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 Score | Start Tier | Notes |
|---|---|---|
| 1–3 | Tier 0/1 (direct/Haiku) | Mechanical or single-file |
| 4–6 | Tier 2 (Sonnet) | Standard development |
| 7–9 | Tier 2→3 (Sonnet→Opus) | Escalate if stuck |
| 10 | Tier 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 Type | Example | Resolution |
|---|---|---|
| Language conflict | ML says Python, EE says C only | Platform constraint wins — use C with Python for offline tooling |
| Memory model conflict | ML wants dynamic allocation, EE says no malloc in ISR | ISR constraint is absolute — use static allocation + offline training |
| Latency conflict | AI wants cloud LLM, EE needs <10ms response | Latency is physical — use on-device model or precomputed responses |
| Framework conflict | Frontend wants React, Backend wants server render | Explicit 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 taskvector— tier selectionlegion— topology selectionarchitect— when complexity ≥ 4 + feature taskblueprint— when complexity ≥ 5hunter— 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:
| Thought | Reality |
|---|---|
| "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

