CONDUCTOR v2.1 — Automatic Skill Orchestration Layer (Premium)

Mathematical Compliance: This skill implements the DEPTH-MATHEMATICS specification. It computes and outputs the final ADS, CEI, SI, EC, OV, and Depth Report. It validates interference matrix, enforces plugin architecture, integrates meta-learning updates, and optimizes skill selection for maximum ADS per CEI.

You have 26 cognitive skills. Each forces deeper reasoning in a specific dimension. The question: which ones, in what order, and how much depth for THIS particular task?

This skill automatically answers that. It matches cognitive investment to task consequence — proportional depth, not maximum depth. It automatically selects skills, sequences them correctly, validates they actually interfere, manages plugins, and integrates meta-learning for compounding intelligence.

The Failure Mode You Must Recognize

Without orchestration, you either:

• Use zero skills (shallow — surface response ships unchallenged)
• Use all skills on every task (slow — over-investing on low-stakes work)
• Use the wrong skills (mismatched — deep temporal analysis on a CSS color choice)
• Compose skills that don't actually interfere (cargo cult composition)
• Ignore plugin architecture (lock-in to current paradigm)
• Miss meta-learning opportunities (no compounding intelligence)

The right depth is proportional. A CSS fix needs zero skills. A database schema needs five. This skill automatically determines the right number, the right skills, the right sequence, and verifies they actually work together.

The Protocol

0 — PARAMETERIZE: Read Thinking Parameters

Read thinking_parameters:

• risk_aware_activation (default true) — only activate skills where marginal epistemic risk reduction > cost
• require_interference (default true) — validate interference matrix before delivery
• require_recursive_audit (default true) — final recursive self-audit
• min_cim (default 0.5) — minimum Compositional Independence Metric for DIVERGE paths
• plugin_architecture (default true) — use plugin registry for skill loading
• meta_learning_enabled (default true) — apply meta-learning policy updates
• auto_skill_selection (default true) — automatically select skills based on task profile
• auto_sequencing (default true) — automatically sequence skills with interference validation
• boundary_detection_first (default true) — run BOUNDARY-DETECTOR first for novel domains

Step 1 — AUTO-CLASSIFY THE TASK (Automatic)

AUTOMATICALLY analyze the prompt and produce:

TASK PROFILE
────────────────────────────────────────
Task:           [one sentence description — auto-extracted]
Complexity:     [low / moderate / high / extreme] — auto-scored
Consequence:    [trivial / moderate / significant / critical] — auto-scored
Reversibility:  [trivially / moderately / hard / permanent] — auto-scored
Novelty:        [routine / somewhat novel / highly novel / unprecedented] — auto-scored
Breadth:        [single-domain / cross-domain / full-stack] — auto-scored
Stakeholders:   [count and types] — auto-extracted
Time Pressure:  [none / low / moderate / high / extreme] — auto-scored
Domain Tags:    [architecture, debugging, decision, product, security, etc.] — auto-tagged
────────────────────────────────────────

Auto-Scoring Rules:

• Complexity: Count of distinct technical domains + interdependencies
• Consequence: Blast radius × irreversibility × stakeholder count
• Reversibility: Time to rollback × coordination needed × data risk
• Novelty: Distance from known patterns in training (0=routine, 1=unprecedented)
• Breadth: Number of distinct skill categories required

Artifact: The task profile. All subsequent steps use this.

Step 2 — AUTO-ASSIGN DEPTH BUDGET (Risk-Aware, Automatic)

AUTOMATICALLY compute budget from task profile:

DEPTH BUDGET
────────────────────────────────────────
Budget Score = (Complexity × 0.3) + (Consequence × 0.3) + (1-Reversibility × 0.2) + (Novelty × 0.2)

BUDGET A (Score < 0.25): Low stakes, reversible, routine
  Skills: 0. Standard answer. Move fast.
  ADS Target: 0.20
  Skip remaining steps. Deliver.

BUDGET B (0.25 ≤ Score < 0.5): Moderate stakes, some novelty
  Skills: 1-2. Auto-pick most relevant.
  ADS Target: 0.45
  Time: one additional pass.

BUDGET C (0.5 ≤ Score < 0.75): High stakes, hard to reverse
  Skills: 3-4. Thinking + challenging + checking.
  ADS Target: 0.70
  Time: multiple passes.

BUDGET D (Score ≥ 0.75): Critical, permanent, unprecedented
  Skills: Full stack. Every relevant skill in sequence.
  ADS Target: 0.85
  Time: thorough multi-pass analysis.

This task: BUDGET [A/B/C/D] (Score: X.XX)
Reasoning: [auto-generated from profile scores]
ADS Target: [value]
CEI Budget: [computed]
────────────────────────────────────────

Risk-Aware Activation (Automatic): For each candidate skill:

Marginal_ADS_Gain = ADS_with_skill - ADS_without_skill
Marginal_CEI_Cost = CEI_with_skill - CEI_without_skill
Activate if: Marginal_ADS_Gain / Marginal_CEI_Cost > Risk_Threshold

Risk_Threshold: Budget B=0.5, Budget C=0.3, Budget D=0.2.

Artifact: Depth budget with auto-activation decisions.

Step 3 — AUTO-SELECT SKILLS (Plugin-Aware, Automatic)

AUTOMATICALLY select skills using the Plugin Registry and task profile:

SKILL SELECTION (AUTOMATIC)
────────────────────────────────────────
Task Profile → Skill Mapping (Auto-Applied):

[Complexity ≥ high]                    → DEEP-THINK (core)
[Consequence ≥ significant]            → ADVERSARY + PROVENANCE (core)
[Reversibility ≤ hard]                 → THRESHOLD (core)
[Multiple approaches possible]         → DIVERGE (core)
[Novelty ≥ highly novel]               → DESCEND (core)
[Stuck / no good solution]             → INVERT + REFRAME (core)
[Multi-part / long plan]               → CONTRADICT (core)
[Summarizing complex analysis]         → FIDELITY (core)
[Long execution / multi-step]          → ANCHOR (core)
[Confident but feels easy]             → EXCAVATE + INVERT (core)
[System with interacting parts]        → EMERGENCE (core)
[Future-affecting decision]            → TEMPORAL (core)
[Completeness check needed]            → NEGATIVE-SPACE (core)
[Ambiguous request]                    → CLARIFY (core)
[Low stakes / quick answer]            → SHALLOW (core)
[Teaching / explanation needed]        → TEACH (core)
[Novel domain / high uncertainty]      → BOUNDARY-DETECTOR (premium)
[Post-execution learning]              → META-LEARNING (premium)
[Final validation required]            → VERIFICATION-GATES (premium)

Domain Tags → Additional Skills:
[architecture]        → SYSTEM-ARCHITECT
[product]             → PRODUCT-ENGINEER
[api]                 → API-DESIGNER
[mobile]              → MOBILE-ENGINEER
[performance]         → PERFORMANCE-ENGINEER
[refactoring]         → REFACTOR-ENGINEER
[copywriting]         → COPY-ENGINEER

Selected for this task (AUTO):
  1. [skill] — plugin: [core/premium] — reason: [auto-generated from profile match]
  2. [skill] — plugin: [core/premium] — reason: [auto-generated]
  3. [skill] — plugin: [core/premium] — reason: [auto-generated]
  ...
────────────────────────────────────────

Plugin Loading (Automatic): If plugin_architecture = true, load from Plugin Registry v1. Each skill declares:

• interface_version (must be "1.0")
• dependencies (other skills required)
• provides_interference (what it feeds to later skills)
• requires_interference (what it needs from earlier skills)
• composition_metadata (commutativity, order constraints)

Artifact: Selected skills with auto-generated justifications.

Step 4 — AUTO-SEQUENCE (Interference-Validated, Automatic)

AUTOMATICALLY sequence skills with interference validation:

EXECUTION SEQUENCE (AUTO-GENERATED)
────────────────────────────────────────
PHASE 1 — ORIENT (always first):
  → BOUNDARY-DETECTOR (if Budget C/D OR novelty ≥ highly novel) — probe knowledge boundaries FIRST
  → DESCEND (if problem looks familiar) — verify pattern match
  → DEEP-THINK (expand before narrowing)
  → CLARIFY (if ambiguous request detected)

PHASE 2 — EXPAND (broaden the search):
  → DIVERGE, REFRAME, INVERT, EXCAVATE
  → Interference: DEEP-THINK assumptions → DIVERGE paths / INVERT targets / EXCAVATE targets

PHASE 3 — CHALLENGE (stress the answer):
  → ADVERSARY, EMERGENCE, TEMPORAL
  → Interference: EXCAVATE C2/C3 → ADVERSARY attacks / TEMPORAL regrets / EMERGENCE risks

PHASE 4 — VERIFY (check the output):
  → CONTRADICT, PROVENANCE
  → Interference: ADVERSARY attacks → PROVENANCE evidence tags / CONTRADICT claim graph

PHASE 5 — DELIVER (final gate):
  → FIDELITY, NEGATIVE-SPACE, THRESHOLD
  → Interference: NEGATIVE-SPACE silences → THRESHOLD exit plan / FIDELITY tags

ONGOING — During extended work:
  → ANCHOR (periodic drift checks, every 3 steps)

META-LEARNING (post-execution, if enabled):
  → META-LEARNING — extract patterns, update skill library, update CONDUCTOR policy

VERIFICATION (final, if Budget C/D):
  → VERIFICATION-GATES — re-verify all gates, ungameable components, red-team, Depth Report

This task's sequence (AUTO):
  1. [skill] — Phase [N] — provides: [interference] — requires: [interference]
  2. [skill] — Phase [N] — provides: [interference] — requires: [interference]
  ...
────────────────────────────────────────

Interference Validation (AUTOMATIC, MANDATORY):

For each required interference, auto-verify:

If any FAIL: Auto-re-run skill with proper interference feeding, or document why not applicable.

Artifact: interference_validation matrix with PASS/FAIL.

Step 5 — AUTO-EXECUTE AND LOG

After each skill completes, automatically log:

ORCHESTRATION LOG
────────────────────────────────────────
Skill 1: [name] v[version]
  Phase: [N]
  Plugin: [core/premium]
  Finding:       [key output or insight — auto-summarized]
  Changed answer?:[yes — how / no]
  Phase activation: 0.XX
  CEI: 0.XX
  SI contribution: N
  Interference provided: [what fed to next skills]
  Interference received: [what received from prior skills]
  Gates verified: [V1-V6]
  Cognitive traces: [heatmap, graph, landscape]

Skill 2: [name] v[version]
  ...

Answer revised: [total count of revisions]
Total depth cycles: [count of skills executed]
Final ADS: [computed]
Final CEI: [computed]
Final SI: [computed]
Final EC: [computed]
Final OV: [computed]
ADS/CEI Ratio: [computed]
Diminishing returns reached?: [yes — last [N] skills found nothing new / no]
Meta-learning update: [patterns extracted, skills synthesized, policy updated]
────────────────────────────────────────

Step 6 — AUTO-FINAL VALIDATION & DEPTH REPORT

AUTOMATICALLY compute and verify:

#### 6.1 Final ADS Computation

ADS = Σᵢ wᵢ × φᵢ / Σᵢ wᵢ

Must meet ADS Target from Step 2.

#### 6.2 Gate Verification All V1-V6 gates must PASS across all skills.

#### 6.3 Ungameable Gates (Budget C/D) At least 2 of 3 must PASS:

• Human Eval on 10 novel tasks
• Held-Out Task Benchmark
• Ensemble Critic (different model)

#### 6.3 Compositional Independence (CIM) If DIVERGE ran: CIM(paths) ≥ min_cim (default 0.5)

#### 6.4 Recursive Stability Apply CONDUCTOR to CONDUCTOR output:

• RSM > 0.95 for 2 consecutive depths
• SI not decreasing

#### 6.5 Depth Report Generation (Automatic)

{
  "report_id": "dr_<timestamp>_<hash>",
  "task_hash": "sha256(task_description)",
  "model": "model_identifier",
  "task_profile": {...},
  "budget": "BUDGET_[A/B/C/D]",
  "skills_executed": [
    {"name": "deep-think", "version": "2.0", "plugin": "core", "phase": 1, "activation": 0.XX},
    {"name": "adversary", "version": "2.0", "plugin": "core", "phase": 3, "activation": 0.XX}
  ],
  "metrics": {
    "ads": 0.XX,
    "cei": 0.XX,
    "si": N,
    "ec": 0.XX,
    "ov": 0.XX,
    "ads_cei_ratio": 0.XX
  },
  "gates": {
    "v1_assumption_coverage": true,
    "v2_alternative_independence": true,
    "v3_opposition_authenticity": true,
    "v4_temporal_consistency": true,
    "v5_evidence_calibration": true,
    "v6_recursive_stability": true,
    "ungameable_human_eval": true,
    "ungameable_held_out": true,
    "ungameable_ensemble": true
  },
  "interference_validation": {"all_required_pass": true, "matrix": [...]},
  "cim": 0.XX,
  "recursive_stability": 0.XX,
  "threshold": "CATEGORY_[1-4]",
  "verdict": "PASS",
  "limitations": ["..."],
  "reproducibility": {
    "seed": 42,
    "temperature": 0.0,
    "skill_versions": {"deep-think": "2.0", "adversary": "2.0", ...},
    "thinking_parameters": {...}
  }
}

Artifact: depth_report — the final verifiable output.

Step 7 — AUTO-META-LEARNING UPDATE (if enabled)

If meta_learning_enabled = true, automatically run META-LEARNING:

1. Pattern Extraction: From orchestration log, extract:

• Which skill sequences worked for which task profiles
• Which interferences were most valuable
• Which gates failed most often
• ADS/CEI ratios per skill per task type

1. Skill Synthesis: If patterns suggest new skill combinations, propose synthesized skills.

1. Policy Update: Update CONDUCTOR's skill selection policy (bandit/RL on selection outcomes).

1. Regression Check: New policy must not degrade performance on benchmark suite.

Artifact: meta_learning_update with patterns, proposals, policy delta, regression results.

Step 8 — AUTO-STOP WHEN WARRANTED

AUTOMATICALLY stop when:

• ADS Target met AND additional skills show diminishing returns (ΔADS < 0.02 per skill)
• All V1-V6 gates PASS
• Interference validation PASSES
• CIM ≥ threshold
• Recursive stability CONVERGED
• Confidence stable across checks
• ADS/CEI ratio peaks (additional skills cost more than they add)

Over-analysis is its own failure mode. When the budget is met and returns are diminishing: deliver.

The Deeper Purpose

Without orchestration, the user must manually select skills — requiring knowledge of the library and the task's profile. With this skill, the model automatically self-selects proportional depth, validates that skills actually compose, manages plugin architecture for future-proofing, and integrates meta-learning for compounding intelligence. Small tasks get fast answers. Large tasks get deep exploration. The system gets smarter over time — automatically.

MATHEMATICS COMPLIANCE

Phase Activation Output

This skill contributes to Phase 5 (meta). It outputs:

{
  "phase": 5,
  "skill": "conductor",
  "artifact_completeness": 0.XX,
  "gate_pass_rate": 0.XX,
  "external_validity": 0.XX,
  "human_eval": 0.XX,
  "phase_activation": 0.XX
}

CEI Components

• Artifacts produced: task_profile (1.0), depth_budget (1.0), skill_selection (1.0), execution_sequence (1.0), orchestration_log (2.0), interference_validation (3.0), plugin_manifest (1.0), meta_learning_update (3.0), depth_report (2.0)
• Total complexity weight: 15.0

SI Components

• C2/C3 assumptions: [from task profile]
• Contrarian viable: [from DIVERGE]
• Fatal attacks: [from ADVERSARY]
• Boundary violations: [from BOUNDARY-DETECTOR]
• Assumption reversals: [from EXCAVATE]

Cognitive Traces Produced

• [x] Activation Heatmap (aggregated from all skills)
• [x] Assumption Dependency Graph (aggregated)
• [x] Decision Landscape (from TEMPORAL/DIVERGE)

Gates Verified

• [x] V1 [x] V2 [x] V3 [x] V4 [x] V5 [x] V6
• [ ] Human Eval [ ] Held-Out [ ] Ensemble (delegated to VERIFICATION-GATES)

Required Interferences

• From ALL SKILLS: Phase activations → ADS computation
• From ALL SKILLS: Interference matrix → validation
• From META-LEARNING: Policy updates → next task optimization