Token Optimization

Introduced in v2.17.0

GSD 2.17 introduces a coordinated token optimization system that can reduce token usage by 40-60% without sacrificing output quality for most workloads. The system has three pillars: token profiles, context compression, and complexity-based task routing.

Token Profiles

A token profile is a single preference that coordinates model selection, phase skipping, and context compression level. Set it in your preferences:

---
version: 1
token_profile: balanced
---

Three profiles are available:

budget — Maximum Savings (40-60% reduction)

Optimized for cost-sensitive workflows. Uses cheaper models, skips optional phases, and compresses dispatch context to the minimum needed.

Dimension	Setting
Planning model	Sonnet
Execution model	Sonnet
Simple task model	Haiku
Completion model	Haiku
Subagent model	Haiku
Milestone research	Skipped
Slice research	Skipped
Roadmap reassessment	Skipped
Context inline level	Minimal — drops decisions, requirements, extra templates

Best for: prototyping, small projects, well-understood codebases, cost-conscious iteration.

balanced — Smart Defaults (default)

The default profile. Keeps the important phases, skips the ones with diminishing returns for most projects, and uses standard context compression.

Dimension	Setting
Planning model	User’s default
Execution model	User’s default
Simple task model	User’s default
Completion model	User’s default
Subagent model	Sonnet
Milestone research	Runs
Slice research	Skipped
Roadmap reassessment	Runs
Context inline level	Standard — includes key context, drops low-signal extras

Best for: most projects, day-to-day development.

quality — Full Context (no compression)

Every phase runs. Every context artifact is inlined. No shortcuts.

Dimension	Setting
All models	User’s configured defaults
All phases	Run
Context inline level	Full — everything inlined

Best for: complex architectures, greenfield projects requiring deep research, critical production work.

Context Compression

Each token profile maps to an inline level that controls how much context is pre-loaded into dispatch prompts:

Profile	Inline Level	What’s Included
budget	minimal	Task plan, essential prior summaries (truncated). Drops decisions register, requirements, UAT template, secrets manifest.
balanced	standard	Task plan, prior summaries, slice plan, roadmap excerpt. Drops some supplementary templates.
quality	full	Everything — all plans, summaries, decisions, requirements, templates, and root files.

How Compression Works

Dispatch prompt builders accept an inlineLevel parameter. At each level, specific artifacts are gated:

Minimal level reductions:

• buildExecuteTaskPrompt — drops the decisions template, truncates prior summaries to the most recent one
• buildPlanMilestonePrompt — drops PROJECT.md, REQUIREMENTS.md, decisions, and supplementary templates like secrets-manifest
• buildCompleteSlicePrompt — drops requirements and UAT template inlining
• buildCompleteMilestonePrompt — drops root GSD file inlining
• buildReassessRoadmapPrompt — drops project, requirements, and decisions files

These are cumulative — standard drops a subset, minimal drops more. The full level preserves all context (the pre-2.17 behavior).

Overriding Inline Level

The inline level is derived from your token_profile. To control phases independently of the profile, use the phases preference:

---
version: 1
token_profile: budget
phases:
  skip_research: false    # override: run research even on budget
---

Explicit phases settings always override the profile defaults.

Complexity-Based Task Routing

GSD classifies each task by complexity and routes it to an appropriate model tier when dynamic routing is enabled. Simple documentation fixes use cheaper models while complex architectural work gets the reasoning power it needs.

Prerequisite: Dynamic routing requires explicit models in your preferences. Without a models section, routing is skipped and the session’s launch model is used for all phases. Token profiles set models automatically.

Ceiling behavior: When dynamic routing is active, the model configured for each phase acts as a ceiling, not a fixed assignment. The router may downgrade to a cheaper model for simpler tasks but never upgrades beyond the configured model.

How Classification Works

Tasks are classified by analyzing the task plan:

Signal	Simple	Standard	Complex
Step count	≤ 3	4-7	≥ 8
File count	≤ 3	4-7	≥ 8
Description length	< 500 chars	500-2000	> 2000 chars
Code blocks	—	—	≥ 5
Signal words	None	Any present	—

Signal words that prevent simple classification: research, investigate, refactor, migrate, integrate, complex, architect, redesign, security, performance, concurrent, parallel, distributed, backward compat, migration, architecture, concurrency, compatibility.

Empty or malformed plans default to standard (conservative).

Unit Type Defaults

Non-task units have built-in tier assignments:

Unit Type	Default Tier
complete-slice, run-uat	Light
research-*, plan-*, execute-task, complete-milestone	Standard
replan-slice, reassess-roadmap	Heavy
hook/*	Light

Model Routing

Each tier maps to a model configuration:

Tier	Model Phase Key	Typical Model
Light	completion	Haiku (budget) / user default
Standard	execution	Sonnet / user default
Heavy	execution	Opus / user default

Simple tasks use the execution_simple model key when configured. This is set automatically by the budget profile to Haiku.

Budget Pressure

When approaching your budget ceiling, the classifier automatically downgrades tiers:

Budget Used	Effect
< 50%	No adjustment
50-75%	Standard → Light
75-90%	Standard → Light
> 90%	Everything except Heavy → Light; Heavy → Standard

This graduated approach preserves model quality for the most complex work while progressively reducing cost as the ceiling approaches.

Adaptive Learning (Routing History)

GSD tracks the success and failure of each tier assignment over time and adjusts future classifications accordingly. This is opt-in — it happens automatically and persists in .gsd/routing-history.json.

How It Works

1. After each unit completes, the outcome (success/failure) is recorded against the unit type and tier used
2. Outcomes are tracked per-pattern (e.g., execute-task, execute-task:docs) with a rolling window of the last 50 entries
3. If a tier’s failure rate exceeds 20% for a given pattern, future classifications for that pattern are bumped up one tier
4. The system also accepts tag-specific patterns (e.g., execute-task:test vs execute-task:frontend) for more granular routing

User Feedback

Use /gsd rate to submit feedback on the last completed unit’s model tier:

/gsd rate over    # model was overpowered — encourage cheaper next time
/gsd rate ok      # model was appropriate — no adjustment
/gsd rate under   # model was too weak — encourage stronger next time

Feedback signals are weighted 2× compared to automatic outcomes. Requires dynamic routing to be active (the last unit must have tier data).

Data Management

# Routing history is stored per-project
.gsd/routing-history.json

# Clear history to reset adaptive learning
# (happens via the routing-history module API)

The feedback array is capped at 200 entries. Per-pattern outcome counts use a rolling window of 50 to prevent stale data from dominating.

Configuration Examples

Cost-Optimized Setup

---
version: 1
token_profile: budget
budget_ceiling: 25.00
models:
  execution_simple: claude-haiku-4-5-20250414
---

Balanced with Custom Models

---
version: 1
token_profile: balanced
models:
  planning:
    model: claude-opus-4-6
    fallbacks:
      - openrouter/z-ai/glm-5
  execution: claude-sonnet-4-6
---

Full Quality for Critical Work

---
version: 1
token_profile: quality
models:
  planning: claude-opus-4-6
  execution: claude-opus-4-6
---

Per-Phase Overrides

The token_profile sets defaults, but explicit preferences always win:

---
version: 1
token_profile: budget
phases:
  skip_research: false     # override: keep milestone research
models:
  planning: claude-opus-4-6  # override: use Opus for planning despite budget profile
---

How the Pieces Fit Together

PREFERENCES.md
  └─ token_profile: balanced
       ├─ resolveProfileDefaults() → model defaults + phase skip defaults
       ├─ resolveInlineLevel() → standard
       │    └─ prompt builders gate context inclusion by level
       └─ classifyUnitComplexity() → routes to execution/execution_simple model
            ├─ task plan analysis (steps, files, signals)
            ├─ unit type defaults
            ├─ budget pressure adjustment
            └─ adaptive learning from routing-history.json

The profile is resolved once and flows through the entire dispatch pipeline. Explicit preferences override profile defaults at every layer.

Prompt Compression

Introduced in v2.29.0

GSD can apply deterministic prompt compression before falling back to section-boundary truncation. This preserves more information when context exceeds the budget.

Compression Strategy

Set via preferences:

---
version: 1
compression_strategy: compress
---

Two strategies are available:

Strategy	Behavior	Default For
truncate	Drop entire sections at boundaries (pre-v2.29 behavior)	quality profile
compress	Apply heuristic text compression first, then truncate if still over budget	budget and balanced profiles

Compression removes redundant whitespace, abbreviates verbose phrases, deduplicates repeated content, and removes low-information boilerplate — all deterministically with no LLM calls.

Context Selection

Controls how files are inlined into prompts:

---
version: 1
context_selection: smart
---

Mode	Behavior	Default For
full	Inline entire files	balanced and quality profiles
smart	Use TF-IDF semantic chunking for large files (>3KB), including only relevant portions	budget profile

Structured Data Compression

At budget and balanced inline levels, decisions and requirements are formatted in a compact notation that saves 30-50% tokens compared to full markdown tables.

Summary Distillation

When a slice has 3+ dependency summaries and the total exceeds the summary budget, GSD extracts essential structured data (provides, requires, key_files, key_decisions) and drops verbose prose sections before falling back to section-boundary truncation.

Cache Hit Rate Tracking

The metrics ledger now tracks cacheHitRate per unit (percentage of input tokens served from cache) and provides aggregateCacheHitRate() for session-wide cache performance.