oimiragieo/.claude/skills/codebase-exploration

Codebase Exploration

Overview

7-phase progressive exploration protocol for analyzing unfamiliar codebases while keeping token usage under 34K. Based on research into SWE-bench top performers, LocAgent, and OpenHands — the key insight is search-first, read-selectively, write-findings-immediately.

Core principle: Never read a file to discover what's in it. Use search to locate, then read to confirm.

When to Invoke

Skill({ skill: 'codebase-exploration' });

Invoke when:

• Analyzing an external or unfamiliar codebase
• Onboarding a GitHub repository into the ecosystem
• Investigating a codebase to answer a specific question
• Performing due diligence on third-party code

Token budget: 34K total. Hard stop at 60K — invoke context-compressor immediately.

Enforcement Hooks

Input validated against schemas/input.schema.json before execution. Output contract defined in schemas/output.schema.json. Pre-execution hook: hooks/pre-execute.cjs Post-execution hook: hooks/post-execute.cjs

---

Phase 0: Scope Gate (~500 tokens)

Estimate token budget BEFORE diving in. This prevents context overflow.

Actions:

# Count files, excluding noise directories
find . -type f | grep -v node_modules | grep -v .git | grep -v __pycache__ | grep -v dist | grep -v build | grep -v .venv | wc -l

# Estimate token budget of relevant subtree
# ~4 chars per token, so: file_count * avg_file_size_bytes / 4 = token_estimate
# Or use: pnpm search:tokens . (if analyzing our own repo)

Decision gate:

• < 100 files AND < 30K estimated tokens: Proceed as single agent
• 100-500 files OR 30K-100K tokens: Single agent with Phase 6 compression checkpoint

• 500 files OR > 100K tokens: Recommend multi-agent decomposition via planner

Multi-agent decomposition protocol (when >100K tokens):

1. Spawn planner to decompose by concern/directory into 2-4 chunks
2. Each researcher agent gets one chunk + the repo map from Phase 2
3. Each writes to a separate report file in .claude/context/tmp/exploration-<timestamp>/
4. A synthesizer agent reads all reports and produces unified analysis
5. Report paths: chunk-1-findings.md, chunk-2-findings.md, etc.

Write scope assessment to report file immediately. Do not hold in context.

---

Phase 1: Structure Scan (~2K tokens)

Goal: Build a mental map without reading any file content.

# Directory tree, depth 3, exclude noise
find . -maxdepth 3 -type d | grep -v node_modules | grep -v .git | grep -v __pycache__ | grep -v dist | grep -v build | grep -v .venv | sort

# File count per top-level directory
for d in */; do echo "$d: $(find $d -type f 2>/dev/null | wc -l)"; done

# Identify language stack from file extensions
find . -type f | grep -v node_modules | grep -v .git | sed 's/.*\.//' | sort | uniq -c | sort -rn | head -15

Output: Write directory tree + language stack to report file. Note top-level directories and likely purpose.

---

Phase 2: Repo Map Generation (~5K tokens) — MANDATORY

Goal: Extract function/class signatures without reading bodies.

# Read README (first 100 lines only — use offset/limit)
# Read({ file_path: "README.md", limit: 100 })

# Read package manifest
# Read({ file_path: "package.json" }) or pyproject.toml, Cargo.toml, go.mod

# Extract function/class signatures via ripgrep
rg "^export |^class |^function |^def |^func |^type |^interface |^const " \
  --type-add 'src:*.{ts,js,py,go,rs,java}' -t src \
  --no-heading -l | head -30

# Map import/dependency graph (find most-imported modules)
rg "^import |^from |require\(" -l | head -30

Write repo map to report file immediately — do not hold raw output in context.

Include in report:

• Project type (library, API, CLI, SaaS, etc.)
• Language/framework stack
• Key entry points
• Top-level exported symbols

---

Phase 3: Targeted Search (~5K tokens)

Goal: Use search tools to find specific patterns WITHOUT reading full files.

# Find entry points from manifest
# (read manifest → extract main/bin/scripts → note filenames)

# Find API routes / endpoints
rg "app\.(get|post|put|delete|use)\(" -l    # Express
rg "@app\.(route|get|post)" -l              # Flask/FastAPI
rg "router\.(get|post|put)" -l             # Other routers

# Find test patterns
rg "describe\(|it\(|test\(|def test_" -l | head -20

# Find configuration patterns
rg "process\.env\.|os\.environ\." -l | head -20

# Find database/storage patterns
rg "db\.(query|find|select)|mongoose\.|prisma\." -l | head -20

Write search findings to report. Move to Phase 4 only for files identified by search.

---

Phase 4: Selective Deep Reads (~15K tokens, MAX 10 files)

Rules:

1. NEVER read a file without a specific question to answer
2. ALWAYS use Read with offset/limit — never read entire large files
3. Maximum 200 lines per read operation
4. Maximum 10 files total in this phase
5. After reading each file, write a 2-3 sentence summary to report IMMEDIATELY

Pattern:

# Step 1: Find the exact line number of what you need
rg -n "function processOrder" src/core/engine.ts

# Step 2: Read only that section
# Read({ file_path: "src/core/engine.ts", offset: 142, limit: 50 })

# Step 3: Write finding to report file immediately
# Write 2-3 sentence summary before reading next file

If context exceeds 60K tokens after this phase:

Skill({ skill: 'context-compressor' });

---

Phase 5: Cross-Reference (~5K tokens)

Goal: Understand component connections without reading more files.

# Find all callers of a key function
rg "processOrder\(" -l

# Find all implementations of a pattern
rg "implements OrderProcessor" -l

# Trace data flow
rg "db\.(query|find|select|get)" -l

# Find circular dependencies (who imports whom)
rg "require\('./auth'\|from './auth'" -l

If LSP tools are available (TypeScript/JavaScript projects):

// lsp_goToDefinition — find where a symbol is defined
// lsp_findReferences — find all usages
// lsp_incomingCalls — who calls this function?
// lsp_outgoingCalls — what does this function call?
// Note: LSP requires a running language server; fall back to ripgrep if empty results

Write dependency/call flow diagram (text-based) to report.

---

Phase 6: Synthesis Checkpoint (~2K tokens)

Goal: Free context by writing all findings to report file.

# Write comprehensive report to .claude/context/tmp/ or .claude/context/reports/
# Include: project type, architecture, key features (file:line refs), dependencies, test coverage

Report structure:

# Codebase Analysis: [Project Name]

## Project Profile

- Type: [library/API/CLI/SaaS/etc]
- Language: [primary language + version]
- Framework: [frameworks detected]
- Entry points: [file:line refs]

## Architecture

[Text diagram of key components and connections]

## Key Findings

1. [Finding with file:line reference]
2. [Finding with file:line reference]

## Dependencies

- Production: [key deps]
- Dev: [key deps]

## Test Coverage Assessment

- Test files: [count, location]
- Framework: [jest/pytest/etc]

## Unknowns / Follow-up Questions

- [What couldn't be determined without deeper analysis]

Return to caller: file path + 5-bullet summary (max 500 chars). Do NOT inline the full report.

---

Tool Selection Matrix (External Repos)

| Need | Tool | Why | | ------------------ | -------------------------- | ------------------------------------- | | File discovery | Glob | Fast pattern matching | | Content search | Grep (ripgrep) | Regex across whole repo | | Targeted read | Read with offset/limit | Windowed, token-efficient | | Structure patterns | ast-grep (if installed) | Language-aware | | Definitions | LSP (if server available) | Compiler-level accuracy | | External context | WebFetch + docs URL | Find docs, articles about the project | | Compression | context-compressor | When >60K tokens |

Tools NOT available for external repos:

• pnpm search:code — requires index build (internal repo only)
• pnpm search:structure — internal repo only
• Semantic search — requires vector embeddings

---

Anti-Patterns

• NEVER read files breadth-first — always search-first, read-selectively
• NEVER accumulate raw tool output in context — write findings to files immediately
• NEVER read entire large files — use offset/limit for all reads (max 200 lines)
• NEVER guess at file contents — use rg -n to find exact line numbers first
• NEVER proceed past 60K tokens without invoking context-compressor
• NEVER read more than 10 files in Phase 4

Iron Laws

1. ALWAYS write findings to a report file after each phase — not at the end; raw tool output accumulated in context triggers "lost in the middle" degradation.
2. ALWAYS use offset/limit on Read for any file over 200 lines — full-file reads of large files consume 5-15K tokens in a single operation.
3. NEVER start Phase 4 without completing Phase 3 search — reading files without search context is breadth-first anti-pattern.
4. ALWAYS invoke context-compressor when context exceeds 60K tokens — not after; at the boundary.
5. ALWAYS return only a file path + 5-bullet summary — the report file is the artifact, not the inline response.

Memory Protocol (MANDATORY)

Before starting:

node .claude/lib/memory/memory-search.cjs "codebase exploration external repo analysis"

Read .claude/context/memory/learnings.md Read .claude/context/memory/decisions.md

After completing:

• New exploration pattern → .claude/context/memory/learnings.md
• Tool limitation discovered → .claude/context/memory/issues.md
• Decomposition decision → .claude/context/memory/decisions.md

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.