Inception Token Optimizer

Reduce Inception API token consumption through prompt engineering, context management, and budget enforcement.

Free-Tier Limits (Inception Labs)

Core Strategies

1. Prompt Compression

• Remove redundant instructions, filler words, and repeated context.
• Use short system prompts: "Concise answers. French." beats a 200-word persona block.
• Avoid re-sending unchanged context — only send deltas.
• Ask for short replies: "Réponds en \x3C 100 mots."

2. Context Pruning

• Before sending, estimate tokens: len(text) // 4 (rough heuristic).
• If total context > target budget, drop oldest messages and replace with a 1-2 sentence summary.
• Use references/pruning-strategies.md for detailed patterns.

3. Caching

• Identical prompts → reuse previous response. Do not re-call.
• Hash the prompt; if seen recently (within session), return cached reply.
• scripts/lru_cache.py provides a drop-in LRU cache (256 items default).

4. Model Selection

• Use cheaper/faster models for simple tasks (summarisation, classification).
• Reserve Mercury (or flagship) for complex reasoning only.
• Batch trivial queries into a single prompt instead of multiple calls.

5. Output Budgeting

• Set max_tokens explicitly — never leave it open-ended.
• Target 150-200 output tokens for conversational replies.
• Use temperature=0.7 to reduce verbose wandering.

Token Budget Guard

scripts/token_bucket.py enforces per-minute caps using a sliding window:

from scripts.token_bucket import TokenBucket

bucket = TokenBucket(req_per_min=100, in_tok_per_min=100_000, out_tok_per_min=10_000)
bucket.wait_for_slot(in_tokens=500, out_tokens=200)
# proceed with API call

Blocks until a slot is available. Use before every Inception API call.

When to Use This Skill

• Before sending a prompt to Inception → compress & prune first.
• When monitoring costs → check token estimates.
• When near free-tier limits → activate budget guard.
• When building automation → integrate caching + bucket guard.