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AI Coding Agent Context Packing

AI coding agents work better when they start near the right files. In a large repository, a cold agent session often spends early turns searching for routes, services, tests, config, and project rules before it can safely edit code. AgentPack prepares that starting map locally.

AgentPack is a local context engine for AI coding agents such as Claude Code, Codex, Cursor, Windsurf, and Antigravity. It ranks relevant repository files for one concrete task and builds compact task-focused context packs.

The problem

Large repos create context waste:

  • broad file searches before every task
  • missed tests or config files
  • repeated orientation across sessions
  • stale context after active edits
  • hard-to-measure file selection quality

Dumping the whole repo into a prompt is usually too expensive and too noisy. Generic code search is useful, but it does not always answer the coding-agent question: "What should this agent inspect first for this task?"

AgentPack approach

AgentPack scans the repository locally, then scores files against task text, file paths, symbols, imports, related tests, configs, git changes, repo history, and deterministic offline summaries. It writes context with several detail levels:

  • full: selected source content
  • diff: task-scored dirty hunks
  • symbols: definitions and imports
  • skeleton: interface-level structure
  • summary: compact local summary

The result is a compact context pack with selected files, omitted-file receipts, freshness metadata, token stats, and suggested checks.

First command

Use the read-only router when you want a safe starting point:

agentpack route --task "fix flaky payment webhook test"

Use a full context pack when your agent needs a markdown artifact:

agentpack task set "fix flaky payment webhook test"
agentpack pack --task auto

For JavaScript-heavy teams:

npx @vishal2612200/agentpack route --task "fix flaky payment webhook test"

What the agent receives

A route result or pack can include:

  • likely relevant files
  • likely tests
  • scoped repo rules
  • installed skills
  • suggested commands
  • safety warnings
  • task freshness and git state
  • why each file was selected or omitted

The agent still verifies source before editing. AgentPack is a ranked map, not a correctness oracle.

Measuring quality

AgentPack includes benchmark commands so context selection can be tested against files actually changed by real tasks:

agentpack benchmark --release-gate
agentpack benchmark --misses

The public release gate is smoke proof, not a universal guarantee. Add benchmark cases from your own repository when tuning file-selection quality.

When to use AgentPack

Use AgentPack when:

  • repo context is too large to paste
  • agents repeatedly search the same project structure
  • tests or configs are easy to miss
  • teams need explainable local context selection
  • CI jobs need task-specific context artifacts
  • MCP-capable agents should fetch fresh context on demand

AgentPack is not a coding agent, hosted code search, or vector database. It prepares local context so the coding agent starts closer to the right part of the repo.