mission-control-pro

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

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

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

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

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