Token Saver Protocol (2026 Edition)
SkillDev toolsSkill to implement token saving scheme, concise, and focused on essential changes / Skill untuk menerapkan skema penghematan token, ringkas, dan fokus pada perubahan esensial tanpa basa-basi.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Token Saver Protocol (2026 Edition) skill
What this skill tells your AI
The instructions your AI receives, as published by roedyrustam/vibes-plug in skills/token-saver/SKILL.md and read by ahel’s review.
English | Bahasa Indonesia
English
Description
Implements token-efficient communication protocols for long-context AI coding sessions. Activates concise mode — minimizing response verbosity while maintaining precision and completeness of technical output.
Trigger Conditions
- User asks for token-saving mode, concise responses, or minimal output.
- Session is running long and context window is being consumed rapidly.
- User explicitly says "be concise", "save tokens", or "minimal".
Token Budget Strategies for Long Context
1. Response Compression Rules (Active When Triggered)
- No preamble: Skip "Of course! I'll help you with that...".
- No restatement: Never repeat back what the user just said.
- No trailing summaries: Don't summarize what you just did at the end.
- Code-first: Show the code change immediately, explain briefly after.
- Diff format: For small changes, show only the changed lines (not the full file).
- Bullet > prose: Use bullet lists instead of paragraphs for explanations.
2. Tool Call Efficiency
- Batch parallel reads: Read multiple files in a single turn (not sequentially).
- Targeted grep over full reads: Use
grep_searchto find specific content before reading the whole file. - Write once: Produce correct output on first try — avoid edit-then-edit-again cycles.
- Skip confirmation requests: Don't ask "Shall I proceed?" — just proceed.
3. Context Window Budget Awareness
When working on a long session:
Token budget allocation (for 200K context):
├── System prompt + skills: ~15K
├── Conversation history: ~50K (truncates older turns)
├── File contents read: ~100K (most expensive)
└── Response generation: ~35K
- Prefer
grep_searchover reading full large files. - Summarize large files mentally; only
view_filethe specific section needed. - When context is nearly full, create a checkpoint with
session-handoff-resumeskill.
4. Output Size Minimization
For file edits:
# Preferred: diff format showing only changes
- const OLD_VALUE = 'old';
+ const NEW_VALUE = 'new';
For explanations:
# Preferred: 1-sentence rationale
Changed X to Y because Z.
# Avoid: multi-paragraph explanation of an obvious change
5. Code Generation — First Draft Quality
Generate correct, production-ready code on the first attempt:
- Apply all relevant best practices from skills without being asked.
- Include error handling, types, and edge cases inline.
- Avoid TODOs, placeholder values, or "implement this later" comments.
Bahasa Indonesia
Deskripsi
Mengimplementasikan protokol komunikasi hemat token untuk sesi coding AI yang panjang. Mengaktifkan mode ringkas — meminimalkan verbositas respons sambil mempertahankan presisi dan kelengkapan output teknis.
Kondisi Pemicu
- Pengguna meminta mode hemat token, respons ringkas, atau output minimal.
- Sesi sudah panjang dan context window sedang dikonsumsi dengan cepat.
- Pengguna secara eksplisit berkata "ringkas", "hemat token", atau "minimal".
Strategi Token Budget untuk Konteks Panjang
1. Aturan Kompresi Respons (Aktif Saat Dipicu)
- Tanpa pembuka: Lewati "Tentu saja! Saya akan membantu Anda dengan...".
- Tanpa pengulangan: Jangan pernah mengulangi apa yang baru saja dikatakan pengguna.
- Tanpa ringkasan di akhir: Jangan rangkum apa yang baru saja dilakukan.
- Code-first: Tunjukkan perubahan kode segera, jelaskan singkat setelahnya.
- Format diff: Untuk perubahan kecil, tunjukkan hanya baris yang berubah.
- Poin > prosa: Gunakan daftar poin daripada paragraf.
2. Efisiensi Pemanggilan Tool
- Baca beberapa file secara paralel dalam satu giliran.
- Gunakan
grep_searchuntuk menemukan konten spesifik sebelum membaca file lengkap. - Hasilkan output yang benar pada percobaan pertama.
- Jangan tanyakan "Apakah saya harus melanjutkan?" — langsung lanjutkan.
3. Kesadaran Budget Context Window
Alokasikan token secara bijak: batasi baca file besar, gunakan grep_search daripada membaca file penuh, dan buat checkpoint dengan skill session-handoff-resume saat konteks hampir penuh.
4. Minimalisasi Ukuran Output
Gunakan format diff untuk edit file. Berikan penjelasan 1 kalimat untuk perubahan yang jelas.
5. Kualitas Draft Kode Pertama
Hasilkan kode yang benar dan siap produksi pada percobaan pertama — termasuk error handling, tipe, dan edge case secara inline. Hindari TODO, nilai placeholder, atau komentar "implementasikan ini nanti".
Orchestration & Integration
- Integrates with
zero-to-prod-orchestrator,session-handoff-resume,brainstorming, andai-cost-token-optimizer.
Integrasi Orkestrasi
- Terintegrasi dengan
zero-to-prod-orchestrator,session-handoff-resume,brainstorming, danai-cost-token-optimizer.
Signals
- GitHub stars
- 50
- Forks
- 10
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
token-saver- Source
- github.com/roedyrustam/vibes-plug