| name | context-budget-audit |
| description | Audit the seven claimants on an LLM call's context window, set a working ceiling, and cut in the right order. Use when prompts grow, agents drift, or token bills surprise you. |
| version | 1.0.0 |
| author | Zorost Intelligence |
| license | MIT |
| metadata | {"zorost":{"tags":["context-engineering","tokens","cost","build"],"related_skills":["prompt-suite-versioning","agent-loop-safety"],"program_weeks":[6]}} |
Context Budget Audit
1 · Purpose
Make the context window a managed budget, allocated, measured, and alerted, instead
of an invisible resource that fails silently when it runs out.
2 · When to use
- Designing any new LLM call or agent step.
- When an agent "forgets" instructions it was given earlier (the classic symptom).
- When token cost or latency climbs without a quality gain.
3 · Inputs
- The model's advertised context limit (from its card or API docs).
- One real captured request (log, trace, or print of the assembled messages).
- The actual tokenizer for the model, or
tiktoken/the provider's usage field.
4 · Procedure
- Set the working ceiling: 80% of the advertised limit. The last 20% is
headroom you never plan against, models degrade near the wall.
- Measure, do not estimate, the seven claimants with the real tokenizer:
system instructions, tool schemas, durable memory, conversation history,
retrieved evidence, scratchpad/plan state, and the reservation for the model's
own output.
- Write the budget table: claimant × tokens × percent of ceiling. Total must be
under 100% with output space reserved as a real line item.
- Order the material: stable content first (cache-friendly), background in the
middle, decisive evidence last, next to the instruction it decides.
- Set the compaction policy for anything that grows: summarize older turns, keep
decisions and commitments, drop scaffolding. Tier every claimant as pinned,
compressible, or disposable.
- Add the runtime guard: log
ctx=used/ceiling on every call; alert above 90%.
- When over budget, cut in this order, disposable history first, then
compressible memory, then retrieved evidence (retrieve less, rerank harder).
Never cut the output reservation or the decisive evidence last-mile.
- Re-measure after the cut and record both budgets in the design note.
5 · Anti-rationalization
| Excuse | Answer |
|---|
| "The window is huge; budgeting is premature." | Huge windows still fail silently, "lost in the middle" does not throw an error. |
| "I'll estimate tokens by eye." | Four characters per token is a folk average, wrong per model and per language. Tokenize for real. |
| "Compaction loses information." | Unbounded growth loses the instruction the agent stops honoring. Compaction chooses what to keep. |
| "The API will error if we're over." | Some APIs truncate silently. The 90% alert is the only honest signal. |
6 · Red flags
- Nobody can say how many tokens the system prompt costs.
- History grows unbounded between two log lines.
- Retrieved chunks are appended, never re-ranked or trimmed.
- The output reservation is zero, the model's answer gets whatever is left.
7 · Verify
- A budget table exists with measured (not estimated) token counts for all seven
claimants, totaling under the working ceiling.
- The runtime log shows
ctx=used/ceiling per call.
- A compaction or tiering policy is named for every growing claimant.
8 · ZoroLogistics example
Week 6's extraction call against an 8,000-token working ceiling: system+schema 78,
document ~123, history 0 (single-shot), output reservation 1,500. The audit catches
the real risk early, a later version that threaded documents through a shared
session pushed history to 4,000 and silently dropped the null rule. The ctx= log
line is what made the regression visible in minutes instead of at the weekly review.
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