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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.

Overview

PublisherGadaaLabs
Repositoryclaude-code-on-steroids
Skill nameoracle
Stars
67
Forks
10
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by GadaaLabs on GitHub. Read the source before you install it.

Installation

Install the Oracle AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GadaaLabs/claude-code-on-steroids.git /tmp/claude-code-on-steroids
mkdir -p .claude/skills
cp -r /tmp/claude-code-on-steroids/skills/oracle .claude/skills/oracle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oracle in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oracle on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oracle is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Task Intake

Overview

ORACLELatin "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

Frequently asked questions

What does the Oracle AI skill do?

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.

Why use Oracle on TypingMind?

Because you install it once and use it with any model. Oracle is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oracle in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GadaaLabs/claude-code-on-steroids/tree/main/skills/oracle. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Oracle?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oracle?

As many as you like. As long as a model supports skills, you can use Oracle with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oracle AI skill free?

It is published on GitHub by GadaaLabs. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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