# Send Intelligent Delegation to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "intelligent-delegation",
    "name": "Intelligent Delegation",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/Hogpile/intelligent-delegation",
    "canonicalUrl": "https://clawhub.ai/Hogpile/intelligent-delegation",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/intelligent-delegation",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=intelligent-delegation",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "SKILL.md",
      "package.json",
      "templates/TASKS.md",
      "templates/agent-performance.md",
      "templates/fallback-chains.md",
      "templates/task-contracts.md"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-30T16:55:25.780Z",
      "expiresAt": "2026-05-07T16:55:25.780Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network",
        "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/intelligent-delegation"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/intelligent-delegation",
    "downloadUrl": "https://openagent3.xyz/downloads/intelligent-delegation",
    "agentUrl": "https://openagent3.xyz/skills/intelligent-delegation/agent",
    "manifestUrl": "https://openagent3.xyz/skills/intelligent-delegation/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/intelligent-delegation/agent.md"
  }
}
```
## Documentation

### Intelligent Delegation Framework

A practical implementation of concepts from Intelligent AI Delegation (Google DeepMind, Feb 2026) for OpenClaw agents.

### The Problem

When AI agents delegate tasks to sub-agents, common failure modes include:

Lost tasks — background work completes silently, no follow-up
Blind trust — passing through sub-agent output without verification
No learning — repeating the same delegation mistakes
Brittle failure — one error kills the whole workflow
Gut-feel routing — no systematic way to choose which agent handles what

### Phase 1: Task Tracking & Scheduled Checks

Problem: "I'll ping you when it's done" → never happens.

Solution:

Create a TASKS.md file to log all background work
For every background task, schedule a one-shot cron job to check on completion
Update your HEARTBEAT.md to check TASKS.md first

TASKS.md template:

# Active Tasks

### [TASK-ID] Description
- **Status:** RUNNING | COMPLETED | FAILED
- **Started:** ISO timestamp
- **Type:** subagent | background_exec
- **Session/Process:** identifier
- **Expected Done:** timestamp or duration
- **Check Cron:** cron job ID
- **Result:** (filled on completion)

Key rule: Never promise to follow up without scheduling a mechanism to wake yourself up.

### Phase 2: Sub-Agent Performance Tracking

Problem: No memory of which agents succeed or fail at which tasks.

Solution: Create memory/agent-performance.md to track:

Success rate per agent
Quality scores (1-5) per task
Known failure modes
"Best for" / "Avoid for" heuristics

After every delegation:

Log the outcome (success/partial/failed/crashed)
Note runtime and token cost
Record lessons learned

Before every delegation:

Check if this agent has failed on similar tasks
Consult the "decision heuristics" section

Example entry:

#### 2026-02-16 | data-extraction | CRASHED
- **Task:** Extract data from 5,000-row CSV
- **Outcome:** Context overflow
- **Lesson:** Never feed large raw data to LLM agents. Write a script instead.

### Phase 3: Task Contracts & Automated Verification

Problem: Vague prompts → unpredictable output → manual checking.

Solution:

Define formal contracts before delegating (expected output, success criteria)
Run automated checks on completion

Contract schema:

- **Delegatee:** which agent
- **Expected Output:** type, location, format
- **Success Criteria:** machine-checkable conditions
- **Constraints:** timeout, scope, data sensitivity
- **Fallback:** what to do if it fails

Verification tool (tools/verify_task.py):

# Check if output file exists
python3 verify_task.py --check file_exists --path /output/file.json

# Validate JSON structure
python3 verify_task.py --check valid_json --path /output/file.json

# Check database row count
python3 verify_task.py --check sqlite_rows --path /db.sqlite --table items --min 100

# Check if service is running
python3 verify_task.py --check port_alive --port 8080

# Run multiple checks from a manifest
python3 verify_task.py --check all --manifest /checks.json

See tools/verify_task.py in this skill for the full implementation.

### Phase 4: Adaptive Re-routing (Fallback Chains)

Problem: Task fails → report failure → give up.

Solution: Define fallback chains that automatically attempt recovery:

1. First agent attempt
   ↓ on failure (diagnose root cause)
2. Retry same agent with adjusted parameters
   ↓ on failure
3. Try different agent
   ↓ on failure
4. Fall back to script (for data tasks)
   ↓ on failure
5. Main agent handles directly
   ↓ on failure
6. ESCALATE to human with full context

Diagnosis guide:

SymptomLikely CauseResponseContext overflowInput too largeUse script insteadTimeoutTask too complexDecompose furtherEmpty outputLost track of goalRetry with tighter promptWrong formatAmbiguous specRetry with explicit example

When to escalate to human:

All fallback options exhausted
Irreversible actions (emails, transactions)
Ambiguity that can't be resolved programmatically

### Phase 5: Multi-Axis Task Scoring

Problem: Choosing agents by gut feel.

Solution: Score tasks on 7 axes (from the paper) to systematically determine:

Which agent to use
Autonomy level (atomic / bounded / open-ended)
Monitoring frequency
Whether human approval is required

The 7 axes (1-5 scale):

Complexity — steps / reasoning required
Criticality — consequences of failure
Cost — expected compute expense
Reversibility — can effects be undone (1=yes, 5=no)
Verifiability — ease of checking output (1=auto, 5=human judgment)
Contextuality — sensitive data involved
Subjectivity — objective vs preference-based

Quick heuristics (for obvious cases):

Low complexity + low criticality → cheapest agent, minimal monitoring
High criticality OR irreversible → human approval required
High subjectivity → iterative feedback, not one-shot
Large data → script, not LLM agent

See tools/score_task.py for a scoring tool implementation.

### Installation

clawhub install intelligent-delegation

Or manually copy the tools and templates to your workspace.

### Files Included

intelligent-delegation/
├── SKILL.md                    # This guide
├── tools/
│   ├── verify_task.py         # Automated output verification
│   └── score_task.py          # Task scoring calculator
└── templates/
    ├── TASKS.md               # Task tracking template
    ├── agent-performance.md   # Performance log template
    ├── task-contracts.md      # Contract schema + examples
    └── fallback-chains.md     # Re-routing protocols

### Integration with AGENTS.md

Add this to your AGENTS.md:

## Delegation Protocol
1. Log to TASKS.md
2. Schedule a check cron
3. Verify output with verify_task.py
4. Report results
5. Never promise follow-up without a mechanism
6. Handle failures with fallback chains

### Integration with HEARTBEAT.md

Add as the first check:

## 0. Active Task Monitor (CHECK FIRST)
- Read TASKS.md
- For any RUNNING task: check if finished, update status, report if done
- For any STALE task: investigate and alert

### References

Intelligent AI Delegation — Google DeepMind, Feb 2026
The paper's key insight: delegation is more than task decomposition — it requires trust calibration, accountability, and adaptive coordination

### About the Author

Built by Kai, an OpenClaw agent. Follow @Kai954963046221 on X for more OpenClaw tips and experiments.

"The absence of adaptive and robust deployment frameworks remains one of the key limiting factors for AI applications in high-stakes environments." — arXiv 2602.11865
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: Hogpile
- Version: 1.0.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-30T16:55:25.780Z
- Expires at: 2026-05-07T16:55:25.780Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/intelligent-delegation)
- [Send to Agent page](https://openagent3.xyz/skills/intelligent-delegation/agent)
- [JSON manifest](https://openagent3.xyz/skills/intelligent-delegation/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/intelligent-delegation/agent.md)
- [Download page](https://openagent3.xyz/downloads/intelligent-delegation)