harness-24h

name

harness-24h

description

A production-grade framework for long-running, autonomous agents based on Harness Engineering principles. Features three-layer self-healing memory, circuit breaker protection, KAIROS dream-mode consolidation, and multi-agent coordination. When a user describes a task idea, Agent automatically initializes the workspace, fills all template files, sets up the cron schedule, and begins execution immediately.

Harness-24h | 24-hour Harness Framework

This skill provides a complete framework for executing long-running, autonomous tasks based on the principles of Harness Engineering.

Key principle: The user only describes their task idea. Agent handles everything else — workspace creation, filling all files, setting up the cron schedule, and starting execution.

Core Concepts

The framework is built around 10 core components of Harness Engineering:

1. Machine-verifiable contracts: mission.md and eval_criteria.md
2. Knowledge as system of record: playbook.md
3. Agent senses and effectors: Configured via harness_boot.py
4. Solving long-term amnesia: Three-Layer Memory Architecture (see below)
5. Externalized validation loop: harness_eval.py
6. Mechanized constraints and entropy control: harness_cleanup.py
7. Three-Layer Self-Healing Memory: Pointer index + topic files + grep-only stream
8. Circuit Breaker Protection: Auto-block after N consecutive failures
9. KAIROS Dream Mode: Offline memory consolidation during idle periods
10. Multi-Agent Coordination: File-system IPC for parallel subtask execution

For detailed architecture, read references/architecture.md. For common anti-patterns to avoid, read references/anti-patterns.md.

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Three-Layer Memory Architecture

Layer	File(s)	Purpose	Access Pattern
L1	heartbeat.md	Compact pointer index (< 2KB)	Always in context
L2	knowledge/*.md	Topic-specific knowledge files	On-demand loading
L3	logs/execution_stream.log	Append-only raw execution log	Grep-only, never read fully

Strict Write Discipline: Only update L1 pointers AFTER harness_eval.py confirms success.

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Workflow A: User Describes a New Task (FULLY AUTOMATIC)

When a user describes a task idea, follow ALL steps below without asking the user to fill in any files manually.

Step A1: Decide Workspace Path

Choose a workspace path based on the task name. Convention:

~/.openclaw/workspace/harness/{task-slug}/

Step A2: Initialize Workspace

python3 {skill_dir}/scripts/harness_boot.py /path/to/workspace --init

This copies all template files and creates knowledge/, logs/, and output/ directories.

Step A3: Fill ALL Template Files Yourself

Based on the user's task description, Agent must write the following files completely. Do NOT leave any placeholder text.

File 1 — mission.md: Write the task contract with machine-checkable completion criteria.

File 2 — playbook.md: Write the step-by-step execution guide with explicit tools, URLs, and failure handling.

File 3 — eval_criteria.md: Write the external validation rules with Python code snippets.

File 4 — cron_config.md: Write the scheduling config with cron expression and prompt template.

File 5 — heartbeat.md: Write the initial Layer 1 pointer index.

IMPORTANT: Write to {workspace}/heartbeat.md (workspace root). Use the three-layer template format with System Status, Execution Pointer, Knowledge Index, and Active Alerts sections. Initial state: idle, Step 0, Circuit Breaker off.

File 6 — progress.md: Write the initial progress log at {workspace}/progress.md.

Step A4: Create Output Directory Structure

mkdir -p /path/to/workspace/output
mkdir -p /path/to/workspace/knowledge
mkdir -p /path/to/workspace/logs

Step A5: Verify Workspace is Ready

python3 {skill_dir}/scripts/harness_boot.py /path/to/workspace

The output must say "Workspace is ready". If it shows issues, fix them.

Step A6: Set Up Cron Schedules

Primary Schedule (Main Task Execution):

python3 {skill_dir}/scripts/harness_setup_cron.py /path/to/workspace

Read the output and use the schedule tool with those parameters.

Secondary Schedule (KAIROS Dream Mode — REQUIRED):

Dream mode MUST be scheduled separately to run during off-hours (e.g., 3 AM daily). This is NOT optional — it prevents memory bloat and knowledge fragmentation.

Use the schedule tool with these parameters:

name: {task-name}-dream
type: cron
cron: 0 0 3 * * *  (daily at 3 AM, adjust timezone as needed)
repeat: true
prompt: |
  Run KAIROS dream mode memory consolidation for the harness task at /path/to/workspace.
  
  Execute:
  python3 {skill_dir}/scripts/harness_dream.py /path/to/workspace
  
  This consolidates fragmented knowledge/*.md files, prunes stale Layer 1 pointers,
  extracts patterns from logs/execution_stream.log, and updates playbook.md with
  distilled best practices. Only runs when the main task is idle.

Step A7: Execute the First Run Immediately

Do NOT wait for the first cron trigger. Begin executing using Workflow B below.

---

Workflow B: Executing a Task (Cron Trigger)

Step B1: Boot and Check Status

python3 {skill_dir}/scripts/harness_boot.py /path/to/workspace

Read the output carefully:

• If mission_complete → Stop.
• If blocked or Circuit Breaker tripped → Stop. Wait for human intervention.
• If stuck detected (status still running) → Boot will auto-recover.
• Otherwise → Proceed.

Step B2: Mark Start

python3 {skill_dir}/scripts/harness_heartbeat.py /path/to/workspace start

Step B3: Read Context (Cache-Aware Order)

Read in this order to maximize prompt cache hits (static content first):

1. {workspace}/mission.md — goal and constraints (rarely changes)
2. {workspace}/eval_criteria.md — validation rules (rarely changes)
3. {workspace}/playbook.md — execution steps (semi-static)
4. {workspace}/heartbeat.md — current state pointer (changes each run)
5. Only load knowledge/*.md topic files relevant to the current step

How to find relevant knowledge topics:

# List all registered topics
python3 {skill_dir}/scripts/harness_memory.py /path/to/workspace topics

# Search execution stream for keywords
python3 {skill_dir}/scripts/harness_memory.py /path/to/workspace search --query "timeout"

Then read only the knowledge/{topic}.md files mentioned in heartbeat.md's Knowledge Index section.

Step B4: Execute from Breakpoint

Perform the steps defined in playbook.md, starting from the step recorded in heartbeat.md.

During execution, actively use the memory system:

Log significant events (use liberally — this feeds KAIROS dream mode):

python3 {skill_dir}/scripts/harness_memory.py /path/to/workspace stream \
  --step "Step 2.3" --event "Fetched 50 articles from API, took 12s"

python3 {skill_dir}/scripts/harness_memory.py /path/to/workspace stream \
  --step "Step 3.1" --event "Rate limit hit, waiting 60s" --level WARN

python3 {skill_dir}/scripts/harness_memory.py /path/to/workspace stream \
  --step "Step 4" --event "Validation failed: missing required field 'author'" --level ERROR

Save reusable knowledge (when you discover a pattern or workaround):

python3 {skill_dir}/scripts/harness_memory.py /path/to/workspace learn \
  --topic "api_rate_limits" \
  --insight "GitHub API returns 403 after 5000 requests/hour. Wait 60s and retry with exponential backoff."

python3 {skill_dir}/scripts/harness_memory.py /path/to/workspace learn \
  --topic "data_validation" \
  --insight "Articles from source X often missing 'author' field. Use 'Unknown' as default and log warning."

This creates/updates knowledge/api_rate_limits.md and registers the pointer in heartbeat.md.

Step B5: Mark Completion or Failure

If succeeded:

python3 {skill_dir}/scripts/harness_heartbeat.py /path/to/workspace done \
  --step "Step 4" --summary "Processed 50 articles, 3 validation warnings"

If failed:

python3 {skill_dir}/scripts/harness_heartbeat.py /path/to/workspace fail \
  --step "Step 3" --error "API timeout after 3 retries"

Step B6: External Validation

python3 {skill_dir}/scripts/harness_eval.py /path/to/workspace

Step B7: Entropy Control (Every 10 Runs)

python3 {skill_dir}/scripts/harness_cleanup.py /path/to/workspace

This auto-compacts progress.md if it exceeds 2000 lines, archives old execution stream logs, and checks memory integrity.

Step B8: Mission Complete Check

If all completion criteria met:

python3 {skill_dir}/scripts/harness_heartbeat.py /path/to/workspace mission_complete

---

Workflow C: KAIROS Dream Mode (Scheduled Off-Hours)

When to run: Automatically via the secondary cron schedule set up in Step A6. Typically runs once daily at 3 AM.

What it does:

1. Reads logs/execution_stream.log from the last 24 hours
2. Extracts recurring patterns (errors, successes, warnings)
3. Consolidates fragmented knowledge/*.md files
4. Prunes stale Layer 1 pointers from heartbeat.md
5. Updates playbook.md with distilled best practices
6. Writes a human-readable journal to logs/dream_journal.md

Manual invocation (for testing or immediate consolidation):

# Full dream cycle
python3 {skill_dir}/scripts/harness_dream.py /path/to/workspace

# Dry run (show what would be done without making changes)
python3 {skill_dir}/scripts/harness_dream.py /path/to/workspace --dry-run

# Only consolidate knowledge, skip playbook update
python3 {skill_dir}/scripts/harness_dream.py /path/to/workspace --skip-playbook

CRITICAL: Dream mode should ONLY run when the main task is idle (status != running). The scheduled prompt in Step A6 handles this check automatically.

---

Workflow D: Multi-Agent Parallel Execution (Optional)

When to use: When a single step in playbook.md involves processing multiple independent items (e.g., "Scrape 100 product pages", "Analyze 50 PDF documents").

When NOT to use: For sequential steps or when items depend on each other.

Step D1: Initialize Agents

python3 {skill_dir}/scripts/harness_coordinator.py /path/to/workspace init --agents 3

This creates agents/worker-1/, agents/worker-2/, agents/worker-3/, each with inbox/, outbox/, and manifest.json.

Step D2: Dispatch Subtasks

For each independent item, dispatch a subtask:

python3 {skill_dir}/scripts/harness_coordinator.py /path/to/workspace dispatch \
  --task "Scrape product page: https://example.com/product/123" \
  --step "Step 2.1"

python3 {skill_dir}/scripts/harness_coordinator.py /path/to/workspace dispatch \
  --task "Scrape product page: https://example.com/product/456" \
  --step "Step 2.1"

python3 {skill_dir}/scripts/harness_coordinator.py /path/to/workspace dispatch \
  --task "Scrape product page: https://example.com/product/789" \
  --step "Step 2.1"

Each task is written as an XML-like Markdown file to an available worker's inbox/.

Step D3: Execute Worker Tasks

Important: The coordinator does NOT auto-execute. You must manually process each worker's inbox.

For each worker (1, 2, 3):

# Check if worker has tasks
ls /path/to/workspace/agents/worker-1/inbox/

# Read the task file
cat /path/to/workspace/agents/worker-1/inbox/task_abc123.md

# Execute the task according to its instructions
# ... (your actual scraping/processing logic here)

# Write result to outbox
cat > /path/to/workspace/agents/worker-1/outbox/result_abc123.md << 'EOF'
## Result: task_abc123

**Status**: success

**Output**:
Product title: Example Product
Price: $29.99
Stock: In stock
EOF

Step D4: Monitor and Collect

Check status:

python3 {skill_dir}/scripts/harness_coordinator.py /path/to/workspace status

Collect all results from worker outboxes:

python3 {skill_dir}/scripts/harness_coordinator.py /path/to/workspace collect

This reads all outbox/result_*.md files, updates worker manifests back to idle, and prints a summary.

Step D5: Cleanup

Archive all inbox/outbox files to logs/agent_archive/:

python3 {skill_dir}/scripts/harness_coordinator.py /path/to/workspace cleanup

---

When to Use What

Scenario	Tool	Timing
Log a significant event during execution	harness_memory.py stream	Every time something notable happens
Discover a reusable pattern or workaround	harness_memory.py learn	When you solve a problem that might recur
Search past execution logs for debugging	harness_memory.py search	When investigating errors or stuck states
Consolidate fragmented knowledge	harness_dream.py	Scheduled off-hours (e.g., 3 AM daily)
Process multiple independent items	harness_coordinator.py	Only when parallelization is beneficial
Check memory integrity	harness_memory.py check	After manual edits to knowledge files

---

Critical Rules (NEVER Violate)

1. heartbeat.md and progress.md always live at {workspace}/ root — not any subdirectory.
2. Never leave placeholder text in any file — overwrite with real content before running.
3. Never self-certify completion — always run harness_eval.py as the external validator.
4. Never skip reading heartbeat.md — always resume from the recorded breakpoint.
5. Never modify mission.md during execution — it is the user's constitution, read-only.
6. Always execute the first run immediately after setup.
7. Respect the circuit breaker — if tripped, do NOT attempt to bypass it.
8. Log to execution stream liberally — use harness_memory.py stream for all significant events.
9. Distill, don't dump — knowledge files should contain insights, not raw logs.
10. Read static content first — mission.md and eval_criteria.md before dynamic state files.
11. Always schedule KAIROS dream mode — it is NOT optional, memory will bloat without it.
12. Never run dream mode while task is running — only during idle periods.
13. Coordinator does not auto-execute — you must manually process worker inboxes.