Authoring Commands

This guide explains how to write effective commands for Archon’s AI workflow system. Commands are the building blocks of workflows - each command is a prompt template that instructs the AI agent what to do.

What is a Command?

A command is a markdown file that serves as a detailed instruction set for an AI agent. When a workflow executes a step like - command: investigate-issue, Archon:

1. Loads the command file from .archon/commands/investigate-issue.md
2. Substitutes variables like $ARGUMENTS with actual values
3. Sends the entire document as a prompt to the AI
4. The AI follows the instructions and produces output

Commands are prompts, not code. They guide AI behavior through clear instructions.

---

File Format

Commands live in .archon/commands/ relative to the working directory and are loaded at runtime.

CLI vs Server: The CLI reads commands from wherever you run it (sees uncommitted changes). The server reads from ~/.archon/workspaces/owner/repo/, which only syncs from the remote before worktree creation — so changes must be committed and pushed for the server to pick them up.

Commands use this structure:

---
description: One-line description shown in /commands list
argument-hint: <expected-input-format>
---

# Command Name

**Input**: $ARGUMENTS

---

[Instructions for the AI agent...]

Frontmatter Fields

Field	Required	Purpose
description	Recommended	Shown in /commands list and workflow routing
argument-hint	Optional	Tells users what input to provide

---

The Golden Rule: Artifacts Are Everything

The artifact you produce IS the specification for the next step.

In multi-step workflows, agents don’t share memory. The ONLY way to pass information between steps is through artifacts - files saved to disk.

Step 1: investigate-issue    Step 2: implement-issue
┌─────────────────────┐      ┌─────────────────────┐
│ AI Agent A          │      │ AI Agent B          │
│                     │      │                     │
│ Analyzes issue      │      │ Reads artifact      │
│ Produces artifact ──┼──────┼─> Executes plan     │
│                     │      │                     │
└─────────────────────┘      └─────────────────────┘
        │                            │
        ▼                            │
  $ARTIFACTS_DIR/                    │
  issues/issue-123.md ◄──────────────┘

Why This Matters

• No shared context: Each workflow node can run with context: fresh
• Resumability: If a step fails, the artifact preserves progress
• Auditability: Artifacts create a paper trail of AI decisions
• Handoff quality: The artifact determines if the next step succeeds

What Makes a Good Artifact

The artifact must contain everything the next agent needs:

Include	Why
Problem statement	Next agent needs context
Specific file paths + line numbers	No guessing where to look
Actual code snippets	Not summaries - real code
Step-by-step implementation plan	Actionable without questions
Validation commands	How to verify success
Edge cases and risks	What to watch out for

Bad artifact: “Fix the authentication bug in the login handler”

Good artifact:

## Problem
Users get 401 errors when token refresh races with API calls.

## Root Cause
`src/auth/refresh.ts:45` - The refresh lock doesn't wait for in-flight requests.

## Implementation Plan

### Step 1: Add request queue
**File**: `src/auth/refresh.ts`
**Lines**: 45-60

**Current code:**
```typescript
async function refresh() {
  // Current problematic code
}
```

**Change to:**
```typescript
async function refresh() {
  // Fixed code with queue
}
```

### Step 2: Add test
**File**: `src/auth/refresh.test.ts`
**Action**: CREATE

```typescript
describe('refresh', () => {
  it('queues requests during refresh', async () => {
    // Test implementation
  });
});
```

## Validation
```bash
bun run type-check
bun test src/auth/
```

---

Command Structure

Phase-Based Organization

Break commands into clear phases. This helps the AI:

• Know where it is in the process
• Self-verify before proceeding
• Recover if something fails

## Phase 1: LOAD - Get Context

### 1.1 First action
[Instructions...]

### 1.2 Second action
[Instructions...]

**PHASE_1_CHECKPOINT:**
- [ ] Data loaded
- [ ] Context understood
- [ ] Ready to proceed

---

## Phase 2: ANALYZE - Process Information

[...]

Why Phases Work

1. Chunked reasoning: AI handles complex tasks better in pieces
2. Self-verification: Checkpoints force the AI to validate progress
3. Debugging: When something fails, you know which phase
4. Consistency: Similar structure across commands = predictable behavior

Common Phase Patterns

Phase Name	Purpose	Example Actions
LOAD	Gather inputs and context	Read files, fetch from GitHub, parse arguments
EXPLORE	Understand the codebase	Search for patterns, trace code flow
ANALYZE	Form conclusions	Root cause analysis, design decisions
GENERATE	Produce output	Write artifact, create files
VALIDATE	Verify correctness	Run tests, check types, review output
COMMIT	Save to git	Stage, commit, push
REPORT	Communicate results	Output summary to user

---

Checkpoints

End each phase with a checkpoint:

**PHASE_2_CHECKPOINT:**
- [ ] Root cause identified with evidence
- [ ] All affected files listed
- [ ] Implementation approach determined

Why Checkpoints Matter

• Self-regulation: AI verifies it completed all steps
• Quality gate: Prevents rushing to next phase
• Debugging aid: Shows where process broke down
• Documentation: Records what was accomplished

---

Variable Substitution

Archon replaces variables in command text before sending to the AI. The most commonly used variables in commands:

Variable	Value
$ARGUMENTS / $USER_MESSAGE	User’s input message
$1, $2, $3	Positional arguments (direct invocation only)
$ARTIFACTS_DIR	Pre-created artifacts directory for this workflow run
$BASE_BRANCH	Base branch (auto-detected or configured)
$DOCS_DIR	Documentation directory path (default: docs/)
$WORKFLOW_ID	Unique workflow run ID
$CONTEXT	GitHub issue/PR context (if available)

See the Variable Reference for the complete list, including $LOOP_USER_INPUT, $REJECTION_REASON, node output references, substitution order, and context variable behavior.

Usage Pattern

Always show the input at the top:

# Investigate Issue

**Input**: $ARGUMENTS

---

## Your Mission
[...]

This ensures the AI knows exactly what it’s working with.

---

Artifact Conventions

Where Artifacts Live

Artifacts are stored outside the repository in the Archon workspace directory. Use the $ARTIFACTS_DIR variable to reference the pre-created artifacts directory for each workflow run:

~/.archon/workspaces/owner/repo/artifacts/runs/{workflow-id}/

This keeps artifacts out of git and avoids polluting the working tree.

Naming Conventions

Artifact Type	Path Pattern
Issue investigation	$ARTIFACTS_DIR/issues/issue-{number}.md
Free-form investigation	$ARTIFACTS_DIR/issues/investigation-{timestamp}.md
PR review scope	$ARTIFACTS_DIR/reviews/pr-{number}/scope.md
Code review findings	$ARTIFACTS_DIR/reviews/pr-{number}/code-review-findings.md

Instructing the AI to Save

Be explicit about artifact creation:

## Phase 4: GENERATE - Create Artifact

### 4.1 Create Directory

```bash
mkdir -p $ARTIFACTS_DIR/issues

4.2 Write Artifact

Write to $ARTIFACTS_DIR/issues/issue-{number}.md:

# Investigation: {Title}

**Issue**: #{number}
**Type**: {BUG|ENHANCEMENT}
...

CRITICAL: This artifact is the ONLY way to pass information to the next workflow step. Include everything needed for implementation:

• Exact file paths with line numbers
• Actual code snippets (not summaries)
• Step-by-step implementation instructions
• Validation commands
• Edge cases to handle

The implementing agent will work ONLY from this artifact.

---

## Writing Effective Instructions

### Be Explicit About Tools

Tell the AI which tools to use:

```markdown
### 2.1 Search for Relevant Code

Use Task tool with subagent_type="Explore":

Find all files related to authentication:

• Token handling
• Session management
• Login/logout flows

### 2.2 Check Git History

```bash
git log --oneline -10 -- {affected-file}
git blame -L {start},{end} {affected-file}

### Provide Decision Trees

Help the AI handle different scenarios:

```markdown
### 3.2 Handle Git State

┌─ IN WORKTREE? │ └─ YES → Use it (assume it’s for this work) │ ├─ ON MAIN BRANCH? │ └─ Clean? → Create branch: fix/issue-{number} │ └─ Dirty? → STOP, ask user to commit/stash │ └─ ON FEATURE BRANCH? └─ Use it (assume it’s for this work)

Include Error Handling

Tell the AI what to do when things go wrong:

## Handling Edge Cases

### Artifact not found

Artifact not found at $ARTIFACTS_DIR/issues/issue-{number}.md

Run /investigate-issue {number} first.

### Code has drifted

Code has changed since investigation:

File: src/x.ts:45

• Artifact expected: {snippet}
• Actual code: {different}

Options:

1. Re-run /investigate-issue
2. Proceed with manual adjustments

---

Success Criteria

End every command with clear success criteria:

## Success Criteria

- **ARTIFACT_COMPLETE**: All sections filled with specific content
- **EVIDENCE_BASED**: Every claim has file:line reference
- **IMPLEMENTABLE**: Next agent can execute without questions
- **COMMITTED**: Artifact saved in git

These serve as:

• Final checklist for the AI
• Definition of “done”
• Quality bar for the command

---

Template: Basic Command

---
description: Brief description of what this command does
argument-hint: <expected-input>
---

# Command Name

**Input**: $ARGUMENTS

---

## Your Mission

{1-2 sentences explaining the goal and what success looks like}

**Output artifact**: `$ARTIFACTS_DIR/{category}/{name}.md`

---

## Phase 1: LOAD - Gather Context

### 1.1 Parse Input

{Instructions for understanding the input}

### 1.2 Load Dependencies

{Instructions for loading required context}

**PHASE_1_CHECKPOINT:**
- [ ] Input parsed correctly
- [ ] Required context loaded

---

## Phase 2: PROCESS - Do the Work

### 2.1 Main Action

{Core instructions}

### 2.2 Secondary Action

{Supporting instructions}

**PHASE_2_CHECKPOINT:**
- [ ] Main work completed
- [ ] Results validated

---

## Phase 3: GENERATE - Create Artifact

### 3.1 Artifact Location

```bash
mkdir -p $ARTIFACTS_DIR/{category}

Path: $ARTIFACTS_DIR/{category}/{name}.md

3.2 Artifact Content

Write this structure:

# {Title}

**Created**: {timestamp}
**Input**: {original input}

## Summary

{Key findings/results}

## Details

{Comprehensive information for next step}

## Next Steps

{What the next agent should do with this}

CRITICAL: Include everything the next workflow step needs.

PHASE_3_CHECKPOINT:

• Artifact file created
• All sections populated
• Information is actionable

---

Phase 4: COMMIT - Save Work

git add .
git commit -m "{Descriptive message}"

PHASE_4_CHECKPOINT:

• Changes committed

---

Phase 5: REPORT - Output Results

## Complete

**Artifact**: `$ARTIFACTS_DIR/{category}/{name}.md`

### Summary

{Brief results}

### Next Step

Run `/{next-command}` to continue.

---

Success Criteria

• CONTEXT_LOADED: Required information gathered
• WORK_COMPLETE: Main task accomplished
• ARTIFACT_SAVED: Output written to correct location
• COMMITTED: Changes saved to git

---

## Anti-Patterns to Avoid

### 1. Vague Instructions

Bad:
```markdown
Analyze the code and find the problem.

Good:

### 2.1 Trace the Error Path

1. Find where the error originates using grep:
   ```bash
   grep -r "ErrorType" src/

2. Read the file and identify the function:

   cat src/handlers/error.ts

3. Document the call chain leading to the error.

### 2. Missing Artifact Instructions

Bad:
```markdown
## Results

Output your findings.

Good:

## Phase 4: GENERATE - Create Artifact

Write to `$ARTIFACTS_DIR/issues/issue-{number}.md`:

[Exact template with all required sections]

**CRITICAL**: This artifact is the handoff to the implementing agent.

3. No Error Handling

Bad:

Create the PR.

Good:

### Create PR

**First, check if PR already exists:**
```bash
gh pr list --head $(git branch --show-current)

If PR exists: Use existing PR, skip creation.

If no PR: Create new PR:

gh pr create --title "..." --body "..."

### 4. Assuming Context

Bad:
```markdown
Fix the bug in the file we discussed.

Good:

### 1.1 Load Artifact

```bash
cat $ARTIFACTS_DIR/issues/issue-{number}.md

Extract:

• File paths to modify
• Line numbers for changes
• Expected behavior

---

## Testing Your Command

1. **Run it manually**: `bun run cli workflow run {workflow} "test input"`
2. **Check artifact output**: Does it contain everything needed?
3. **Simulate next step**: Can another agent work from just the artifact?
4. **Edge cases**: What happens with bad input? Missing files?

---

## Summary

1. **Commands are prompts** - Write clear instructions for AI agents
2. **Artifacts are the handoff** - The ONLY way to pass data between steps
3. **Use phases** - Break work into verifiable chunks
4. **Be explicit** - Tell the AI exactly what to do, where, and how
5. **Include everything** - The next agent works ONLY from your artifact