Token Optimization

Token Optimization

Overview

Reducing token usage is rarely a single trick — it's a stack of small wins applied at the right layer. This skill is a triage map: it diagnoses where the tokens are going (system prompt, conversation history, tool results, agent loops, output verbosity), then points to the matching tactic. The correct optimization target is tokens-per-task, not tokens-per-request: a one-shot 12k-token prompt that solves the problem beats a 2k-token loop that takes 30 turns. Apply tactics in order: measure first, then cache, then compress, then route, then trim output.

When to activate

• ✅ User says they're hitting context limits, billing surprises, or slow loops
• ✅ User asks how to use prompt caching, compaction, or extended thinking budgets
• ✅ User wants to choose a cheaper model or split work across models
• ✅ Agent itself notices a long-running session crossing roughly 70% context-window utilization (rule-of-thumb trigger; use exact counts when available, heuristic estimate otherwise — trigger earlier on long-running sessions where headroom matters more, later on short tasks)
• ✅ Reviewing a system prompt, CLAUDE.md, or memory file that "feels heavy"

Do NOT activate when:

• The topic is a specific tokenizer library (use a tokenization-* skill)
• "Token" refers to auth/JWT/payment tokens, or UI design tokens
• The user only wants pricing math (point at provider pricing pages)

Workflow

Apply the tactics in the order below. Each layer is cheap to try and independent of the next, so stop as soon as the cost/context goal is met.

Step 1 — Measure where the tokens go

Before optimizing, locate the cost. For a single request, mentally split the prompt into five buckets and estimate each:

Heuristic estimation (no real tokenizer needed):

• English prose: tokens ≈ words × 1.3
• Code or mixed: tokens ≈ chars / 4
• CJK / multi-byte: tokens ≈ chars / 2

Accuracy is roughly ±15%. For exact counts in a Claude / Anthropic SDK context, use the SDK's count_tokens endpoint before the real call. For detailed budget math (output multipliers, depth tiers), see references/measurement-and-budgets.md.

Output: state which 1–2 buckets dominate. The bucket dictates which step below to focus on.

Step 2 — Make the static prefix cacheable

The single biggest win for repeated calls is prompt caching: a stable prefix (system prompt + tool defs + long reference docs) is hashed once and reused at a heavy discount on subsequent requests within the cache TTL. To exploit it:

• Place everything stable at the front of the prompt; place changing content (the user's current question, the latest tool result) at the end. Cache lookups match by exact prefix.
• Mark the cache breakpoint at the end of the largest stable block. In the Anthropic SDK, set cache_control: {type: "ephemeral"} on the last content block of that prefix.
• Keep the prefix byte-identical between calls. Reordering tool defs, inserting timestamps, or shuffling memory files invalidates the cache.
• Group reads in a stable order so file-content blocks reused across turns hit the same cache entry.

When the prefix legitimately needs to change, never edit an earlier message in place. Append a new message carrying the updated state instead — every prior call's cached prefix stays valid.

Editing in place rewrites bytes the cache already hashed — every subsequent call misses. Appending costs only the new message's tokens. (Role names like role=developer / role=user above are Anthropic-flavored — use the equivalent role bucket on other hosts.)

For provider-specific cache mechanics (TTL, minimum block sizes, billing behavior), see references/prompt-caching.md.

Step 3 — Slim the always-loaded context

Files like CLAUDE.md, AGENTS.md, GEMINI.md, .cursorrules, and memory indexes are injected on every message. A 5,000-token memory file across 100 messages is 500,000 input tokens of pure overhead.

Targets and trims:

Slimming rules:

1. Cut defaults. "You are a helpful assistant", "write clean code" — the model already does these.
2. Merge overlapping bullets. "Be concise" + "don't be verbose" + "keep responses short" → one bullet.
3. Move examples to references. Examples bloat the always-loaded payload; put them in on-demand references/<topic>.md files.
4. Bullets, not paragraphs. Shorter and parsed more reliably.
5. Drop unused agents/skills. If a memory entry hasn't been referenced in two weeks, it's a candidate for removal.
6. Audit MCP-style tool servers. Each enabled server injects its tool defs into every turn — ten servers with 5–10 tool defs each is easily 5,000+ tokens of per-turn overhead. Audit the enabled list against actual use — disable servers that haven't been called in the last few sessions before reaching for harder optimizations. A working ceiling of around 10 enabled per project keeps overhead in check, but capability trumps the count: keep what the task needs. Prefer a CLI binary when an equivalent exists (e.g., gh over a GitHub MCP server).

Step 4 — Compress conversation history

Long sessions accumulate stale turns. Apply a tiered compression policy when the history bucket dominates Step 1's accounting:

• Last 3–5 turns: keep verbatim.
• Turns 6–10: keep decisions and conclusions, drop intermediate reasoning.
• Older than that: one-line summary per turn — but keep verbatim any turn whose specific reasoning is still load-bearing for the current task.
• Failed tool calls past the fix: drop the failure noise; keep the fix.
• Large file reads superseded by edits: keep only the final state.

Pin the primary objective at the top of any compressed prompt so the model never loses the goal during compression. Never prune system instructions or safety headers. For the structured-summary template and artifact-trail rules, see references/context-management.md.

Anti-anxiety calibration. Frontier models with context-window awareness sometimes underestimate remaining context and start rushing — premature summaries, shortcut decisions, "let me finalize" mid-task. Counter it with:

• Aim to stay well below the window cap on long sessions (e.g., budget ~200k of a 1M window for active context). The headroom is a soft buffer for the model's confidence, not a hard ceiling — extend it when the task genuinely needs more.
• Include an explicit budget statement near the start of the compressed snapshot (not in the cacheable static prefix — that would break Step 2's discipline): "Context budget: ~180k tokens remaining. Continue working at full depth."
• If you spot mid-task wrap-up signals (sudden summary, "let me finalize") while real work clearly remains, re-state the budget rather than letting the model self-direct. If the wrap-up is correct — task done or context genuinely tight — let it finalize.

This is model-side behavior, not host-side — applies on any host running a context-aware model.

Step 5 — Mask verbose tool observations

Tool output is the largest unbounded source of tokens. Replace verbose results with observation references the model can re-fetch on demand:

• Successful file read → [read src/foo.ts: 312 lines, exports: a,b,c]
• Successful search → [grep "X": 7 hits in 4 files: …]
• Long command stdout → keep first/last 5 lines + exit code
• Screenshots / images → resize to the lowest resolution that still works for the task. 1280×720 is usually the sweet spot for UI screenshots; bump up to 1920×1080 or higher for dense text / OCR work where legibility is load-bearing — full 4K is ~5× more tokens than needed for typical UI work.

This is masking, not deleting: the underlying file/command is still addressable, the model just doesn't carry the bytes through every turn.

Step 6 — Route to the right model

Match model to task complexity. Reaching for the largest model on every call is the most common single source of overspend.

Keep a fallback chain (top → mid → small) for rate-limit or latency backoff. When sub-agents are available, delegate narrow searches and file scans to a cheaper-model sub-agent and return only the synthesis to the main loop — the main loop never pays for the raw search output.

Step 7 — Cut agent-loop overhead

Each loop iteration re-transmits the full conversation context. Reducing iterations is multiplicative.

• Batch tool calls. Issue independent tool calls in parallel in one assistant turn, not sequentially across turns.
• Combine small tasks. Process N files in one iteration instead of spawning N iterations.
• Set iteration caps. Cap retries on a single failing operation (e.g., ~5) with a clean "summarize and stop" exit, while leaving room for legitimately multi-step plans to run their length.
• Avoid speculative reads. Read what the task needs, not "everything related". Use glob/grep to narrow before opening files.
• Background long jobs. Don't keep the model in a polling loop on a multi-minute task — schedule a wake-up or use the host's background primitive.

For platform-specific patterns (parallel tool calls in the Anthropic SDK, sub-agent delegation in Claude Code, Task tools in Codex), see references/agent-loop-patterns.md.

Step 8 — Trim the output

Output tokens are typically priced higher than input. Tactics:

• Depth tiers. Offer "essential / moderate / detailed / exhaustive" before answering long-form questions; default to the smallest tier the user clearly implied. When intent is ambiguous, err one tier higher rather than under-answering.
• Cut filler, keep orientation. Skip pure throat-clearing ("Sure, here is …", "Great question!"). A one-line orientation ("Updated X to Y because Z") is fine and often helpful — the goal is to cut filler, not strip context.
• Structured output for machine consumption. JSON / tool-use schemas are usually shorter than equivalent prose and don't need filler.
• Stop sequences. When a known terminator delimits the useful output, set it; the model stops generating once reached.
• Cap thinking. For models with extended thinking, set an explicit thinking budget (e.g., 1k–4k tokens for routine tasks; reserve larger budgets for genuinely hard problems).

For depth-tier templates, bridge-phrase strip list, stop-sequence cookbook, and thinking-budget calibration tables, see references/output-and-verification.md.

Step 9 — Verify the win

Before declaring success, re-measure with the same buckets from Step 1 and report a delta:

Before: input=N1, output=M1, est cost C1
After:  input=N2, output=M2, est cost C2
Delta:  -X% input, -Y% output, -$Z per call

If the delta is negligible, the dominant bucket was different from what you optimized — go back to Step 1 with fresh measurements.

For the full re-measurement template (incl. CSV variant for tracking multiple optimization rounds), the negligible-delta troubleshooting matrix, tokens-per-task vs tokens-per-call verification method, and "when to call optimization done" exit conditions, see references/output-and-verification.md.

Expected ranges. Use these as calibration points, not guarantees:

• Compaction: 50–70% reduction at <5% quality loss.
• Masking: 60–80% reduction in masked observations.
• Prefix cache hit rate: ≥ 70% on stable workloads.

If your delta is at the upper end of these ranges, you're in the diminishing-returns zone for that tactic on typical workloads — further wins come from layering, not from squeezing one lever harder.

Cross-agent invocation

The tactics above are agent-agnostic; the commands to apply them differ per host runtime. Use this table to translate a tactic into the host you're in.

Notes:

• Env vars apply to the host runtime's settings file (e.g., ~/.claude/settings.json's env block for Claude Code). Other hosts have their own settings location.
• n/a means the feature is not exposed on that host's CLI surface today — the underlying tactic may still apply via direct provider SDK use.
• MCP-style tool servers count against tool-definition tokens on every host that supports them. Keep enabled count modest (around 10 per project is a reasonable working ceiling); see Step 3 for the broader instruction-file diet.

Rules

Hard rules (never violate):

• Never prune system instructions, safety headers, or compliance text during compression — context-loss jailbreaks are a real risk.
• Never invalidate a stable prefix unintentionally (e.g., by injecting a timestamp or random ID at the front). If a value must vary per call, put it at the end.
• Tokens-per-task is the optimization target, not tokens-per-request. Don't celebrate a 30% per-call saving that quintuples the loop count.
• Don't remove an instruction just because the model "should know it" if doing so changes user-visible behavior. Verify with a real example.

Preferences (override-able):

• Apply tactics in the listed order; stop as soon as the goal is met.
• Prefer reference-fetch (file paths the model can re-read) over inline-dumping large content.
• For repeated calls, optimizing the cacheable prefix usually beats any other single change.
• When unsure between two tactics, pick the one that makes the prompt shorter at the start (caching benefits) over the end.

Output

This skill produces a diagnosis and a prioritized tactic list — not a mechanical rewrite. The expected hand-off is:

1. A bucketed measurement of current token usage (Step 1).
2. The 2–3 tactics from Steps 2–8 most likely to move the dominant bucket.
3. Concrete proposed edits (system-prompt diff, cache-breakpoint placement, model-routing rule, etc.) for the user to accept or reject.
4. A re-measurement after changes are applied (Step 9).

The abstract consumer is whichever agent runtime is hosting this session (Claude Code, Codex, Cursor, Gemini CLI, Copilot). Tactics that reference provider-specific features (cache_control, thinking budgets, sub-agents) carry their own provider tag in the reference files.

Progressive disclosure

Heavy detail lives in subfolders, loaded only on demand:

• references/measurement-and-budgets.md — load during Step 1. Heuristic-vs-exact estimation, complexity-based output multipliers, depth-tier math.
• references/prompt-caching.md — load during Step 2. Cache-breakpoint placement, TTL, minimum block sizes, invalidation triggers, debugging cache misses.
• references/context-management.md — load during Steps 3–5. Slimming rules with examples, structured summary template for compaction, observation-masking patterns, image resolution guidance.
• references/agent-loop-patterns.md — load during Steps 6–7. Model-routing decision table, parallel tool-call pattern, sub-agent delegation, iteration caps, background job patterns.
• references/output-and-verification.md — load during Steps 8–9. Depth-tier picker template + default-tier heuristic, bridge-phrase strip list, structured-output rules of thumb, stop-sequence cookbook by use case, thinking-budget calibration table, re-measurement template (incl. CSV variant), negligible-delta troubleshooting matrix, "when to call optimization done" exit conditions.

No assets/ or scripts/ ship in v1. Token estimation is heuristic and described inline; if a project later wants exact counts, call the host provider's count_tokens endpoint directly.

Body budget

Soft targets — not hard caps. Grow the body when the topic genuinely warrants it; move overflow to references/<topic>-extras.md rather than truncating signal.

• description: agentskills.io spec caps this at 1,024 chars; that one is an external hard limit, not a soft target.
• Body: aim for ~500 lines / ~5,000 tokens as a comfortable read.
• Total file size: stay well below ~30,000 chars so the body still parses cleanly when re-rendered as a Copilot custom agent.