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agent-context

License Version Python

Portable system context for agents, checked into the repo.

Agents start cold. They re-read the same tree, rediscover the same ownership boundaries, and miss the same hidden invariants. agent-context turns that repeated exploration into a small, reviewable context pack that lives beside the code.

The skill authors the pack. The CLI verifies it, checks freshness, and makes it safe to commit.

agent-context impact at a glance

Quickstart

Two commands set up both the CLI and the bundled skill:

uv tool install agent-context-cli       # or: pipx install agent-context-cli
agent-context install-skill --agent claude

(For Codex, register skills/agent-context/agents/openai.yaml with your Codex skill registry. For Cursor, open the target repo; Cursor reads .cursorrules after the pack exists.)

In the repo you want to improve, ask:

Use the agent-context skill to build context for this repo.

Then make the generated diff reviewable:

agent-context verify .
agent-context freshness . --base-ref origin/main

Open a PR with .agent-context/, the managed routing blocks, and any CI/hook follow-up the skill recommends.

Advanced/manual/scripted setup (from a clone)

Use the CLI from a clone when no agent is in the loop, or when bootstrapping repos in scripts:

git clone https://github.com/cote-star/agent-context.git ~/agent-context
cd /path/to/your-repo
~/agent-context/bin/agent-context init --tier 3 --install-hook .

init scaffolds the files and routing blocks. It does not replace the authoring workflow; the pack still needs repo-specific content before verify will pass.

Why This Exists

Capability What it gives you
Agent-authored context One prompt produces a reviewable .agent-context/ PR instead of a private model memory.
Cross-agent routing Cursor, Claude, Codex, Gemini, OpenCode, and human reviewers consume the same checked-in pack.
Machine checks verify, freshness, doctor, and install-hook make the artifact auditable locally and in CI.
Evidence-backed workflow Q2 2026 rerun: 288 graded tasks across 48 cells, plus a historical reviewer-confirmed run set.
Portable pattern Code repos are the validated venue today; the same context pattern applies to any system with state, rules, risk, and work to do.
Zero infrastructure Markdown and JSON committed to your repo. No server, vector store, crawler, or API key.

What Gets Created

The skill and CLI scaffold a tiered pack under .agent-context/current/:

.agent-context/current/
├── 00_START_HERE.md
├── 10_SYSTEM_OVERVIEW.md
├── 20_CODE_MAP.md
├── 30_BEHAVIORAL_INVARIANTS.md
├── 40_OPERATIONS_AND_RELEASE.md
├── routes.json
├── completeness_contract.json
├── reporting_rules.json
├── search_scope.json
├── manifest.json
└── acceptance_tests.md

.agent-context/tools/
├── verify_agent_context.py
├── check_freshness.sh
└── pre-push-hook.sh

It also writes short managed routing blocks to CLAUDE.md, AGENTS.md, GEMINI.md, and .cursorrules so agents read the pack before opening source files.

Layer Files Job
Content 00_* through 40_* markdown Human-readable map, risks, invariants, operations
Authority routes.json, completeness_contract.json, reporting_rules.json Completeness rules for trust-and-follow agents
Navigation search_scope.json Scoped search and verification shortcuts for search-and-verify agents
Quality manifest.json, acceptance_tests.md, helper tools Validation, freshness, and PR review support

How It Works

The product experience is skill-first:

  1. Ask the agent to use the agent-context skill.
  2. The skill inventories the repo, chooses the right tier, scaffolds files when needed, fills templates, and writes grep-backed acceptance tests.
  3. The CLI verifies structure, JSON schema, real glob matches, template cleanup, and freshness.
  4. You review the diff like code and merge it through PR.

The CLI remains intentionally boring:

agent-context init --tier 3 --install-hook .   # scaffold
agent-context verify .                         # validate pack integrity
agent-context freshness . --base-ref origin/main
agent-context doctor                           # local setup audit

init is a bootstrap command. verify and freshness are what make agent-written context safe to commit.

Results

Q2 2026 multi-agent rerun

Current evidence: 288 graded tasks across 48 cells: 6 repos × 4 model variants × bare/structured × 6 tasks. Every structured_fresh clone passed agent-context verify and strict freshness checks before the agent started.

Agent / Model Bare yes-rate Structured yes-rate Δ
Claude Opus 4.7 80% (4.80/6) 100% (6.00/6) +20pp
Cursor claude-opus-4-7-medium 89% (5.33/6) 97% (5.83/6) +8pp
Cursor composer-2-fast 61% (3.67/6) 81% (4.83/6) +20pp
Codex CLI 0.130.0 72% (4.33/6) 78% (4.67/6) +6pp

Headline stories:

  • Claude Opus + structured: 6/6 across all 6 repos.
  • Cursor composer-2-fast: largest correctness lift at +20 percentage points.
  • Cursor Opus medium: 219s → 78s median duration under structured context.
  • Production-risk flags drop to zero for Codex and Cursor Opus medium with structured context.

Grading is LLM-provisional via independent Claude Code subagents, one fresh-context grader per cell. Treat the Q2 numbers as directional until reviewer spot-audit is complete. Anomalies are disclosed rather than hidden; see metrics methodology.

Full Q2 results · headline metrics · evidence dashboard

Historical reference

The March/April 2026 run set used 78+ reviewer-confirmed grades across three repos. It is preserved as a historical reference and is not directly comparable to the Q2 LLM-provisional rerun.

Metric Bare With agent-context Change
Correct answers 50% 88% +76%
Files opened by Claude 6.3 1.9 ~70% fewer
Tokens used by Claude 38.6K 13.1K ~66% fewer
Dead ends 2-3 per repo 0 eliminated
Production-risk answers 7 total 0 eliminated

agent-context proof summary — per-agent + historical

Agent Architectures

The same .agent-context/ pack serves two opposite loops:

Trust-and-follow: Claude, Gemini, OpenCode with Anthropic backend
  routing block → required files → completeness contract → answer

Search-and-verify: Cursor, Codex, OpenCode with local model
  search scope → scoped grep → verification shortcut → answer

Claude-like agents can stop when the completeness contract is satisfied. Cursor/Codex-like agents still verify against source, but the pack tells them where to search and what evidence matters.

Explorable recall as a three-track system

Tested Repositories

The Q2 rerun used the same general-purpose template across six code repos with zero template modifications.

Repo type Stack Notes
CLI/library Rust + Node.js agent-chorus
ML pipeline Python training/inference workflow
React frontend TypeScript React Query + Zustand
Backend service Python FastAPI service
Polyglot monorepo mixed multi-language workspace
macOS daemon Swift process broker / daemon

The seventh candidate, org-second-brain, was skipped because its experiment setup caused an interactive Claude session loop. It remains a follow-up, not part of the headline slate.

Non-code corpora are not yet validated. The design is intentionally broader than repos, but public evidence currently covers code repositories only.

Tiers

Start small. Promote only when the repo needs more structure.

Tier Files Best for Direct CLI scaffold
1 minimal 2 Quick adoption, smaller repos init --tier 1 .
2 standard 6 Most teams starting out init --tier 2 .
3 full 11 Complex repos, production workflows init --tier 3 --install-hook .

Examples

Example Size Why look at it
examples/hello-service/ 6 files Read the whole pack in five minutes
examples/agent-chorus-reference/ 155 files Real dual Rust/Node CLI pack

Comparison

agent-context Long-term memory Multi-agent orchestration agent-chorus
Primitive Checked-in system context Stored memory Worker coordination Cross-agent session visibility
Best for Cold-start agent work and PR-scoped guidance Persona/history recall Delegated task execution Reading and comparing agent sessions
Runtime dependency none service/vector store optional framework runtime chorus CLI
Lives in repo yes no no no

For multi-agent session visibility and messaging, pair with agent-chorus.

Documentation

Need Document
First install Getting started
Architecture deep-dive Architecture guide
Evidence Experiment results · metrics summary
Agent-driven creation SKILL.md
CI setup CI adaptation
Design rationale 16 design principles
Roadmap Roadmap

Project Scope

The public agent-context CLI, templates, verifier, examples, and evidence docs live here. chorus session-reading commands live in agent-chorus.

Found a bug or a missing system pattern? Open an issue.

About

Navigation contract for AI coding agents in large repos. Templates, validation scripts, and a Python CLI for authoring three-layer context packs. Companion to the agent-recall research.

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