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Agent Ergonomic CLI Spec

AXI — Agent eXperience Interface

Let's build apps that [agents] love. 10 design principles for agent-ergonomic CLI. 100% task success at the lowest cost — validated across benchmarks and 915 runs.

Avg Cost
$0.074
vs $0.120 MCP-code
Avg Duration
21.5s
vs 36.2s MCP-code
Task Success
100%
Validated benchmark
Install AXI Skill

Add the AXI skill globally to fetch the design guidelines & scaffolding:

npx skills add kunchenguid/axi --skill axi
Run `npx -y gh-axi` for GitHub ops Run `npx -y chrome-devtools-axi` for browser
01. Overview

Why AXI?

AI agents interact with external services through two dominant paradigms: shell-based CLI execution (parsing text outputs) and Model Context Protocol (invoking typed tools through the framework).

The Problem: CLIs separate action from observation (forcing repeated snapshot calls and wasting token budgets). MCPs introduce massive schema overhead (averaging 185K tokens per task vs. 79K for AXI) and suffer from poor discoverability.

The Solution: AXI treats token budgets as a first-class constraint. It achieves the reliability advantages MCP promises (structured outputs, discoverability) at the cost profile of a CLI.

An agent-first CLI not only closes the gap but surpasses MCP on every metric: success, cost, duration, and turns.

02. Design Principles

10 Rules for Agent-Ergonomic CLI

1

Token-Efficient Output

Use Token-Optimized Object Notation (TOON) instead of JSON on stdout. TOON omits braces, quotes, and commas, yielding ~40% token savings while remaining highly readable by LLMs.

2

Minimal Default Schemas

Return 3–4 fields per list item by default, not 10+. Agents rarely need all fields and can request additional ones explicitly via a --fields flag.

3

Content Truncation

Truncate large text fields to a configurable limit, appending a size hint (e.g. ... (truncated, 2847 chars total — use --full)). This avoids wasting token budget.

4

Pre-Computed Aggregates

Include aggregate or derived fields (like totalCount or checks summary) to eliminate round trips. A single detail prevents an entire follow-up request.

5

Definitive Empty States

Output explicit zero-result messages (e.g. "0 tasks found") rather than empty output. Empty output leaves agents unsure if the command failed silently.

6

Structured Errors & Exit Codes

Mutations must be idempotent, errors written to stdout (not stderr) in structured format, and commands must never block on interactive prompts.

7

Ambient Context

Install into the agent's session hooks (e.g. Claude Code or Codex start hooks) so every conversation begins with relevant directory-scoped state already visible.

8

Content First

Running a command with no arguments should display live, actionable data rather than help text. Let the agent see the state and act immediately.

9

Contextual Disclosure

Append actionable help[] lines at the end of output suggesting next steps as concrete parameterized commands (e.g. tasks view <id>).

10

Consistent Way to Get Help

Provide concise per-subcommand --help flags that list available flags, defaults, required parameters, and 2-3 concrete examples.

03. Ecosystem Catalog

AXIs in the Wild

Official Reference implementations
AXI Command Domain Description
gh-axi GitHub Issues, PRs, workflow runs, releases. Wraps the official gh CLI with agent-ergonomic output.
chrome-devtools-axi Browser Automation Navigate, click, fill, and extract with combined operations. Wraps chrome-devtools-mcp.
lavish-axi Human Review Turns agent-generated HTML artifacts into collaborative review surfaces for annotations & feedback.
Community Implementations
AXI Command Author Domain Description
npm-axi SSBrouhard npm registry Search & inspect npm registry packages, README previews, and dependency trees with TOON output.
sqlite-axi SSBrouhard SQLite Databases Inspect schemas, sample rows, and run capped read-only SQLite queries with TOON output.
slack-axi Jarvus Innovations Slack messaging Read, search, sweep, and safely draft Slack messages with token-efficient output.
gws-axi Jarvus Innovations Google Workspace Gmail, Calendar, Docs, Drive, and Slides; draft mail, never sends to maintain safety boundaries.
harvest-axi Jarvus Innovations Time tracking Review, log, and edit Harvest time entries for yourself, team, projects or clients.
specops Jarvus Innovations Spec-driven dev Spec-driven development for agents; ships embedded in a skill instead of a standalone npm binary.
gitsheets-axi Jarvus Innovations Git-backed data Read and mutate git-backed records over the shell with TOON output and idempotent commits.
metabase-axi Jarvus Innovations Analytics / BI Query, explore, and export from Metabase over the shell (SQL/MBQL) with schema introspection.
otter-axi Jarvus Innovations Otter.ai meetings Find and pull Otter.ai meeting transcripts headlessly over the shell.
04. Skill Specification

Runtime Skill Schema (SKILL.md)

---
name: axi
description: >
  Agent eXperience Interface (AXI) — ergonomic standards for building CLI tools that agents
  use via shell execution. Use when building, modifying, or reviewing any agent-facing CLI.
---

# Agent eXperience Interface (AXI)

AXI defines ergonomic standards for building CLI tools that autonomous agents interact with through shell execution.

## Before you start

Read the [TOON specification](https://toonformat.dev/reference/spec.html) before building any AXI output.

## 1. Token-efficient output

Use [TOON](https://toonformat.dev/) (Token-Oriented Object Notation) as the output format on stdout.
TOON provides ~40% token savings over equivalent JSON while remaining readable by agents.
Convert to TOON at the output boundary — keep internal logic on JSON.

```
tasks[2]{id,title,status,assignee}:
  "1",Fix auth bug,open,alice
  "2",Add pagination,closed,bob
```

## 2. Minimal default schemas

Every field in stdout costs tokens — multiplied by row count in collections.
Default to the smallest schema that lets the agent decide what to do next: typically an identifier, a title, and a status.

- Default list schemas: 3-4 fields, not 10
- Default limits: high enough to cover common cases in one call (if most repos have <100 labels, default to 100, not 30)
- Long-form content (bodies, descriptions) belongs in detail views, not lists
- Offer a `--fields` flag to let agents request additional fields explicitly

## 3. Content truncation

Detail views often contain large text fields. Omitting them forces agents to hunt; including them wastes tokens.
Truncate by default and tell the agent how to get the full version.

```
task:
  number: 42
  title: Fix auth bug
  state: open
  body: First 500 chars of the issue body...
    ... (truncated, 8432 chars total)
help[1]: Run `tasks view 42 --full` to see complete body
```

- Never omit large fields entirely — include a truncated preview
- Show the total size so the agent knows how much it's missing
- Suggest the escape hatch (`--full`) only when content is actually truncated
- Choose a truncation limit that covers most use cases (500-1500 chars)

## 4. Pre-computed aggregates

The most expensive token cost is often not a longer response — it's a follow-up call. If your backend has data that agents commonly need as a next step, compute it and include it.

**Aggregate counts**: include the **total count** in list output, not just the page size. Agents need "how many are there?" and will paginate if the answer isn't definitive.

```
count: 30 of 847 total
tasks[30]{number,title,state}:
  1,Fix auth bug,open
  ...
```

**Derived status fields**: when the next step almost always involves checking related state, include a lightweight summary inline.

```
task:
  number: 42
  title: Deploy pipeline fix
  state: open
  checks: 3/3 passed
  comments: 7
```

Only include derived fields your backend can provide cheaply — a summary ("3/3 passed"), not the full data.

## 5. Definitive empty states

When the answer is "nothing", say so explicitly. Ambiguous empty output causes agents to re-run with different flags to verify.

```
$ tasks list --state closed
tasks: 0 closed tasks found in this repository
```

State the zero with context. Make it clear the command succeeded — the absence of results is the answer.

## 6. Structured errors & exit codes

### Idempotent mutations

Don't error when the desired state already exists. If the agent closes something already closed, acknowledge and move on with exit code 0. Reserve non-zero exit codes for situations where the agent's intent genuinely cannot be satisfied.

```
$ tasks close 42
task: #42 already closed (no-op)    # exit 0
```

### Structured errors on stdout

Errors go to **stdout** in the same structured format as normal output, so the agent can read and act on them. Include what went wrong and an actionable suggestion. Never let raw dependency output (API errors, stack traces) leak through.

```
error: --title is required
help: tasks create --title "..." [--body "..."]
```

- Validate required flags before calling any dependency
- Translate errors — extract actionable meaning, discard noise
- Never leak dependency names — suggestions reference your CLI's commands, not the underlying tool

### No interactive prompts

Every operation must be completable with flags alone. If a required value is missing, fail immediately with a clear error — don't prompt for it. Suppress prompts from wrapped tools.

### Output channels

- **stdout**: all structured output the agent consumes — data, errors, suggestions
- **stderr**: debug logging, progress indicators, diagnostics (agents don't read this)
- **Exit codes**: 0 = success (including no-ops), 1 = error, 2 = usage error

Never mix progress messages into stdout. An agent that reads "Fetching data..." will try to interpret it as data.

## 7. Ambient context via session integrations

Register your tool into the agent's session lifecycle so every conversation starts with relevant state already visible — before the agent takes any action.

**Pattern:**

1. Provide an explicit setup command that installs or repairs a session hook or plugin after user intent is clear
2. At session start, the integration runs your tool and provides a compact dashboard as context
3. The agent receives this as initial context and can act immediately

```
# Agent sees this at session start — no invocation needed:
specs[2]{id,title,status}:
  1,Fix auth bug,open
  2,Add pagination,in-progress

help[2]:
  Run `mytool specs view 1` for details
  Run `mytool specs create --title "..."` to add a spec
```

**Rules:**

- **Default app targets**: by default, support Claude Code, Codex, and OpenCode. Do not hard-code a single agent integration when the tool can reasonably support multiple agents
- **Explicit opt-in**: register hooks or plugins only from a user-invoked setup command, not from ordinary CLI commands
- **Portable commands**: hook commands should use a PATH-verified binary name when it resolves to the current executable, and fall back to the full absolute path otherwise. This keeps global installs portable while ensuring hooks do not accidentally run a different binary
- **Path repair**: setup commands should check existing hooks and update the executable path if it has changed (e.g., after reinstall or relocation)
- **Idempotent**: repeated installs with the same path are silent no-ops
- **Directory-scoped**: show only state relevant to the current working directory
- **Token-budget-aware**: this context loads on _every_ session — ruthlessly minimize it. Include just enough for the agent to orient and act; deep data belongs in explicit invocations
- **Lifecycle capture**: use session-end hooks to capture what happened (transcripts, files touched, specs referenced) so future session-start context gets richer over time

**How to integrate with each app:**

- **Claude Code**: use native hooks in `~/.claude/settings.json` or project `.claude/settings.json`. Prefer `SessionStart` to inject compact context via stdout
- **Codex**: use native hooks in `~/.codex/hooks.json` or `<repo>/.codex/hooks.json`, and ensure `[features].hooks = true` in `config.toml`. Prefer `SessionStart` for ambient context via stdout
- **OpenCode**: use a managed plugin in `~/.config/opencode/plugins/`. Prefer ambient system-context injection for the home view rather than adding a custom tool

**Also ship an installable skill (secondary recommendation):**

The session hook is the primary integration, but it only helps agents whose harness supports hooks, and it loads on _every_ session.
Offer an installable [Agent Skill](https://agentskills.io) as a secondary discovery path.
It loads on demand when the agent recognizes a matching task, carries no per-session token cost, and works in any agent that supports the skill format.
Recommend the hook first (ambient context plus live state) and the skill second (lower overhead, broader agent support) - they are complementary, and a user installs whichever fits, or both.

```sh
npx skills add <owner>/<repo> --skill <name>
```

- **Single source of truth**: generate `SKILL.md` from the same content your no-args home view prints, so the skill never drifts from the CLI's own guidance. Add a `--check` build step to CI that fails if the committed skill is stale
- **Strip live state**: a skill is static, so omit dynamic data (open sessions, current items) that only the hook can show
- **Non-interactive commands**: rewrite command examples to a form the agent can run without a global install (e.g. `npx -y mytool ...`), since a skill may be installed without the binary on PATH
- **Trigger-shaped frontmatter**: include `name` and a `description` written as a trigger — terse and outcome-focused so the agent loads it on the right intent
- **Document both paths**: in your README, present the hook and the skill as two ways to achieve the same thing, and make clear the user only needs one

## 8. Content first

Running your CLI with no arguments should show the most relevant live content — not a usage manual.
When an agent sees actual state it can act immediately. When it sees help text, it has to make a second call.

```
$ tasks
tasks[3]{id,title,status}:
  1,Fix auth bug,open
  2,Add pagination,open
  3,Update docs,closed
help[2]:
  Run `tasks view <id>` to see full details
  Run `tasks create --title "..."` to add a task
```

## 9. Contextual disclosure

Include **a few next steps** that follow logically from the current output.
The agent discovers your CLI's surface area organically by using it, not by reading a manual upfront.

Rules:

- **Relevant**: after an open item → suggest closing; after an empty list → suggest creating; after a list → suggest viewing
- **Actionable**: every suggestion is a complete command (or template) carrying forward any disambiguating flags from the current invocation (e.g., `--repo`, `--source`)
- **Parameterize dynamic values**: when a suggested command needs a runtime value such as an ID, title, branch, URL, or path, use placeholders like `<id>` or `"<title>"` instead of guessing a concrete value that may mislead the agent
- **Omit when self-contained**: when the output fully answers the query (a detail view, a count, a confirmation), suggestions are noise — leave them out. Include them on list and mutation responses where the next step isn't obvious.
- **Guide discovery, not workflows**: suggest a variety of possible next actions, don't prescribe a fixed sequence. An agent that already knows what it wants should never be nudged into an extra step.
- **Reveal truncated lists**: when a list shows only the most recent N items out of a larger total, add a help hint telling the agent how to see all of them (e.g., `Run 'mytool list' for all 47 items`). Don't encode pagination into TOON array headers — use help hints instead.
- **Resolve errors**: on errors, suggest the specific command that fixes the problem, not "see `--help`"

## 10. Consistent way to get help

The top-level home view should also identify the tool itself before the live data:

- Include the absolute path of the current executable, with the user's home directory collapsed to `~`
- Include a one-sentence description of what this AXI does

```
$ tasks
bin: ~/.local/bin/tasks
description: Manage project tasks in the current workspace
...
```

Every subcommand should support `--help` with a concise, complete reference: available flags with defaults, required arguments, and 2-3 usage examples. Keep it focused on the requested subcommand — don't dump the entire CLI's manual.