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.
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:
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.
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.
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.
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. |
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 onebuildPlanMilestonePrompt— dropsPROJECT.md,REQUIREMENTS.md, decisions, and supplementary templates likesecrets-manifestbuildCompleteSlicePrompt— drops requirements and UAT template inliningbuildCompleteMilestonePrompt— drops root GSD file inliningbuildReassessRoadmapPrompt— 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).
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.
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
modelsin your preferences. Without amodelssection, routing is skipped and the session's launch model is used for all phases. Token profiles setmodelsautomatically.
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.
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).
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 |
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.
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.
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.
- After each unit completes, the outcome (success/failure) is recorded against the unit type and tier used
- Outcomes are tracked per-pattern (e.g.,
execute-task,execute-task:docs) with a rolling window of the last 50 entries - If a tier's failure rate exceeds 20% for a given pattern, future classifications for that pattern are bumped up one tier
- The system also accepts tag-specific patterns (e.g.,
execute-task:testvsexecute-task:frontend) for more granular routing
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).
# 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.
---
version: 1
token_profile: budget
budget_ceiling: 25.00
models:
execution_simple: claude-haiku-4-5-20250414
------
version: 1
token_profile: balanced
models:
planning:
model: claude-opus-4-6
fallbacks:
- openrouter/z-ai/glm-5
execution: claude-sonnet-4-6
------
version: 1
token_profile: quality
models:
planning: claude-opus-4-6
execution: claude-opus-4-6
---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
---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
│ └─ capability scoring (when capability_routing: true)
│ └─ 7-dimension model profiles × task requirement vectors
└─ context_management
├─ observation masking (before_provider_request hook)
├─ tool result truncation (tool_result_max_chars)
└─ phase handoff anchors (injected into prompt builders)
The profile is resolved once and flows through the entire dispatch pipeline. Explicit preferences override profile defaults at every layer.
Introduced in v2.59.0
During auto-mode sessions, tool results accumulate in the conversation history and consume context window space. Observation masking replaces tool result content older than N user turns with a lightweight placeholder before each LLM call. This reduces token usage with zero LLM overhead — no summarization calls, no latency.
Masking is enabled by default during auto-mode. Configure via preferences:
context_management:
observation_masking: true # default: true (set false to disable)
observation_mask_turns: 8 # keep results from last 8 user turns (range: 1-50)
tool_result_max_chars: 800 # truncate individual tool results beyond this length- Before each provider request, the
before_provider_requesthook inspects the messages array - Tool results (
toolResult,bashExecution) older than the configured turn threshold are replaced with[result masked — within summarized history] - Recent tool results (within the keep window) are preserved in full
- All assistant and user messages are always preserved — only tool result content is masked
This pairs with the existing compaction system: masking reduces context pressure between compactions, and compaction handles the full context reset when the window fills.
Individual tool results that exceed tool_result_max_chars (default: 800) are truncated with a …[truncated] marker. This prevents a single large tool output from dominating the context window.
Introduced in v2.59.0
When auto-mode transitions between phases (research → planning → execution), structured JSON anchors are written to .gsd/milestones/<mid>/anchors/<phase>.json. Downstream prompt builders inject these anchors so the next phase inherits intent, decisions, blockers, and next steps without re-inferring from artifact files.
This reduces context drift — the 65% of enterprise agent failures caused by agents losing track of prior decisions across phase boundaries.
Anchors are written automatically after successful completion of research-milestone, research-slice, plan-milestone, and plan-slice units. No configuration needed.
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.
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.
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 |
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.
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.
The metrics ledger now tracks cacheHitRate per unit (percentage of input tokens served from cache) and provides aggregateCacheHitRate() for session-wide cache performance.