ClawdContext VS Code Extension Tutorial: Stop Prompting, Start Orchestrating

Your AI Agent Has a Config Problem (And You Don't Know It)

You just added 47 rules to CLAUDE.md. Your agent is slower now. Not faster. Slower.

This isn't a paradox — it's physics. Research from ETH Zurich shows that context files reduce success rates and increase inference costs by 20%. Stanford's "Lost in the Middle" benchmark proves that instructions at position 10 in a config file have 27% less attention than instructions at position 1.

Your agent has 200K tokens of RAM. Your config eats 30K at boot. The remaining 170K are competing with noise.

You wouldn't ship a program without a compiler. Why are you shipping agent configs without a linter?

This tutorial shows you how to install and use ClawdContext — a free VS Code extension that treats your agent markdown files as a system, not a pile of prompts.

Time to complete: 5 minutes.
What you'll get: A CER score, security scan, and actionable diagnostics for your agent config — before you ship.

Step 1: Install ClawdContext from the Marketplace

The fastest way: open the ClawdContext page on the VS Code Marketplace and click Install. It launches VS Code and installs in one click.

The ClawdContext Marketplace listing — click Install to add it directly to VS Code.

Alternatively, open VS Code and press Ctrl+Shift+X (or Cmd+Shift+X on macOS). Search for "ClawdContext" and click Install.

Search "ClawdContext" in the Extensions sidebar to find and install it.

Or run this from your terminal:

code --install-extension clawdcontext.clawdcontext

That's it. No configuration required. ClawdContext auto-detects your agent files on activation.

Step 2: Scaffold Your Markdown OS (If Starting Fresh)

If you already have CLAUDE.md, AGENTS.md, or lessons.md files, skip to Step 3.

Otherwise, open the Command Palette (Ctrl+Shift+P) and run:

ClawdContext: Scaffold Markdown OS Templates

This creates the full Markdown OS structure:

File	Role	OS Equivalent
CLAUDE.md	Global invariants and memory	Kernel config (/etc)
SKILL.md	Reusable procedures	System calls (libc)
todo.md	Current task state	Process control block
lessons.md	Governed learning cache	Adaptive cache (L2/L3)

The key insight: each file type has a distinct purpose. Procedures in the kernel waste tokens. Heuristics in skills waste attention. ClawdContext enforces these boundaries.

Step 3: Analyze Your Workspace

Run the analysis command:

ClawdContext: Analyze Workspace

ClawdContext scans every .md agent file and computes:

• Token counts per file and per layer (kernel, skills, learning, tasks)
• CER (Context Efficiency Ratio) — how much of the context window is left for actual reasoning
• Diagnostics — linter warnings for misplaced instructions, contradictions, bloat
• Security scan — suspicious patterns in SKILL.md files (exfiltration, prompt override, credential access)

The CER Dashboard shows your context budget at a glance. Green = healthy (CER ≥ 0.4). Red = critical (CER < 0.2).

Step 4: Read the Diagnostics

After analysis, check the Problems panel (Ctrl+Shift+M). ClawdContext reports diagnostics with codes you can act on:

Code	Meaning	Action
CER_CRITICAL	Context efficiency below 0.2	Reduce always-loaded config size
KERNEL_BLOAT	CLAUDE.md exceeds token threshold	Extract procedures to SKILL.md
PROCEDURE_IN_KERNEL	Step-by-step procedures in CLAUDE.md	Use quick fix to extract
STALE_LESSON	Lesson older than TTL (60 days)	Review, archive, or refresh
CONTRADICTION	Conflicting rules across files	Resolve the conflict manually
LOST_IN_MIDDLE	Critical instructions buried at position 10+	Move to top or bottom of file
KESSLER_RISK	lessons.md has too many entries	Prune stale lessons

Every diagnostic comes with a quick-fix code action. Click the lightbulb to extract, move, or archive.

Each diagnostic links to a quick-fix code action. Hover over the warning, click the lightbulb icon, and ClawdContext will refactor for you:

• Extract Procedure to SKILL.md — moves step-by-step workflows out of the kernel
• Move Heuristic to lessons.md — relocates temporal patterns where they belong
• Add Missing Metadata — enforces governance fields on lessons
• Archive Deprecated Entries — cleans dead rules from lessons.md

Step 5: Check the Security Scanner

If you use community skills or downloaded SKILL.md files, the security scanner is critical.

Research from "Agent Skills in the Wild" found that 26% of 31,132 publicly available agent skills had vulnerabilities — from exfiltration beacons to credential harvesting.

ClawdContext scans for 6 threat categories:

1. Exfiltration / network beacons — instructions to send data to external URLs
2. Credential access — patterns targeting API keys, tokens, passwords
3. Code execution / shell abuse — dangerous command patterns
4. Obfuscation — base64 encoding, hex-encoded payloads
5. Prompt override / injection — attempts to redefine agent behavior
6. Persistence / reconnaissance — patterns for maintaining unauthorized access

Each skill gets a verdict: clean, suspicious, or dangerous. Findings include the exact pattern and line number.

Open the Dashboard (ClawdContext: Open Dashboard) and scroll to the Security section to see per-skill verdicts.

Understanding CER: The One Number That Matters

CER (Context Efficiency Ratio) is the single most important metric for agent config health:

CER = (Context Limit − Always-Loaded Tokens) / Context Limit

Example: Context limit = 200,000 tokens. CLAUDE.md = 12,000 tokens. AGENTS.md = 5,000 tokens. Always-loaded total = 17,000 tokens. CER = (200,000 − 17,000) / 200,000 = 0.915 ✅

The thresholds:

• CER ≥ 0.4 — Healthy. Research target is > 0.6.
• CER 0.2–0.4 — Warning. Your agent is leaving performance on the table.
• CER < 0.2 — Critical. 80%+ of the context window is consumed before the agent starts reasoning.

Most teams we've analyzed run at CER 0.1–0.3. That means their agent is reasoning in a cramped 20–30% of its available context while 70–80% is wasted on boot-time configuration that the model barely attends to.

The fix is architectural: move procedures to SKILL.md (loaded on demand), move heuristics to lessons.md (governed), and keep CLAUDE.md lean.

Step 6: Your Daily Workflow

After the initial setup, ClawdContext runs in the background. Here's the workflow:

Morning (or after git pull)

1. Check the CER status bar — is it still green?
2. Open the Lessons panel — any stale lessons?
3. Review CodeLens badges on lessons.md — age and confidence at a glance

When adding new rules

1. Add the rule to the correct file (kernel vs. skill vs. lesson)
2. Run ClawdContext: Lint .md Agent Files
3. Check CER impact — did it drop?
4. Use the What-If Simulator to predict CER before committing

Weekly review

1. Run ClawdContext: Prune Stale Lessons
2. Run ClawdContext: Review Promotion Candidates
3. Generate a ClawdContext: Context Health Report for your team

Command Reference

Core Commands (14)

Command	What It Does
Analyze Workspace	Full scan — tokens, CER, diagnostics, security
Open Dashboard	Interactive CER dashboard with layer breakdown
Lint .md Agent Files	Run all diagnostic rules on agent markdown
Generate Context Health Report	Exportable markdown report
Prune Stale Lessons	Archive lessons past TTL
Review Promotion Candidates	Find lessons ready to become kernel rules
Scaffold Markdown OS Templates	Generate starter files
Extract Procedure to SKILL.md	Refactor kernel → skill
Move Heuristic to lessons.md	Refactor kernel → learning
Archive Deprecated Entries	Clean dead rules
Analyze Kernel Bloat	Deep token analysis of CLAUDE.md
Apply Config Preset	Apply predefined configuration (Minimal, Standard, Enterprise)
Export Dashboard	Export dashboard data as JSON or Markdown
CER Diff: Show Changes	Track CER changes between Git commits

AI Commands (11)

Command	What It Does
AI: Test Connection	Verify your AI provider configuration
AI: Analyze Context	AI-powered analysis of context health
AI: Generate Improvement Suggestions	Get actionable optimization suggestions
AI: Review Security	Security audit of agent configuration
AI: Check Tool Safety	Scan MCP tools for safety concerns
AI: Estimate CER Impact	Predict CER after proposed changes
AI: Suggest Refactoring	AI-driven decomposition recommendations
AI: Generate Context Report	Comprehensive AI-generated analysis
AI: Interactive Chat	Chat interface for context optimization
AI: Configure Provider	Set up your preferred AI provider
AI: Multi-Provider Compare	Compare results across providers

Configuration

All settings are in VS Code's Settings → Extensions → ClawdContext. v0.4.0 organizes settings into four categories:

Core Settings

Setting	Default	Description
tokenBudget	200,000	Your model's context window size
cerWarningThreshold	0.4	CER below this triggers a warning
cerCriticalThreshold	0.2	CER below this triggers critical
lessonsTtlDays	60	Days before a lesson is considered stale
lessonsMaxEntries	50	Maximum lessons before Kessler risk
configPreset	standard	Apply a preset: minimal, standard, or enterprise
dashboard.exportFormat	json	Default export format (json or markdown)

AI Provider Settings

Setting	Default	Description
ai.provider	anthropic	AI provider: anthropic, openai, azure, google, ollama
ai.model	(per provider)	Model identifier for the selected provider
ai.temperature	0.3	Response temperature (0–1)
ai.maxTokens	4096	Max tokens for AI responses
ai.requestTimeout	30000	Timeout in ms for AI requests

mTLS / Enterprise Settings

Setting	Default	Description
ai.mtls.enabled	false	Enable mutual TLS for AI provider connections
ai.mtls.certPath	—	Path to client certificate (PEM)
ai.mtls.keyPath	—	Path to client private key (PEM)
ai.mtls.caPath	—	Path to CA bundle (PEM)

Security Settings

Setting	Default	Description
security.scanOnSave	true	Auto-scan agent files on save for secrets
security.blockedPatterns	[…]	Regex patterns to detect leaked secrets

For Claude (200K context), keep the defaults. For GPT-4 (128K), set tokenBudget to 128000. Most AI providers require an API key stored in VS Code's secret storage.

What's New in v0.4.0

Version 0.4.0 is the biggest update yet, adding AI-powered analysis, enterprise security, and advanced tracking:

• 5 AI Providers — Anthropic, OpenAI, Azure OpenAI, Google Gemini, and Ollama (local). Full mTLS support for enterprise deployments.
• 11 AI Commands — From context analysis and security review to interactive chat and multi-provider comparison.
• CER Diff Tracking — Track how your Context Efficiency Ratio changes between Git commits. Catch context bloat before it ships.
• Config Presets — One-click configuration: Minimal (solo dev), Standard (team), Enterprise (production + mTLS).
• Dashboard Export — Export your CER dashboard as JSON or Markdown for team reviews and compliance.
• Expanded Security Patterns — 23 patterns detect leaked API keys, tokens, and secrets in agent files.

Before vs. After: A Real-World Example

Metric	Before	After ClawdContext
CLAUDE.md size	8,200 tokens	2,100 tokens
Total always-loaded	14,500 tokens	3,800 tokens
CER	0.28 ⚠️	0.81 ✅
Diagnostic warnings	—	0 (all resolved)
Contradictions found	3 (hidden)	0 (resolved)
Skills extracted	0	4 procedures
Stale lessons pruned	0	11 archived

Result: 74% less boot-time context consumption. The agent now has 3.8x more reasoning headroom.

What's Next

ClawdContext is free and open source. Here's how to go deeper:

• Install from Marketplace: VS Code Marketplace
• Star the repo: github.com/yaamwebsolutions/clawdcontext4vscode
• Read the research: Your AI Agent Has 200K Tokens of RAM — And You're Wasting 80% of It
• Join the discussion: GitHub Discussions
• Report issues: GitHub Issues

Stop prompting. Start orchestrating.