# Context Budget

> Pick the optimal set of skills/docs to load for a task under a token budget — an exact 0/1 knapsack (maximize relevance while summed token cost stays under budget), not an LLM "decide what's relevant" call. Deterministic, stdlib, 0 cloud tokens. Cuts the always-on context cost (loading the whole skill catalog every turn). Use when you want to load only the most relevant context within a budget, or to decide which skills/docs an agent should read for a task.

- Skill: `zedarvates/context-budget` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add zedarvates/context-budget`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zedarvates/context-budget/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: zedarvates (https://skillmd.com/u/zedarvates)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zedarvates/context-budget

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# context_budget — optimal context under a token budget

The OR-Tools principle applied to the agent's always-on cost: choosing *which*
skills/docs to load is a **0/1 knapsack** — maximize total relevance while the
summed token cost stays under a budget. That's an exact deterministic solver
(stdlib DP), so it costs **0 tokens** and beats the greedy "take the top matches
until full" heuristic.

```bash
python -m skills.context_budget.cli "optimize slow postgres queries and add tests" --budget 3000
python -m skills.context_budget.cli "<task>" --budget 4000 --json
```

## How it selects

1. **Rank** — score every skill against the task lexically ([[skill_finder]],
   0 tokens), with each skill's token cost.
2. **Knapsack** — `knapsack(items, budget)` finds the subset that maximizes
   summed relevance subject to `Σ tokens ≤ budget` (exact DP, token costs scaled
   to bound the table). Optimal, not greedy.
3. **Frame** — report the chosen set, tokens used, relevance captured, and the
   saving vs loading the whole catalog.

On this repo a typical task loads ~4 skills (~2k tok) instead of the whole
~36-skill catalog (~15k tok) — an ~85% cut in always-on context for that task.

The `knapsack(items, budget)` engine is generic (takes `Item(name, kind, tokens,
relevance)`), so docs ([[docs_steward]]) or any context source plug in the same
way. Exposed via [[llm_mcp]] as `context_budget`. Related: [[skill_finder]]
(ranking), [[metrics]] (measures the always-on cost this cuts).

First of the deterministic "solver" hybridizations (OR-Tools-inspired): exact
combinatorial optimization replacing LLM reasoning for structured decisions.