How do I install the Context Budget skill?

Run npx skillmds@latest add zedarvates/context-budget in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Context Budget safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Context Budget?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Context Budget free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Context Budget?

zedarvates (@zedarvates) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Budget by zedarvates — Agent Skill

Name: Context Budget
Author: zedarvates

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.

zedarvates Updated Sep 17, 2026

File contents

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.

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

Rank — score every skill against the task lexically ([[skill_finder]], 0 tokens), with each skill's token cost.
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.
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.

---name: context-budgetdescription: 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.---# context_budget — optimal context under a token budgetThe 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 thesummed 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 matchesuntil full" heuristic.```bashpython -m skills.context_budget.cli "optimize slow postgres queries and add tests" --budget 3000python -m skills.context_budget.cli "<task>" --budget 4000 --json```## How it selects1. **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 sameway. 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): exactcombinatorial optimization replacing LLM reasoning for structured decisions.

Frequently asked questions