Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/cote-star/agent-chorus/agent-contextnpx skills add cote-star/agent-chorus --skill agent-contextgit clone --depth 1 https://github.com/cote-star/agent-chorusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cote-star/agent-chorus/agent-context)<a href="https://agentmods.dev/skills/cote-star/agent-chorus/agent-context"><img src="https://agentmods.dev/badge/skills/cote-star/agent-chorus/agent-context.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00057 | $0.06483 |
| Opus 5 | $0.00028 | $0.03241 |
| Sonnet 5 | $0.00011 | $0.01297 |
| Haiku 4.5 | $0.00006 | $0.00648 |
Grade A, and why
agent-context scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- agent-context — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 471 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Context
Create, validate, and maintain a structured .agent-context/ directory for a
repository. This is the single most impactful thing you can do to improve how
AI agents work on a large codebase.
How It Works — The Big Picture
What gets created
A .agent-context/ directory in the repo root with three layers:
- Content (5 markdown files) — architecture, code map with risk ratings, change checklists with explicit file paths, silent failure modes, operations. Readable by humans and all agents.
- Authority (3 JSON files, tier 3) — task routing, completeness contracts ("these files MUST be in your answer"), reporting rules. Used by Claude and similar agents that trust structured guidance.
- Navigation (1 JSON file) — search scopes ("search HERE, not THERE"), verification shortcuts with line ranges. Used by Codex, Cursor, and similar agents that verify everything against code.
Plus 2-3 sentence routing blocks in CLAUDE.md / AGENTS.md / GEMINI.md / .cursorrules (~90 tokens each) that tell agents to read the pack before opening any repo files.
Tiers
Not every repo needs the full pack. Choose a tier based on complexity:
| Tier | Files | Best for |
|---|---|---|
| Tier 1 (minimal) | 20_CODE_MAP.md + search_scope.json |
Quick adoption, 50-100 file repos |
| Tier 2 (standard) | + 00_START_HERE.md, 30_BEHAVIORAL_INVARIANTS.md, manifest.json, acceptance_tests.md |
Most repos, 100-500 files |
| Tier 3 (full) | + all 5 markdown + authority layer (routes.json, completeness_contract.json, reporting_rules.json) |
Complex repos, 500+ files, multi-agent workflows |
If using the CLI: agent-context init --tier 1|2|3 . --install-hook (default: tier 3).
First time setup
You ask an agent: "set up agent context for this repo"
The agent reads the entire repo, fills the pack files describing the architecture, key paths, change patterns, and search boundaries, copies the helper tools, installs the advisory freshness hook when safe, validates everything, runs acceptance tests with grep verification, and commits. Takes ~15-20 minutes for a large repo.
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 273 B
- templates/00_START_HERE.md 1.7 KB
- templates/10_SYSTEM_OVERVIEW.md 654 B
- templates/20_CODE_MAP.md 498 B
- templates/30_BEHAVIORAL_INVARIANTS.md 557 B
- templates/40_OPERATIONS_AND_RELEASE.md 568 B
- templates/acceptance_tests.md 2.5 KB
- templates/completeness_contract.json 952 B
- templates/manifest.json 569 B
- templates/reporting_rules.json 1.4 KB
- templates/routes.json 1.6 KB
- templates/search_scope.json 1.3 KB
- tools/check_freshness.sh 876 B runs code
- tools/pre-push-hook.sh 1.4 KB runs code
- tools/verify_agent_context.py 14 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 471 lines · 57 tokens per session scan A 5be1aa4b70a1
agent-context is a skill published in the GitHub repository cote-star/agent-chorus (13 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 6,483 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
gsd-debug
Systematic debugging with persistent state across context resets.
project-context
Use PowerContext project memory and handoff tools through MCP when continuing prior work, recalling decisions, maintaining durable memory, or transferring work across tasks, sessions, or agents.
user-general-facts
Capture and organize general facts about the user by topic.
daily-logs
Track daily activity logs and summaries for the user. TRIGGER BY: read/edit user memory.
aidd-context:02:project-init
Initialize or refresh the project memory bank, scaffold the rules directory, and ensure AI context files contain the project memory block. Use when running aidd init for the first time, bootstrapping a new project, or re-running the init flow on an existing project. Do NOT use for updating individual memory files…
learn
Diagnose and fix agent behavioral surfaces when the user corrects a mistake — connects to Claude native memory.