Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/shyftlabs/continuum/token-efficiencygit clone --depth 1 https://github.com/shyftlabs/continuumWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00247 |
| Opus 5 | $0.00000 | $0.00123 |
| Sonnet 5 | $0.00000 | $0.00049 |
| Haiku 4.5 | $0.00000 | $0.00025 |
Grade A, and why
token-efficiency scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to token-efficiency — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Token Usage Optimization
Purpose
Reduce token consumption while maintaining quality through intelligent coordination.
Optimization Strategies
1. Smart Caching
- Search results cached for 5 minutes
- File content cached during session
- Pattern recognition reduces redundant searches
2. Efficient Coordination
- Agents share context automatically
- Avoid duplicate file reads
- Batch related operations
3. Measurement & Tracking
# Check token savings after session
Tool: mcp__claude-flow__token_usage
Parameters: {"operation": "session", "timeframe": "24h"}
# Result shows:
{
"metrics": {
"tokensSaved": 15420,
"operations": 45,
"efficiency": "343 tokens/operation"
}
}
Best Practices
- Use Task tool for complex searches
- Enable caching in pre-search hooks
- Batch operations when possible
- Review session summaries for insights
Token Reduction Results
- 📉 32.3% average token reduction
- 🎯 More focused operations
- 🔄 Intelligent result reuse
- 📊 Cumulative improvements
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 45 lines · 0 tokens per session scan A ecb6c03b491c
token-efficiency is a command published in the GitHub repository shyftlabs/continuum (84 stars, last pushed 12d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 247 tokens. A static security scan graded it A with 0 findings. It is 100% identical to token-efficiency, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
dashboard-flow-abort
Abort the running flow on a session. Usage /dashboard:flow-abort.
auto-status
SPEC 대시보드 — 현재 프로젝트와 서브모듈의 SPEC 상태를 표시합니다.
auto-verify
프론트엔드 UX 검증 — Playwright 기반 비주얼 검증을 실행합니다.
end-session
Wrap up the work session with read-only evidence gathering, owner-classified documentation handoffs, gated issue sync, Kano refinement, and git hygiene checks.
tldr-help
Quick reference card for tldr modes, slash commands, and triggers.
weekly-review
Works for you. Go outside and live. — AI orchestrator that auto-routes tasks to the cheapest model that solves them. 70% run free on local models. Self-auditing, self-improving, zero prompting skill needed. Built with vibe coding by a finance student. Your models, your data.