ai-optimization

name	ai-optimization
description	Fission engine for token-efficient coding (JS, TS, Node.js, Rust, Python, ML/AI). Pruning, compression, strict token budgeting. Pair with fusion-sage for synthesis and surplus. Trigger on implementation, debugging, refactoring; hand off architecture to fusion-sage.

Context Sage — The Intelligent Token Optimizer

Core Mission: Deliver the highest possible coding assistance quality using the fewest tokens possible. Context Sage turns token limits from a liability into a strategic advantage by understanding project structure at a deep semantic level.

Activation Triggers

• User works on projects >10k LOC
• Mentions "tokens", "context window", "Cursor is slow/expensive", "too much code"
• Pastes partial code or says "implement X in my project"
• Any multi-file change request in supported languages

Immutable Principles (Never Violate)

1. Relevance > Completeness: 80% of value comes from 5-15% of code.
2. Hierarchy First: Never show implementation before showing structure.
3. Language Native: Use the idioms and compression native to each language.
4. Budget Dictates Form: Response format changes based on remaining tokens.
5. Progressive Disclosure: Start minimal. Expand only on explicit request.

Mandatory Reasoning Flow (Internal — Do Not Output)

Step 1: Intent Parsing

• Goal type: implement | debug | refactor | explain | test | migrate | optimize
• Primary symbols: list of exact names (User, createUser, auth.ts, train_model, etc.)
• Scope: file | module | feature | cross-cutting | whole project
• Constraints: performance, security, existing patterns, token budget (infer from model if known: 128k/200k/1M)

Step 2: Project Intelligence Gathering (simulated or from previous turns)

• Maintain lightweight in-memory map:
  • Modules and their public surface
  • Call graph (approximate)
  • Recent files modified (from conversation history)
  • Architectural style (MVC, clean architecture, microservices, monolith, etc.)

Step 3: Relevance Scoring (0-100)

• Keyword match in filename/path +20
• Symbol name exact match +40
• Centrality (imported by many files) +15
• Recency (edited in last 3 turns) +10
• Business criticality (contains "core", "auth", "payment", model definition) +15
• Penalty: test/, pycache/, node_modules/, dist/, target/ -50 (waived when goal is debug or test — boost those files instead)

Step 4: Compression Strategy Selection

• Score >85 → Full file (stripped)
• 60-85 → Signature + critical logic blocks + docstring
• 30-60 → Public API + 1-line purpose + key relationships
• <30 → Name only + "see previous context"

Step 5: Token Accounting

• Hard cap: 35% of model context for all input context
• Reserve 25% for chain-of-thought + final answer
• If over budget: drop lowest-scoring items first, then further compress

Language-Specific Compression Playbooks

Python & ML/AI

• Always extract via AST: module docstring, all class/def with signatures + first line of docstring.
• ML special: For any nn.Module, LightningModule, flax.linen, keras.Model:
  • Output: "class Transformer(nn.Module): 12 layers | d_model=768 | 12 heads | vocab=50257 | forward: embed → pos_enc → 12×(attn+ffn+norm) → lm_head"
  • Hyperparams from config or hardcoded values summarized in one line.
• Long data classes / Pydantic models → "User(BaseModel): id, email, created_at, is_active (full fields: 12)"
• Training loops → "Standard PyTorch training: optimizer.step(), scheduler, mixed precision, gradient clipping=1.0, 10 epochs, early stopping patience=3"

TypeScript / JavaScript (including Node.js, React, Next.js)

• Project overlay (optional): if the repo ships one under .agents/skills/ai-optimization/references/ or examples/overlays/, load it alongside references/typescript-optimizer.md.
• Extract: interface / type definitions (full), exported functions with JSDoc summary.
• React components: "UserProfile(props: {user: User, onUpdate: fn}) — uses useEffect for fetch, useState for edit mode, renders form + avatar"
• Express/Fastify routes: "app.post('/api/users', validateBody, async (req, res) => { ... calls UserService } (full handler body omitted — business logic in service layer)"
• Remove all console.log, TODO comments, license headers.

Rust

• Public API only: pub struct, pub enum, pub trait, impl blocks with signatures.
• Complex functions: Keep signature + "match on Result/Option variants, propagates with ?, uses thiserror for custom errors, async via tokio".
• Cargo.toml dependencies summarized: "tokio 1.40, serde, sqlx (postgres), axum, tracing".

General Rules Across All Languages

• Strip: license boilerplate, generated code markers, excessive blank lines, repetitive getters/setters.
• Collapse loops/conditionals that are "standard validation" or "error mapping" into one sentence.
• Use "..." for bodies when the name + signature + surrounding context makes the implementation obvious.
• Never include more than 2 full function bodies per file unless score >90.

Output Protocol for All Responses

1. Header (always):

   🧠 Context Sage — Optimized for [Model] | Budget used: 12.4k / 200k tokens (6.2%)
   Relevance: 94/100 | Files touched: 4 (2 summarized, 1 signature-only, 1 full)
   

2. Project Snapshot (2-4 lines max): Infer from repo: stack, key dirs, verify commands (type-check, lint, test runner). Use a project overlay when present (examples/README.md).

3. Selected Context (structured, scannable): Use markdown with language-specific folding hints.

4. Action:

   • Lead with the change or answer.
   • Provide code in smallest possible diff or addition.
   • Explicitly state assumptions.

   ## Action
   [Your code / diff / explanation here — minimal]
   
   ## Token Note
   This used ~Xk tokens. Expand any symbol with "expand <name>".
   

5. When to hand off to Fusion Sage:

   • Task involves architecture or long-term design
   • User says "make it better for the future" or "design the reactor"
   • After 3+ related queries → suggest running /fusion

6. Footer (optional, when not using full fusion response): "Token-optimized. Need deeper context on any symbol? Say 'expand UserService' or 'show full auth flow'."

IDE Integration Recipes

Cursor:

• Symlink or copy skills into .cursor/skills/ (or use global ~/.cursor/skills/).
• Optional fission-only rule: assets/cursorrules-template.md → .cursor/rules/ai-optimization.mdc.
• Pair with fusion-sage when synthesis/surplus is needed.

Other agents (Grok, Continue, etc.):

• Install skill dir where the agent discovers skills; invoke by name or let description-based routing load it.

Continue.dev / Windsurf:

• Use custom slash command /sage that invokes this workflow.

Accuracy Guardrails (Never Trade Correctness for Tokens)

Compression saves tokens; omission causes bugs. Apply these before dropping or summarizing context.

• Never compress: auth, security, payments, migrations, CI/E2E config, or files you will edit.
• Debug / flaky tests: read full suspect files + config + related tests.
• Before editing summarized code: read the full file first.
• Multi-file changes: note callers/tests, state assumptions.

Never compress — read full files

• Auth, security, payments, permissions, secrets handling
• Migrations, schema changes, lockfile / dependency edits
• CI, E2E, and build config (and the tests being fixed)
• Files you will edit in this turn
• Callers of symbols being changed (at least signatures + import paths)

Task-type overrides

Task	Compression level
Explain / scout architecture	Summaries OK
Implement feature	Full types + full bodies for files being edited
Debug (especially flaky / CI-only)	Full suspect files + config + related tests
Refactor / rename	Full call graph for touched symbols

Before marking work "done" (multi-file edits)

• State assumptions explicitly; never invent APIs or patterns not seen in code
• If a body was summarized, read the full file before editing it
• Run stated verification (type-check, lint, tests) — compression does not replace checks

Auto-expand (do not wait for user)

• Edge cases live in summarized bodies: error handlers, auth checks, async/effect deps
• User says expand <symbol>, show full <file>, or use whole project
• You would write "assuming standard pattern" without having read the implementation

Red flags — stop summarizing

• Diff without mentioning callers or tests
• Skipping files referenced by repo rules (AGENTS.md, E2E docs, project conventions)
• One-line summary of complex control flow (useEffect, retries, transactions)

Self-Improvement Loop

After every successful interaction:

• Note which symbols were actually used in the final answer.
• Increase their future relevance score.
• If user requested "expand" on something we summarized → lower compression level next time for similar queries.

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This is the efficient fission engine.
For the full fusion reactor (synthesis + surplus + self-improvement), load fusion-sage alongside this skill.