How do I install the Context Budget skill?

Run npx skillmds add majiayu000/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. Capability flags: docs only. 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?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Budget by majiayu000 — Agent Skill

Name: Context Budget
Rating: 4.666666666666667 (3 reviews)
Author: majiayu000

Context Budget

Context budget tracking and skill loading limits for agent sessions. Monitors token consumption per agent, enforces preloading limits, and alerts on context bloat. Use when spawning agents, configuring skill preloading, or optimizing token usage.

majiayu000 67e29f2 2 files · 3.8 KB Updated Sep 9, 2026 567 repo stars

File contents

Context Budget Management

Why This Matters

The system prompt already includes ~50 built-in instructions. Adding a 451-line CLAUDE.md means every agent starts with ~500 instructions, of which only 10-20 are relevant. Instruction degradation is uniform — when context bloats, ALL instructions degrade, not just those at the bottom.

Skill Preloading Limits

Max 5 preloaded skills per agent (via skills field in agent spec)
Max 500 lines total preloaded skill content per agent
Max 8 on-demand skills available per API request (LLM selection degrades beyond 8)
Preloaded skills bypass the 8-skill on-demand limit entirely

Per-Agent Budget Targets

Agent Tier	Max Preloaded Skills	Max Lines	Rationale
Haiku (junior-coder, docs, explainer, optimizer)	3	200	Limited context capacity
Sonnet (coder, tech-lead, reviewer, qa, etc.)	5	400	Standard working context
Opus (planner, red-teamer, senior-coder)	5	500	Deep reasoning, can handle more

Tracking Checklist

When spawning an agent:

Count preloaded skills from agent spec skills field
Sum total lines across all preloaded SKILL.md files
If > 500 lines total → remove least-relevant skill or split skill into sub-skills
Log which skills are loaded per agent session to claude-mem (for optimizer analysis)

Context Bloat Signals

Watch for these signs that an agent's context is overloaded:

Agent ignores instructions that are clearly in its preloaded skills
Agent repeats questions that are already answered in its context
Agent produces inconsistent output between similar tasks
Agent takes longer on simple tasks (reasoning through noise)

MCP Context Impact

MCP tool descriptions load into context alongside skills. Account for:

Serena: ~500 tokens for tool descriptions (find_symbol, get_symbols_overview, etc.)
context7: ~200 tokens (query-docs, resolve-library-id)
claude-mem: ~300 tokens (search, save_memory, get_observations, timeline)
Linear: ~800 tokens (many issue/project tools) Total MCP overhead: ~1,800 tokens baseline, always present Factor this into per-agent budget calculations.

Measurement

Count lines of each SKILL.md: wc -l .claude/skills/*/SKILL.md
Sum per agent configuration from agent spec skills field

Log task outcomes to claude-mem using this schema:

"Agent [type] ([model]) [passed|failed] [task-type]: [one-line reason]"
Example: "Agent coder (sonnet) passed rust-implementation: all tests green, clippy clean"
Example: "Agent junior-coder (haiku) failed test-writing: couldn't handle async test patterns"

Run optimizer agent periodically to review budget efficiency

---name: context-budgetdescription: Context budget tracking and skill loading limits for agent sessions. Monitors token consumption per agent, enforces preloading limits, and alerts on context bloat. Use when spawning agents, configuring skill preloading, or optimizing token usage.--- # Context Budget Management ## Why This Matters The system prompt already includes ~50 built-in instructions. Adding a 451-line CLAUDE.md means every agent starts with ~500 instructions, of which only 10-20 are relevant. Instruction degradation is uniform — when context bloats, ALL instructions degrade, not just those at the bottom. ## Skill Preloading Limits - **Max 5 preloaded skills per agent** (via `skills` field in agent spec) - **Max 500 lines total** preloaded skill content per agent - **Max 8 on-demand skills** available per API request (LLM selection degrades beyond 8) - Preloaded skills bypass the 8-skill on-demand limit entirely ## Per-Agent Budget Targets | Agent Tier | Max Preloaded Skills | Max Lines | Rationale | |------------|---------------------|-----------|-----------| | Haiku (junior-coder, docs, explainer, optimizer) | 3 | 200 | Limited context capacity | | Sonnet (coder, tech-lead, reviewer, qa, etc.) | 5 | 400 | Standard working context | | Opus (planner, red-teamer, senior-coder) | 5 | 500 | Deep reasoning, can handle more | ## Tracking Checklist When spawning an agent: 1. Count preloaded skills from agent spec `skills` field 2. Sum total lines across all preloaded SKILL.md files 3. If > 500 lines total → remove least-relevant skill or split skill into sub-skills 4. Log which skills are loaded per agent session to claude-mem (for optimizer analysis) ## Context Bloat Signals Watch for these signs that an agent's context is overloaded: - Agent ignores instructions that are clearly in its preloaded skills - Agent repeats questions that are already answered in its context - Agent produces inconsistent output between similar tasks - Agent takes longer on simple tasks (reasoning through noise) ## MCP Context Impact MCP tool descriptions load into context alongside skills. Account for: - Serena: ~500 tokens for tool descriptions (find_symbol, get_symbols_overview, etc.) - context7: ~200 tokens (query-docs, resolve-library-id) - claude-mem: ~300 tokens (search, save_memory, get_observations, timeline) - Linear: ~800 tokens (many issue/project tools) Total MCP overhead: ~1,800 tokens baseline, always present Factor this into per-agent budget calculations. ## Measurement - Count lines of each SKILL.md: `wc -l .claude/skills/*/SKILL.md` - Sum per agent configuration from agent spec `skills` field - Log task outcomes to claude-mem using this schema: ``` "Agent [type] ([model]) [passed|failed] [task-type]: [one-line reason]" Example: "Agent coder (sonnet) passed rust-implementation: all tests green, clippy clean" Example: "Agent junior-coder (haiku) failed test-writing: couldn't handle async test patterns" ``` - Run optimizer agent periodically to review budget efficiency

Frequently asked questions