Multi Agent Task Orchestrator V2

Multi-Agent Task Orchestrator

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/multi-agent-task-orchestrator from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Multi-Agent Task Orchestrator

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: How It Works, Common Pitfalls, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when you have 3+ specialized agents that need to coordinate on complex tasks
• Use when agents are doing duplicate or conflicting work
• Use when you need audit trails showing who did what and when
• Use when agent output quality is inconsistent and needs verification gates
• Use when the request clearly matches the imported source intent: Route tasks to specialized AI agents with anti-duplication, quality gates, and 30-minute heartbeat monitoring.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

A production-tested pattern for coordinating multiple AI agents through a single orchestrator. Instead of letting agents work independently (and conflict), one orchestrator decomposes tasks, routes them to specialists, prevents duplicate work, and verifies results before marking anything done. Battle-tested across 10,000+ tasks over 6 months.

Imported: How It Works

Step 1: Define the Orchestrator Identity

The orchestrator must know what it IS and what it IS NOT. This prevents it from doing work instead of delegating:

You are the Task Orchestrator. You NEVER do specialized work yourself.
You decompose tasks, delegate to the right agent, prevent conflicts,
and verify quality before marking anything done.

WHAT YOU ARE NOT:
- NOT a code writer — delegate to code agents
- NOT a researcher — delegate to research agents
- NOT a tester — delegate to test agents

This "NOT-block" pattern reduces task drift by ~35% in production.

Step 2: Build a Task Registry

Before assigning work, check if anyone is already doing this task:

import sqlite3
from difflib import SequenceMatcher

def check_duplicate(description, threshold=0.55):
    conn = sqlite3.connect("task_registry.db")
    c = conn.cursor()
    c.execute("SELECT id, description, agent, status FROM tasks WHERE status IN ('pending', 'in_progress')")
    for row in c.fetchall():
        ratio = SequenceMatcher(None, description.lower(), row[1].lower()).ratio()
        if ratio >= threshold:
            return {"id": row[0], "description": row[1], "agent": row[2]}
    return None

Step 3: Route Tasks to Specialists

Use keyword scoring to match tasks to the best agent:

AGENTS = {
    "code-architect": ["code", "implement", "function", "bug", "fix", "refactor", "api"],
    "security-reviewer": ["security", "vulnerability", "audit", "cve", "injection"],
    "researcher": ["research", "compare", "analyze", "benchmark", "evaluate"],
    "doc-writer": ["document", "readme", "explain", "tutorial", "guide"],
    "test-engineer": ["test", "coverage", "unittest", "pytest", "spec"],
}

def route_task(description):
    scores = {}
    for agent, keywords in AGENTS.items():
        scores[agent] = sum(1 for kw in keywords if kw in description.lower())
    return max(scores, key=scores.get) if max(scores.values()) > 0 else "code-architect"

Step 4: Enforce Quality Gates

Agent output is a CLAIM. Test output is EVIDENCE.

After agent reports completion:
1. Were files actually modified? (git diff --stat)
2. Do tests pass? (npm test / pytest)
3. Were secrets introduced? (grep for API keys, tokens)
4. Did the build succeed? (npm run build)
5. Were only intended files touched? (scope check)

Mark done ONLY after ALL checks pass.

Step 5: Run 30-Minute Heartbeats

Every 30 minutes, ask:
1. "What have I DELEGATED in the last 30 minutes?"
2. If nothing → open the task backlog and assign the next task
3. Check for idle agents (no message in >30min on assigned task)
4. Relance idle agents or reassign their tasks

Examples

Example 1: Ask for the upstream workflow directly

Use @multi-agent-task-orchestrator-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @multi-agent-task-orchestrator-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @multi-agent-task-orchestrator-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @multi-agent-task-orchestrator-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

Example 1: Delegating a Code Task

[ORCHESTRATOR -> code-architect] TASK: Add rate limiting to /api/users
SCOPE: src/middleware/rate-limit.ts only
VERIFICATION: npm test -- --grep "rate-limit"
DEADLINE: 30 minutes

Example 2: Handling a Duplicate

User asks: "Fix the login bug"
Registry check: Task #47 "Fix authentication bug" is IN_PROGRESS by security-reviewer
Decision: SKIP — similar task already assigned (78% match)
Action: Notify user of existing task, wait for completion

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Always define NOT-blocks for every agent (what they must refuse to do)
• Use SQLite for the task registry (lightweight, no server needed)
• Set similarity threshold at 55% for anti-duplication (lower = too many false positives)
• Require evidence-based quality gates (not just agent claims)
• Log every delegation with: task ID, agent, scope, deadline, verification command
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.

Imported Operating Notes

Imported: Best Practices

• Always define NOT-blocks for every agent (what they must refuse to do)
• Use SQLite for the task registry (lightweight, no server needed)
• Set similarity threshold at 55% for anti-duplication (lower = too many false positives)
• Require evidence-based quality gates (not just agent claims)
• Log every delegation with: task ID, agent, scope, deadline, verification command

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/multi-agent-task-orchestrator, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Common Pitfalls

• Problem: Orchestrator starts doing work instead of delegating Solution: Add explicit NOT-blocks and role boundaries

• Problem: Two agents modify the same file simultaneously Solution: Task registry with file-level locking and queue system

• Problem: Agent claims "done" without actual changes Solution: Quality gate checks git diff before accepting completion

• Problem: Tasks pile up without progress Solution: 30-minute heartbeat catches stale assignments and reassigns

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.