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SKILL verified MIT Self-run

Context Engineer

skill-cacheforge-ai-cacheforge-skills-context-engineer · by cacheforge-ai

Context window optimizer — analyze, audit, and optimize your agent's context utilization. Know exactly where your tokens go before they're sent.

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Install

$ agentstack add skill-cacheforge-ai-cacheforge-skills-context-engineer

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

✓ Security review passed
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○ 7mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

When to use this skill

Use this skill when the user wants to:

  • Understand where their context window tokens are going
  • Analyze workspace files (SKILL.md, SOUL.md, MEMORY.md, etc.) for bloat
  • Audit tool definitions for redundancy and overhead
  • Get a comprehensive context efficiency report
  • Compare before/after snapshots to measure optimization progress
  • Optimize system prompts for token efficiency

Commands

# Analyze workspace context files — token counts, efficiency scores, recommendations
python3 skills/context-engineer/context.py analyze --workspace ~/.openclaw/workspace

# Analyze with a custom budget and save a snapshot for later comparison
python3 skills/context-engineer/context.py analyze --workspace ~/.openclaw/workspace --budget 128000 --snapshot before.json

# Audit tool definitions for overhead and overlap
python3 skills/context-engineer/context.py audit-tools --config ~/.openclaw/openclaw.json

# Generate a comprehensive context engineering report
python3 skills/context-engineer/context.py report --workspace ~/.openclaw/workspace --format terminal

# Compare two snapshots to see projected token savings
python3 skills/context-engineer/context.py compare --before before.json --after after.json

What It Analyzes

  • System prompt efficiency — Length, redundancy detection, compression potential
  • Tool definition overhead — Count tools, per-tool token cost, identify unused/overlapping
  • Memory file bloat — MEMORY.md size, stale entries, optimization suggestions
  • Skill overhead — Installed skills contributing to context, per-skill token cost
  • Context budget — What % of model context window is consumed by static content vs available for conversation

Options

  • --workspace PATH — Path to workspace directory (default: ~/.openclaw/workspace)
  • --config PATH — Path to OpenClaw config file (default: ~/.openclaw/openclaw.json)
  • --budget N — Context window token budget (default: 200000)
  • --snapshot FILE — Save analysis snapshot to FILE for later comparison
  • --format terminal — Output format (currently: terminal)

Notes

  • Token estimates are approximate (~4 characters per token). For precise counts, use a model-specific tokenizer.
  • No external dependencies required — runs with Python 3 stdlib only.
  • Built by Anvil AI — context engineering experts. https://labs.anvil-ai.io

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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