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openspec/changes/add-context-tracking-hooks/proposal.md

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+# Change: Add Context Usage Tracking Documentation via Pre-Message and Post-Response Hooks
+
+## Why
+
+Users want to monitor token consumption per request and overall context usage throughout a Claude Code session. Currently, the hooks documentation shows a basic context-usage example using the Stop hook, but it doesn't demonstrate how to track **per-request** token consumption by comparing measurements at two points in time.
+
+By documenting how to use `UserPromptSubmit` as a "pre-message" hook and `Stop` as a "post-response" hook, users can calculate the delta in token usage for each request, enabling accurate per-request consumption metrics.
+
+## What Changes
+
+- **ADDED**: Documentation for using `UserPromptSubmit` and `Stop` hooks together for context tracking
+- **ADDED**: A new example demonstrating token delta calculation between pre-message and post-response
+- **MODIFIED**: Enhance the existing context usage reporting requirement with delta-based tracking approach
+- **ADDED**: Detailed explanation of token estimation methodology and its limitations
+
+## Impact
+
+- Affected specs: `hooks-documentation`
+- Affected code: `06-hooks/README.md` (documentation updates)
+- No breaking changes - purely additive documentation
+
+## Technical Analysis
+
+### Current Hook Events Mapping
+
+| Desired Hook | Claude Code Event | Trigger Point |
+|--------------|-------------------|---------------|
+| Pre-Message Hook | `UserPromptSubmit` | Before user prompt is processed by the model |
+| Post-Response Hook | `Stop` | After model completes its full response |
+
+### Token Counting Methods (Offline, No API Key)
+
+Since we need offline token counting without an API key, we offer **two local approaches**:
+
+#### Method 1: tiktoken with p50k_base (More Accurate)
+
+Use OpenAI's `tiktoken` library with the `p50k_base` encoding as an approximation for Claude's tokenizer:
+
+```python
+import tiktoken
+
+enc = tiktoken.get_encoding("p50k_base")
+tokens = enc.encode(text)
+token_count = len(tokens)
+```
+
+**Pros:**
+- More accurate than character estimation (~90-95% accuracy)
+- Works completely offline
+- No API key required
+- Fast execution
+
+**Cons:**
+- Requires `tiktoken` dependency (`pip install tiktoken`)
+- Not Claude's exact tokenizer (approximation)
+
+#### Method 2: Character-Based Estimation (Simplest)
+
+For zero-dependency estimation:
+
+```python
+estimated_tokens = len(text) // 4
+```
+
+**Pros:**
+- No dependencies at all
+- Works offline
+- Extremely fast
+
+**Cons:**
+- Less accurate (~80-90% for English text)
+- Varies more with code and non-English text
+
+### Token Delta Calculation Approach
+
+1. **Pre-Message (UserPromptSubmit)**: Read transcript, count tokens (via tiktoken or estimation)
+2. **Post-Response (Stop)**: Read transcript again, calculate new total, compute delta
+
+**Accuracy Evaluation:**
+
+| Factor | tiktoken Method | Character Estimation |
+|--------|-----------------|---------------------|
+| Token accuracy | ~90-95% | ~80-90% |
+| Dependencies | tiktoken | None |
+| Speed | Fast (<10ms) | Very fast (<1ms) |
+| Offline | Yes | Yes |
+
+### Limitations
+
+- **No official offline Claude tokenizer exists** - Anthropic hasn't released their tokenizer publicly
+- System prompts and internal Claude Code context are NOT in the transcript
+- The delta includes: user prompt + Claude's response + any tool outputs
+- Both methods are approximations; actual API token counts may differ slightly
+
+## Open Questions
+
+1. Should we persist the pre-message count to a file, or can we rely on the hook's transient state?
+   - **Recommendation**: Use a simple temp file in the session directory for reliability
+2. Should the example be a single Python script handling both hooks, or two separate scripts?
+   - **Recommendation**: Single script with mode detection based on `hook_event_name`