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Skill v1.0.0

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PublishedOctober 29, 2026 at 09:24 PM
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name: oracle description: Pre-task intelligence layer — classify complexity, select skill chain, search patterns, assign model tier. Run BEFORE any non-trivial task begins. Turns the system from reactive to proactive. type: process metadata: version: "1.0.0"


Task Intake

Overview

ORACLE — Latin "oraculum": the prophetic voice consulted before any major decision. When invoked: scores task complexity 1–10 across 8 dimensions, selects the optimal skill chain (BUG / FEATURE / REFACTOR / ARCHITECT), assigns the correct model tier (Haiku → Opus), and searches memory for relevant past patterns — all in 60 seconds before any code is written.

Core principle: Never start a task cold. 60 seconds of classification prevents hours of wrong-direction work.

This skill is the meta-intelligence layer of Superpowers. It classifies every task before execution, selects the optimal skill chain, searches for relevant past patterns, and assigns the correct model tier — automatically.

Announce at start: "Running ORACLE to classify and plan approach."

WHEN TO RUN:

  • Before ANY task that touches code or architecture
  • Before ANY bug investigation
  • Before ANY new feature or design decision
  • At the start of EVERY non-trivial conversation

SKIP only for: Trivial one-liner answers, pure information questions, file reads.


Fast Path (Complexity ≤ 3)

Before running all 5 phases, do a 5-second triage:

Is ALL of the following true?
✓ Single file or fewer than 3 files
✓ No investigation required — cause is known
✓ No security, architecture, or cross-system implications
✓ Similar task done before (no novelty)
✓ No domain skill triggers
→ FAST PATH: Skip Phases 2–5.
Announce: "Complexity ≤ 3 — fast path. Tier 1, no plan needed."
Proceed directly to implementation.

If ANY condition is false, run the full 5-phase intake.


Phase 1: Task Classification (30 seconds)

Step 1: Score Complexity (1–10)

Answer each question honestly:

Question+Points
Touches more than 3 files?+1
Requires investigation before implementation?+1
Touches 2+ engineering domains?+1
Has security implications?+2
Involves architecture decisions?+2
Novel problem (no precedent in codebase)?+2
Cross-system integration?+1
Strict performance or timing requirements?+1

Score → Tier:

1-3: SIMPLE → Tier 0/1 model, single skill, no plan needed
4-6: STANDARD → Tier 1/2 model, 2-4 skills, plan recommended
7-9: COMPLEX → Tier 2/3 model, full skill chain, plan required
10: CRITICAL → Tier 3 model, SPARC structure, human review gate

Step 2: Identify Domain(s)

Check all that apply:

  • [ ] Frontend / UI → invoke prism
  • [ ] ML / Data Science → invoke gradient
  • [ ] AI / LLM / RAG / Agents → invoke nexus
  • [ ] Embedded / Firmware / EE → invoke ironcore
  • [ ] General backend / web → standard skills
  • [ ] Multi-domain → invoke all relevant domain skills

Step 3: Identify Task Type

Is this a BUG?
└─ Yes → [debug-chain]
Is this a NEW FEATURE?
└─ Yes → [feature-chain]
Is this a REFACTOR?
└─ Yes → [refactor-chain]
Is this an ARCHITECTURE DECISION?
└─ Yes → [architecture-chain]
Is this a RESEARCH / EXPLORATION?
└─ Yes → [research-chain]

Phase 2: Skill Chain Selection

Pre-built chains — use the matching one, execute skills in order:

[debug-chain]

1. chronicle → search for similar past bugs
2. hunter → root cause investigation
3. forge → failing test before fix
4. sentinel → verify fix works
5. chronicle → store pattern after success

[feature-chain]

1. chronicle → search for similar past features
2. [domain skill(s)] → apply domain expertise
3. architect → design before code (complexity ≥ 4)
4. blueprint → implementation plan (complexity ≥ 5)
5. vector → assign tier per task
6. legion → topology if 3+ independent files
7. phantom → execute plan (complexity ≥ 6)
8. sentinel → gate before completion
9. tribunal → review gate (complexity ≥ 7)
10. chronicle → store pattern after success

[refactor-chain]

1. chronicle → search past refactor patterns
2. forge → baseline tests before touching code
3. blueprint → plan the refactor (complexity ≥ 5)
4. vector → tier 1/2 for mechanical work
5. sentinel → no regression
6. chronicle → store pattern

[architecture-chain]

1. chronicle → past architecture decisions
2. architect → explore 2-3 approaches
3. blueprint (SPARC) → SPARC structure REQUIRED
4. tribunal → architecture review gate
5. chronicle → store decision + rationale

[research-chain]

1. chronicle → search existing knowledge
2. legion (Mesh) → parallel investigation
3. architect → synthesize findings
4. chronicle → store findings

Phase 3: Pattern Search

BEFORE starting ANY task (complexity ≥ 4):

SEARCH ReasoningBank:
Query: "<task-type> <domain> <key-keywords>"
Example:
- "feature ml-engineering data-drift detection"
- "debug frontend react useEffect stale closure"
- "architecture api jwt multi-tenant"
REVIEW matches:
- What worked? Apply directly.
- What failed? Avoid explicitly.
- What's different? Note context differences.
ANNOUNCE findings:
"Found [N] relevant patterns:
1. [key] — [one-sentence summary]
Applying: [how it influences this approach]"
If no matches:
"No prior patterns. Novel problem — will create pattern after solving."

Phase 4: Model Tier Assignment

Use the decision from vector skill. Quick summary:

Complexity ScoreStart TierNotes
1–3Tier 0/1 (direct/Haiku)Mechanical or single-file
4–6Tier 2 (Sonnet)Standard development
7–9Tier 2→3 (Sonnet→Opus)Escalate if stuck
10Tier 3 (Opus)Architecture/security critical

Phase 5: SPARC Gate

SPARC is REQUIRED (not optional) when ANY of:

  • Complexity score ≥ 8
  • Task involves security-critical code
  • Task involves architecture decisions (system design)
  • Task is cross-system integration
  • Task has been attempted before and failed

SPARC is optional when:

  • Complexity score ≤ 7
  • Clear requirements exist
  • Similar task done before (patterns found)

Task Intake Output Template

After completing all 5 phases, announce:

TASK INTAKE COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Complexity: [score]/10 → [SIMPLE/STANDARD/COMPLEX/CRITICAL]
Domain(s): [list]
Task type: [debug/feature/refactor/architecture/research]
Skill chain: [chain name]
Model tier: [0/1/2/3]
SPARC: [Required/Optional]
Patterns: [N found | 0 found — novel]
Proceeding with [chain name] chain.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Multi-Domain Synthesis

When a task touches 2+ engineering domains simultaneously, conflicting guidance can arise. Resolve it before starting work.

Step 1: Identify Conflicts

After invoking domain skills, check for contradictions:

Conflict TypeExampleResolution
Language conflictML says Python, EE says C onlyPlatform constraint wins — use C with Python for offline tooling
Memory model conflictML wants dynamic allocation, EE says no malloc in ISRISR constraint is absolute — use static allocation + offline training
Latency conflictAI wants cloud LLM, EE needs <10ms responseLatency is physical — use on-device model or precomputed responses
Framework conflictFrontend wants React, Backend wants server renderExplicit user constraint wins; if none, prefer existing codebase pattern

Rule: Physical/hardware constraints always win over software preferences.

Step 2: Build Synthesis Statement

Before starting any multi-domain task, write:

MULTI-DOMAIN SYNTHESIS
━━━━━━━━━━━━━━━━━━━━━━
Domains: [domain 1] + [domain 2] (+ [domain 3])
Conflicts: [list any conflicting guidance]
Resolution: [which constraint wins and why]
Unified constraint: [the combined non-negotiables]
━━━━━━━━━━━━━━━━━━━━━━

Step 3: Add Domain Reviewers

For multi-domain code review, specify ALL relevant domains:

DOMAIN: ml, embedded

The reviewer will apply both checklists.


After-Action Review (Task Close Protocol)

Run this AFTER every task completes — before moving to the next.

This closes the learning loop. Without it, patterns are stored inconsistently or not at all.

AFTER-ACTION REVIEW
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Did the skill chain work as planned?
[ ] Yes — as expected
[ ] Partially — [what deviated]
[ ] No — [what went wrong]
2. Was the complexity score accurate?
Predicted: [N] | Actual feel: [lower/accurate/higher]
→ [adjust future scoring for this task type]
3. Was the model tier right?
[ ] Yes — tier [N] was appropriate
[ ] Should have been higher — [why]
[ ] Could have been lower — [why]
4. What pattern should be stored?
Key: <domain>:<type>:<keywords>
[ ] Storing now in auto-memory
[ ] Nothing novel — skipping storage
5. What would I do differently?
[one sentence or "nothing"]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

After-action is mandatory when:

  • Task complexity was ≥ 6
  • Something unexpected happened
  • A novel approach was used
  • The first approach failed

After-action is optional when:

  • Complexity < 4 and everything went as expected
  • Mechanical task with no surprises

Storage: If a pattern is worth storing, invoke chronicle now.


Integration with Superpowers

Calls into:

  • chronicle — pattern search before task
  • vector — tier selection
  • legion — topology selection
  • architect — when complexity ≥ 4 + feature task
  • blueprint — when complexity ≥ 5
  • hunter — when task type is debug

Called by:

  • ascend — ORACLE is the FIRST process skill for non-trivial tasks

Red Flags

Never skip ORACLE when:

  • You feel "this is obviously simple" (obvious ≠ simple)
  • You're under time pressure (intake is 60 seconds, saves hours)
  • The user says "just quickly..." (scope always grows)
  • You've started before thinking (stop, run intake)

Rationalization table:

ThoughtReality
"I know what to do"Knowing ≠ best approach. Intake confirms.
"Too simple for intake"Simple tasks rarely stay simple.
"User wants it fast"Wrong approach is slower than 60-second intake.
"I'll plan as I go"Unplanned work creates rework.

Final Rule

60 seconds of intake → hours saved
No task starts cold
Classify → chain → search → tier → execute
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