase 3 collects** results, and **Phase 4 synthesizes** findings.
**Announce at start:** "Using ring:exploring-codebases for multi-phase autonomous exploration."
Phase 1: Discovery (3-4 parallel agents) → Architecture, Components, Layers, Organization Phase 2: Deep Dive (N adaptive agents, one per discovered perspective) → Target implementation in each area Phase 3: Synthesis → Actionable guidance with file:line evidence
Extract from user request: core subject, context/intent, depth needed. Set exploration boundaries (include/exclude directories).
Before emitting any Task call, count the discovery agents you intend to launch in this turn.
All discovery agents leave in the SAME TURN, before reading any agent output.
Forbidden sequences:
If you find yourself about to dispatch a discovery agent in a turn AFTER any agent has already returned a result → STOP. You violated parallel dispatch. Report the violation and mark the phase INCOMPLETE rather than completing the trickle.
After the dispatch turn, verify all scoped Task calls were emitted in that single turn. If fewer went out than scoped, the phase did NOT execute correctly. Mark INCOMPLETE and surface the dispatch failure — do NOT silently continue with a partial pool.
Emit all scoped Task calls (the count established in the STOP-CHECK above) in a SINGLE TURN, as one atomic batch.
**If your runtime exposes a `multi_tool_use.parallel` wrapper**, use it to dispatch the complete pool in one wrapped invocation. This is the canonical fan-out mechanism on OpenAI-style tool envelopes and on certain Anthropic SDK consumers — naming it explicitly activates parallel emission on runtimes where trickle-dispatch is the default behavior.
**If your runtime emits parallel tool_use blocks natively** (Claude Code with Claude models), `multi_tool_use.parallel` may not be needed — but naming it is harmless and serves as an enforcement anchor.
The STOP-CHECK, anti-trickle, and self-verify guards above remain binding regardless of which mechanism your runtime uses.
**Dispatch 3-4 discovery agents in a SINGLE turn (parallel):**
**Architecture Discovery:** Find pattern (Hexagonal, Layered, Microservices, Monolith, etc.). Evidence: top-level directory structure, layer separation, file paths. Output: pattern name + confidence + ASCII diagram.
**Component Discovery:** Identify all major components/modules. For each: name, location, responsibility, tech stack, size. Map dependencies between components.
**Layer Discovery:** Within each component, identify layers (HTTP/API, Business Logic, Data Access, Infrastructure). Document how layers are separated and how they communicate.
**Organization Discovery:** Find organizing principle (by layer vs by feature vs by domain). Document file naming conventions, test organization, config locations.
**After Phase 1:** Validate quality — all areas have file:line evidence, no major "unknowns" remain. Determine how many deep-dive agents to launch (one per discovered perspective).
<example> 3-component system → 3 deep-dive agents 4-layer monolith → 4 deep-dive agents (one per layer) 6-service microservices → 6 deep-dive agents </example>
Before emitting any Task call, count the deep-dive agents you intend to launch in this turn.
All deep-dive agents leave in the SAME TURN, before reading any agent output.
Forbidden sequences:
If you find yourself about to dispatch a deep-dive agent in a turn AFTER any agent has already returned a result → STOP. You violated parallel dispatch. Report
Proven engineering practices, enforced through skills. Ring is a comprehensive skills library and workflow system for AI agents that transforms how AI assistants approach software development.
Repo: LerianStudio/ring
Analyzing different approaches for a task or problem with structured comparisons, effort…
Auditing a service's production readiness against Ring engineering standards across base…
Cleaning redundant and obvious comments following clean code principles while preserving…
Commit changes with scope allowlist enforcement, atomic grouping, GPG-signed conventional…
Creating a handoff document that captures session state (completed work, decisions, open…
Creating an isolated git worktree for parallel branch work: selects the directory by priority…