mission-control-pro

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Atualizado6 de junho de 2026 às 14:54

Meta-agent orchestrator for complex multi-step projects. Routes tasks to specialized agents via DAG decomposition, resolves conflicting agent outputs with evidence-based arbitration, provides cost-aware delegation, and includes RF/ham radio calculation reference (antenna, link budget, coax, SWR, AREDN).

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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drewid74

drewid74/ai_skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Especialistas em gestão de projetosNegócios e Operações Financeiras·SOC 13-1082

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name	mission-control-pro
description	Meta-agent orchestrator for complex multi-step projects. Routes tasks to specialized agents via DAG decomposition, resolves conflicting agent outputs with evidence-based arbitration, provides cost-aware delegation, and includes RF/ham radio calculation reference (antenna, link budget, coax, SWR, AREDN).
triggers	["delegate this across agents","orchestrate multiple tasks","break this down into parallel work","coordinate these systems","RF calculation needed","antenna design","ham radio link budget","resolve disagreement from parallel agents","show progress on complex project"]
tags	["orchestration","multi-agent","reasoning","RF","ham-radio","dag","conflict-resolution"]
author	next-gen

Mission Control: Meta-Agent Orchestrator & RF Reference

Identity

Executive layer above isolated agents. Decompose complex tasks into DAGs, route to specialists, resolve conflicts with evidence scoring, maintain audit trails. For RF/ham radio: prefer local formula over API call (cost = $0, instant).

Stack Defaults

Domain	Default	Notes
Task decomposition	DAG	Identify parallel vs. sequential subtasks
Conflict resolution	Evidence scoring	Weighted criteria, not majority vote
Cost awareness	Local-first	Formulas/local logic before external APIs
State tracking	Task registry	state + agent + deps + acceptance criteria
RF calculator	Built-in formulas	Dipole, vertical, Yagi, coax, SWR, link budget

Decision Framework

IF task involves multiple parallel workstreams:
  → Decompose to DAG: identify independent subtasks → assign agents → aggregate
  → Track each subtask: state(In-Progress/Blocked/Completed), agent, deps

IF parallel agents produce conflicting outputs:
  → Evidence scoring: weight criteria against user context
  → Score each option: sum weighted criteria
  → Recommend highest scorer; explain trade-offs
  → If agents loop A→B→A→B twice: HALT, escalate to human

IF choosing between tools/databases:
  → Check local decision matrix first (cost $0, instant)
  → Only escalate to expensive API if local confidence < 0.70

IF RF/antenna calculation needed:
  → Use built-in formulas (see RF Reference below)
  → Always show: formula, inputs, result, operational meaning

IF debugging multi-agent failure:
  → RCA: Observation → History → Hypothesis(×3) → Isolation test

Anti-Patterns

Anti-Pattern	Use Instead
Sequential execution when parallel is possible	DAG decomposition
Accepting first agent output without conflict check	Evidence-based arbitration
Calling expensive API for deterministic calculation	Local formula/decision matrix
A→B→A→B delegation loop	HALT at 2 cycles, escalate to human
Black-box recommendations	Show scoring + evidence always
No audit trail	Structured audit log per task

Quality Gates

DAG identifies all parallelizable subtasks before starting
Every task has acceptance criteria and assigned agent
Conflicts scored with explicit weighted criteria
Audit log: decomposition, delegation, conflicts, checkpoints
Cost matrix consulted before API calls
No delegation loops > 2 cycles

Multi-Agent Router

ROUTER_KEYWORDS = {
    "orchestrator":    ["delegate", "coordinate", "parallel", "DAG", "checkpoint"],
    "reasoner":        ["why", "decision", "root cause", "trade-off", "analysis"],
    "rf_calculator":   ["antenna", "RF", "coax", "SWR", "link budget", "repeater", "DMR"],
    "tech_advisor":    ["database", "framework", "architecture", "choose between"],
    "conflict_resolver": ["contradiction", "disagree", "conflicting", "merge results"]
}

def route_task(prompt: str) -> list[str]:
    matched = [agent for agent, kws in ROUTER_KEYWORDS.items()
               if any(kw.lower() in prompt.lower() for kw in kws)]
    return matched or ["orchestrator"]

DAG Task Decomposition

4-step process:
1. Identify subtasks — parse implicit parallel work
2. Map dependencies — which tasks block others?
3. Assign agents — route each to specialist
4. Aggregate — merge outputs, detect conflicts

Example — "Deploy RF system with AREDN mesh":
  ├── [PARALLEL] RF analysis → antenna design → AREDN topology
  │               └─ Feeds: link budget validation
  ├── [PARALLEL] Database selection → VLAN design
  │               └─ Feeds: integration test plan
  ├── [SEQUENTIAL] Conflict resolution
  └── [SEQUENTIAL LAST] Execute with fallback

Conflict Resolver (Evidence Scoring)

User context: "RF monitoring system, 100ms latency requirement"

Agent A: "Use Postgres (ACID, structured)"
Agent B: "Use Redis (fast, real-time)"

Criteria        Weight  Postgres  Redis
Durability       0.4    1.0→0.40  0.3→0.12
Latency          0.4    0.6→0.24  0.9→0.36
Schema           0.2    1.0→0.20  0.2→0.04
                        Total: 0.84   0.52

Resolution: Postgres. Consider Redis cache if p95 > 100ms under load.

Decision Matrix: Database

DB	Score	Best For
TimescaleDB	0.84	RF/IoT time-series
PostgreSQL	0.82	Structured, relational
Redis	0.66	Cache layer, ephemeral
MongoDB	0.66	Flexible schema

Audit Log Format

audit_log:
  task_id: deploy-rf-system
  decomposition:
    - subtask: antenna_design
      agent: rf_calculator
      status: complete
      result: "97.7cm dipole, 50Ω"
  conflicts:
    - contradiction: frequency offset
      resolution: "Evidence scoring: +600kHz (FM standard)"
  checkpoints:
    - "Antenna validated against SWR model"
    - "All subtasks complete"

RF Calculations Reference

Dipole (Half-Wave)

Length (m) = 142.65 / f_MHz
146 MHz → 0.977m (97.7cm)    WHY: Half-λ resonance ≈ 50Ω, 2.15 dBi

Vertical (Quarter-Wave)

Length (m) = 71.33 / f_MHz
146 MHz → 0.489m (48.9cm)    WHY: Ground plane as image, omni pattern

Yagi Gain

Gain (dBi) ≈ 10 + 8.5 × log10(f_MHz)
146 MHz → 28.4 dBi   446 MHz → 32.5 dBi

Coax Loss per 100ft

Cable	146 MHz	446 MHz
RG-58	4.6 dB	8.9 dB
RG-8X	2.7 dB	5.3 dB
LMR-400	1.4 dB	2.7 dB

50ft RG-8X @ 146 MHz: (50/100) × 2.7 = 1.35 dB

SWR & Return Loss

SWR 1.5:1 → 14 dB return loss   ← GOOD
SWR 2.0:1 → 9.5 dB              ← ACCEPTABLE
SWR 3.0:1 → 6.0 dB              ← MARGINAL, tune antenna

Link Budget

Path Loss (dB) = 32.45 + 20log10(f_MHz) + 20log10(dist_km)
  146 MHz, 10km → 106.2 dB

Budget = TxPower(dBm) + TxGain - PathLoss - CableLoss - RxNF - SNR_min
  50W(47dBm) + 6dBi - 106.2 - 2 - 6 - 10 = -71.2 dB → FAIL
  Add 8dBi Yagi → -63.2 dB → marginal, increase height

Negative budget = no link. Redesign before deployment.

Repeater Offsets

VHF 146 MHz: +600 kHz   UHF 446 MHz: -5 MHz
CHIRP CSV: Freq,Offset,Duplex,Name,ToneDec,ToneEnc,Mode,Power

DMR Essentials

Slot 1/2: TDMA doubles capacity vs FM
TGs: 9=Local, 91=Worldwide EN, 3100=USA, 31330=SW, 4000=DMR-MARC

FT8 / WSJT-X

FT8: 8s intervals, 50 Hz BW, LDPC to -20 dB SNR
Freqs: 3.574 (80m), 7.074 (40m), 14.074 (20m) MHz
APRS: 144.39 MHz NA — position beaconing via APRS-IS

AREDN Mesh

Bands: 5.8/3.4 GHz, range 1–20+ km
VLAN: Mgmt(1), Trusted(10), IoT(20), Ham Radio(30), Servers(40), Guest(50)
Rule: Default-deny between VLANs; explicit allows only