Token Saver Protocol (2026 Edition)

SkillDev tools

Skill 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.

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_search to 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_search over reading full large files.
  • Summarize large files mentally; only view_file the specific section needed.
  • When context is nearly full, create a checkpoint with session-handoff-resume skill.
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_search untuk 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, and ai-cost-token-optimizer.

Integrasi Orkestrasi

  • Terintegrasi dengan zero-to-prod-orchestrator, session-handoff-resume, brainstorming, dan ai-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