003 Name Skill 5d80956d

---
name: context-optimizer
description: "Analyzes Copilot Chat debug logs, agent definitions, skills, and instruction files to audit context window utilization. Provides log parsing, turn-cost profiling, redundancy detection, hand-off gap analysis, and optimization recommendations. Use when optimizing agent context efficiency, identifying where to add subagent hand-offs, or reducing token waste across agent systems."
compatibility: Requires Python 3.10+ for log parser script
---

# Context Window Optimization Skill

Structured methodology for auditing how GitHub Copilot agents consume their
context window. Identifies waste, recommends hand-off points, and produces
prioritized optimization reports.

---

## When to Use This Skill

- Auditing context window efficiency across a multi-agent system
- Identifying where to introduce subagent hand-offs
- Reducing redundant file reads and skill loads
- Optimizing instruction file `applyTo` glob patterns
- Profiling per-turn token cost from debug logs
- Porting agent optimizations to a new project

---

## Quick Reference

| Capability            | Description                                                  |
| --------------------- | ------------------------------------------------------------ |
| Log Parsing           | Extract structured data from Copilot Chat debug logs         |
| Turn-Cost Profiling   | Estimate token spend per turn from timing and model metadata |
| Redundancy Detection  | Find duplicate file reads, overlapping instructions          |
| Hand-Off Gap Analysis | Identify agents that should delegate to subagents            |
| Instruction Audit     | Flag overly broad globs and oversized instruction files      |
| Report Generation     | Structured markdown report with prioritized recommendations  |

---

## Prerequisites

- Python 3.10+ (for log parser script)
- Access to VS Code Copilot Chat debug logs
- Agent definitions in `.github/agents/*.agent.md` (or equivalent)

### Enabling Debug Logs

Copilot Chat writes debug logs automatically to the VS Code log directory.
To find the latest logs:

```bash
find ~/.vscode-server/data/logs/ -name "GitHub Copilot Chat.log" -newer /tmp/marker 2>/dev/null \
  | sort | tail -5
```

For richer output, set `github.copilot.advanced.debug.overrideLogLevels`
in VS Code settings to capture verbose tool-call data.

---

## Log Format Reference

### Request Completion Lines (`ccreq`)

The primary signal. Each completed LLM request produces a line like:

```text
2026-02-27 08:03:29.492 [info] ccreq:c5f11ccd.copilotmd | success | claude-opus-4.6 -> claude-opus-4-6 | 6353ms | [panel/editAgent]
```

Fields:

| Position | Field        | Example                              | Meaning                     |
| -------- | ------------ | ------------------------------------ | --------------------------- |
| 1        | Timestamp    | `2026-02-27 08:03:29.492`            | When the response completed |
| 2        | Level        | `[info]`                             | Log level                   |
| 3        | Request ID   | `ccreq:c5f11ccd.copilotmd`           | Unique request identifier   |
| 4        | Status       | `success`                            | success / error / cancelled |
| 5        | Model        | `claude-opus-4.6 -> claude-opus-4-6` | Requested -> actual model   |
| 6        | Latency      | `6353ms`                             | Total response time         |
| 7        | Request type | `[panel/editAgent]`                  | What triggered the request  |

### Request Types

| Type                            | Meaning                                                 |
| ------------------------------- | ------------------------------------------------------- |
| `[panel/editAgent]`             | Main agent turn (user prompt or tool-call continuation) |
| `[title]`                       | Auto-generated conversation title                       |
| `[progressMessages]`            | Status/progress indicator generation                    |
| `[copilotLanguageModelWrapper]` | Subagent or extension LLM call                          |

### Latency Heuristics

Latency correlates with context size (input tokens) and output length:

| Latency Band | Likely Context Size          | Signal                      |
| ------------ | ---------------------------- | --------------------------- |
| < 3s         | Small (< 10K tokens)         | Efficient turn              |
| 3-8s         | Medium (10-50K tokens)       | Normal agent turn           |
| 8-15s        | Large (50-100K tokens)       | Getting heavy               |
| 15-30s       | Very large (100-150K tokens) | Optimization candidate      |
| > 30s        | Near limit (150K+ tokens)    | Critical — likely truncated |

These are rough estimates. Actual token counts depend on model, streaming
behavior, and output generation length.

---

## Analysis Methodology

### Step 1: Parse Logs

Run the log parser to extract structured data:

```bash
python3 .github/skills/context-optimizer/scripts/parse-chat-logs.py \
  --log-dir ~/.vscode-server/data/logs/ \
  --output /tmp/context-audit.json
```

The parser produces JSON with per-session request arrays.

### Step 2: Profile Turn Costs

Group requests by session and analyze patterns:

- **Burst detection**: Rapid sequential calls (gap < 2s) suggest tool-call
  loops where context accumulates
- **Latency escalation**: If turns get progressively slower within a session,
  context is growing without hand-offs
- **Model mismatch**: Heavy turns on gpt-4o-mini suggest wrong model routing;
  fast turns on Opus suggest the task could use a lighter model

### Step 3: Audit Agent Definitions

For each `.agent.md` file, calculate context cost:

| Component              | Approximate Token Cost           |
| ---------------------- | -------------------------------- |
| System prompt overhead | ~2,000 tokens (VS Code baseline) |
| Tools (per tool)       | ~50-100 tokens each              |
| Handoff definitions    | ~30-50 tokens each               |
| Agent body text        | ~1 token per 4 characters        |
| Loaded instructions    | Varies by file size              |
| Loaded skills          | Varies by SKILL.md size          |

**Red flags**:

- Tool list > 30 items (~2,000+ tokens just for tool schemas)
- Agent body > 300 lines (~2,000+ tokens)
- Multiple `applyTo: "**"` instructions (~500+ tokens each, always loaded)

### Step 4: Map Context Growth

Trace how context accumulates through a conversation:

```text
Turn 1: System prompt + user message     → ~5K tokens
Turn 2: + assistant response + tool call → ~12K tokens
Turn 3: + tool result + response         → ~25K tokens
Turn 4: + file read (large) + response   → ~45K tokens
...
Turn N: Near model context limit         → Quality degrades
```

**Hand-off trigger points**:

- Context estimated > 60% of model limit
- Task completion boundary (e.g., planning done, execution starting)
- Tool-heavy phase transition (querying APIs → processing results)
- Domain shift (infrastructure → application → documentation)

### Step 5: Recommend Optimizations

Priority framework (effort vs impact):

| Priority | Criteria                              | Example                             |
| -------- | ------------------------------------- | ----------------------------------- |
| P0       | Prevents context overflow / data loss | Add subagent for API-heavy phase    |
| P1       | Saves > 5K tokens per session         | Narrow `applyTo` globs              |
| P2       | Saves 1-5K tokens per session         | Trim agent body length              |
| P3       | Marginal improvement                  | Reorder instructions for early exit |

---

## Common Optimization Patterns

### Pattern 1: Subagent Extraction

**Symptom**: Agent turn latency escalates past 15s after tool-heavy phase.

**Fix**: Extract the tool-heavy work into a subagent that returns a structured
result. Parent agent stays lean.

```text
Before: Agent A does planning (5K) + API calls (40K) + reporting (10K) = 55K
After:  Agent A does planning (5K) + delegates to Subagent (40K isolated) + reporting (15K) = 20K
```

### Pattern 2: Instruction Narrowing

**Symptom**: Many instruction files have `applyTo: "**"` but only matter for
specific file types.

**Fix**: Narrow the glob to `**/*.{ts,tsx}` or `**/*.bicep` etc.

### Pattern 3: Progressive Skill Loading

**Symptom**: Agent reads entire SKILL.md (400 lines) when only 1 section is needed.

**Fix**: Move detailed content to `references/` subdirectory. SKILL.md stays
< 200 lines with pointers. Copilot loads Level 3 resources only when referenced.

### Pattern 4: Prompt Deduplication

**Symptom**: Same guidance appears in agent body AND instruction file AND skill.

**Fix**: Single source of truth — put it in the most specific location.
Agent body → for workflow-specific rules. Instruction → for file-type rules.
Skill → for domain knowledge.

### Pattern 5: Context Summarization at Hand-Off

**Symptom**: Subagent receives raw conversation history instead of a structured brief.

**Fix**: Parent agent compiles a focused summary before delegating:

```text
Instead of: "Here's everything we discussed..."
Do:         "Validate these 3 Bicep files: [paths]. Check for: [specific items]."
```

---

## Report Template

See `templates/optimization-report.md` for the full output template.

---

## Portability

This skill contains **no project-specific logic**. To use in another project:

1. Copy `.github/skills/context-optimizer/` to the target repo
2. Copy `.github/agents/11-context-optimizer.agent.md`
3. Copy `.github/instructions/context-optimization.instructions.md`
4. Adjust agent numbering if needed (11 is the slot used in this repo)
5. The log parser auto-discovers VS Code log directories

---

## References

- `scripts/parse-chat-logs.py` — Log parser producing structured JSON
- `templates/optimization-report.md` — Report output template
- `references/token-estimation.md` — Detailed token cost heuristics