This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.
npxskills add guanyang/antigravity-skills--skill context-optimizationLoading…
Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. Effective optimization increases useful capacity without requiring larger models or longer windows — but only when applied with measurement discipline. The techniques below are ordered by impact and risk.
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context-fundamentals.context-degradation.context-compression.filesystem-context.Apply four primary strategies in this priority order:
KV-cache optimization — Reorder and stabilize prompt structure so the inference engine reuses cached Key/Value tensors. This is the cheapest optimization when the runtime supports prefix caching: low quality risk, immediate cost and latency savings. Apply it first when stable prefixes exist.
Observation masking — Replace verbose tool outputs with compact references once their purpose has been served. Tool outputs can dominate agent trajectories (claim-context-optimization-tool-output-dominance), so masking often yields the largest capacity gains. The original content remains retrievable if needed downstream.
Compaction — Summarize accumulated context when utilization exceeds 70%, then reinitialize with the summary. This distills the window's contents while preserving task-critical state. Compaction is lossy — apply it after masking has already removed the low-value bulk.
Context partitioning — Split work across sub-agents with isolated contexts when a single window cannot hold the full problem. Each sub-agent operates in a clean context focused on its subtask. Reserve this for tasks where estimated context exceeds 60% of the window limit, because coordination overhead is real.
The governing principle: context quality matters more than quantity. Every optimization preserves signal while reducing noise. Measure before optimizing, then measure the optimization's effect.
Trigger compaction when context utilization exceeds 70%: summarize the current context, then reinitialize with the summary. This distills the window's contents in a high-fidelity manner, enabling continuation with minimal performance degradation. Prioritize compressing tool outputs first (they consume 80%+ of tokens), then old conversation turns, then retrieved documents. Never compress the system prompt — it anchors model behavior and its removal causes unpredictable degradation.
Preserve different elements by message type:
Target 50-70% token reduction with less than 5% quality degradation. If compaction exceeds 70% reduction, audit the summary for critical information loss — over-aggressive compaction is the most common failure mode.
Mask observations selectively based on recency and ongoing relevance — not uniformly. Apply these rules:
[Obs:{ref_id} elided. Key: {summary}. Full content retrievable.]Masking should achieve 60-80% reduction in masked observations with less than 2% quality impact. The key is maintaining retrievability — store the full content externally and keep the reference ID in context so the agent can request the original if needed.
Maximize prefix cache hits by structuring prompts so that stable content occupies the prefix and dynamic content appears at the end. KV-cache stores Key and Value tensors computed during inference; when consecutive requests share an identical prefix, the cached tensors are reused, saving both cost and latency.
Apply this ordering in every prompt:
Design prompts for cache stability: remove timestamps, session counters, and request IDs from the system prompt. Move dynamic metadata into a separate user message or tool result where it does not break the prefix. Even a single whitespace change in the prefix invalidates the entire cached block downstream of that change.
Target 70%+ cache hit rate for stable workloads. At scale, this translates to 50%+ cost reduction and 40%+ latency reduction on cached tokens.
Partition work across sub-agents when a single context cannot hold the full problem without triggering aggressive compaction. Each sub-agent operates in a clean, focused context for its subtask, then returns a structured result to a coordinator agent.
Plan partitioning when estimated task context exceeds 60% of the window limit. Decompose the task into independent subtasks, assign each to a sub-agent, and aggregate results. Validate that all partitions completed before merging, merge compatible results, and apply summarization if the aggregated output still exceeds budget.
This approach achieves separation of concerns — detailed search context stays isolated within sub-agents while the coordinator focuses on synthesis. However, coordination has real token cost: the coordinator prompt, result aggregation, and error handling all consume tokens. Only partition when the savings exceed this overhead.
Allocate explicit token budgets across context categories before the session begins: system prompt, tool definitions, retrieved documents, message history, tool outputs, and a reserved buffer (5-10% of total). Monitor usage against budget continuously and trigger optimization when any category exceeds its allocation or total utilization crosses 70%.
Use trigger-based optimization rather than periodic optimization. Monitor these signals:
Select the optimization technique based on what dominates the context:
| Context Composition | First Action | Second Action |
|---|---|---|
| Tool outputs dominate (>50%) | Observation masking | Compaction of remaining turns |
| Retrieved documents dominate | Summarization | Partitioning if docs are independent |
| Message history dominates | Compaction with selective preservation | Partitioning for new subtasks |
| Multiple components contribute | KV-cache optimization first, then layer masking + compaction | |
| Near-limit with active debugging | Mask resolved tool outputs only — preserve error details |
Track these metrics to validate optimization effectiveness:
Iterate on strategies based on measured results. If an optimization technique does not measurably improve the target metric, remove it — optimization machinery itself consumes tokens and adds latency.
Example 1: Compaction Trigger
if context_tokens / context_limit > 0.8:
context = compact_context(context)
Example 2: Observation Masking
if len(observation) > max_length:
ref_id = store_observation(observation)
return f"[Obs:{ref_id} elided. Key: {extract_key(observation)}]"
Example 3: Cache-Friendly Ordering
# Stable content first
context = [system_prompt, tool_definitions] # Cacheable
context += [reused_templates] # Reusable
context += [unique_content] # Unique
Example 4: Budget-triggered optimization policy
budgets:
tool_outputs: 35%
message_history: 30%
retrieved_documents: 20%
reserved_buffer: 15%
triggers:
tool_outputs_over_budget: mask resolved observations
total_context_over_70_percent: compact message history
repeated_irrelevant_retrievals: tighten retrieval scope
Whitespace breaks KV-cache: Even a single whitespace or newline change in the prompt prefix invalidates the entire KV-cache block downstream of that point. Pin system prompts as immutable strings — do not interpolate timestamps, version numbers, or session IDs into them. Diff prompt templates byte-for-byte between deployments.
Timestamps in system prompts destroy cache hit rates: Including Current date: {today} or similar dynamic content in the system prompt forces a full cache miss on every new day (or every request, if using time-of-day). Move dynamic metadata into a user message or a separate tool result appended after the stable prefix.
Compaction under pressure loses critical state: When the model performing compaction is itself under context pressure (>85% utilization), its summarization quality degrades — it omits task goals, drops user constraints, and flattens nuanced state. Trigger compaction at 70-80%, not 90%+. If compaction must happen late, use a separate model call with a clean context containing only the material to summarize.
Masking error outputs breaks debugging loops: Over-aggressive masking hides error messages, stack traces, and failure details that the agent needs in subsequent turns to diagnose and fix issues. During active debugging (error in the last 3 turns), suspend masking for all error-related observations until the issue is resolved.
Partitioning overhead can exceed savings: Each sub-agent requires its own system prompt, tool definitions, and coordination messages. For tasks with fewer than 3 independent subtasks, the coordination overhead often exceeds the context savings. Estimate total tokens (coordinator + all sub-agents) before committing to partitioning.
Cache miss cost spikes after deployment changes: Reordering tools, rewording the system prompt, or changing few-shot examples between deployments invalidates the entire prefix cache, causing a temporary cost spike of 2-5x until the new cache warms up. Roll out prompt changes gradually and monitor cache hit rate during deployment windows.
Compaction creates false confidence in stale summaries: Once context is compacted, the summary looks authoritative but may reflect outdated state. If the task has evolved since compaction (new user requirements, corrected assumptions), the summary silently carries forward stale information. After compaction, re-validate the summary against the current task goal before proceeding.
This skill owns token-efficiency tactics and budget policy. Adjacent skills own diagnosis, storage, and architecture:
context-fundamentals: mental models for why context quality and attention placement matter.context-degradation: diagnosis when output quality has already dropped.context-compression: lossy summarization and handoff strategy.filesystem-context: file-backed offloading for full outputs and logs.multi-agent-patterns: partitioning work across isolated agent contexts.latent-briefing: selective KV retention across orchestrator-worker boundaries in compatible runtimes.evaluation: measuring whether the optimization improved quality, cost, or latency.memory-systems: persistent retrieval layers that feed context just in time.Internal reference:
Related skills in this collection:
External resources:
Created: 2025-12-20 Last Updated: 2026-05-15 Author: Agent Skills for Context Engineering Contributors Version: 2.1.0
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CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).