Token Efficiency

Token Efficiency

Token optimization for cost-effective sessions. From Delphine-L/claude_global token-efficiency (90-95% weekly saving) + Headroom integration.

When to use

• Default for all sessions — apply without being asked unless user says "verbose"
• Reading log files, large outputs, codebase navigation, debugging, system status

Workflow

Model routing (50% saving vs all-Opus):

• Opus: 10-15 min learning a new codebase (architecture, patterns)
• Sonnet: all implementation, debugging, routine work (default)
• Return to Opus only for deep architectural understanding or when Sonnet fails

Reading & execution (saves 70-80% vs full reads):

1. Prefer Grep/Glob + Bash filters over Read entire files. Example: Grep "class Foo", head -100, jq '.metadata', head -20 for CSV + wc -l.
2. For exploratory searches, dispatch a task subagent — main context gets a paragraph back, not 40K tokens.
3. Use --quiet/--silent, head/tail sampling for large output. Skills themselves are progressive disclosure: ~155 tokens per skill at start, body loads only when triggered.

Headroom (optional plugin): npm install @ngotrnghia1811/opencode-headroom and add to opencode.json plugin — 60-95% on large tool outputs with local cache (CCR).

Verification

• Default model is Sonnet; Opus only for learning/deep analysis
• Large files read via filters/subagents, not full Read
• Large command outputs sampled with head/tail/jq
• Skills body not assumed in-context — only frontmatter at start

References

• Source: Delphine-L/claude_global token-efficiency
• See also: productivity/prompt-enhancement for input optimization, productivity/context-compression for long sessions