context-attribution

name	context-attribution
description	Estimate per-turn token attribution across 6 categories in Claude Code sessions to show where context budget is spent
version	1.1.0
model	sonnet
invoked_by	both
user_invocable	true
tools	["Read","Bash","Grep","TaskUpdate"]
agents	["developer","architect"]
category	Memory & Context
tags	["context-analysis","token-attribution","observability","cost-optimization"]
best_practices	["Classify every message into exactly one of the 6 categories","Use chars/4 for token estimation when usage fields are missing","Present results as a table with cumulative totals"]
error_handling	graceful
streaming	supported
source	builtin
trust_score	100
provenance_sha	31dc087efe2d9636

Context Attribution

Estimate per-turn token attribution across 6 categories in Claude Code sessions. Based on claude-devtools visible context tracker.

When to Invoke

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

Use when: context pressure is high, optimizing CLAUDE.md sizes, understanding which tool calls consume the most tokens, debugging context overflow.

The 6 Categories

Category	Detection Pattern	Typical %
CLAUDE.md files	System messages with claudeMd or CLAUDE.md content	20-40%
@-mentioned files	Read tool results triggered by user file references	10-20%
Tool outputs	All tool_result content blocks	15-30%
AI thinking/text	Assistant message content (text + thinking blocks)	10-25%
Team coordination	Messages containing <teammate-message> XML	0-15%
User messages	User role messages (prompts, follow-ups)	5-15%

Workflow

Step 1: Load Session JSONL

# Find most recent session
SESSION=$(ls -t ~/.claude/projects/$(pwd | sed 's|/|-|g; s|^-||')/*.jsonl | head -1)

Step 2: Extract Per-Turn Token Data

For each message, classify into one of the 6 categories and estimate tokens:

# Count user messages (Category 6)
grep '"role":"user"' "$SESSION" | grep -v '"tool_result"' | wc -l

# Count tool results (Categories 2-3)
grep '"type":"tool_result"' "$SESSION" |  -l


grep   |  -l


grep   |  -l