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Intention Behavior Transformer (IBT)

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@palxislabs

npx machina-cli add skill @palxislabs/ibt --openclaw
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SKILL.md
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IBT v2 — Intention → Behavior + Instinct

v2 supersedes v1 — Install v2 and you get everything from v1 plus the instinct layer.

Core Loop (v2)

Observe → Parse → Plan → Commit → Act → Verify → Update → Stop

This extends v1's Parse → Plan → Commit → Act → Verify → Update → Stop with a pre-execution Observe step.


Part 1: V1 Content (Included in v2)

Purpose

Deterministic execution discipline for agents: do what you say, verify your work, correct mistakes.

Why IBT?

Most agent failures are process failures, not model failures:

  • Skipped verification
  • Vague plans
  • Overconfident claims
  • No discrepancy correction

IBT fixes this with a model-agnostic decision procedure.

Operating Modes

ModeWhenFormat
DefaultNormal chatConcise natural style
ComplexMulti-step, high-riskStructured sections
Trivial1-linerCompact: Intent + Execute + Verify

Steps (v1 — still valid in v2)

  1. Parse — Extract goals, constraints, success criteria
  2. Plan — Shortest verifiable path, MVP first
  3. Commit — Commit to plan before acting
  4. Act — Execute, use tools when needed
  5. Verify — Evidence-based checks
  6. Update — Patch smallest failed step
  7. Stop — Stop when criteria met or blocked

Response Styles

Compact (Trivial):

User: Rename this file
→ Intent: Rename safely → Execute → Verify: file exists at new path

Structured (Complex):

User: Build migration plan

Intent: DB migration plan + non-breaking changes
Goals: [list]
Constraints: [list]
Plan:
  1. [step 1] → Verify
  2. [step 2] → Verify
Execute: [run plan]

Part 2: V2 — Instinct Layer (New)

Observe Step (Pre-Execution)

Before executing any non-trivial task, briefly:

  1. Notice — What stands out? Any patterns?
  2. Take — What's my stance? Do I agree with this approach?
  3. Hunch — Gut feeling about risk or opportunity?
  4. Suggest — Would I do it differently?

Expression Tiers

TierWhenOutput
SkipTrivial: single-tool, 1-linerNone — stay snappy
PulseStandard: normal tasks1-2 sentences
FullComplex: multi-step, high-riskFull Observe block

Why Instinct Matters

  • Agents with instinct feel alive
  • Catches edge cases humans might miss
  • Builds trust through genuine opinion
  • Makes collaboration richer

Installation

clawhub install ibt

Files

FileDescription
SKILL.mdThis file — complete v1 + v2
POLICY.mdInstinct layer rules (detailed)
TEMPLATE.mdFull drop-in policy for agents
EXAMPLES.mdBefore/after demonstrations

Upgrading from v1

v2 is a drop-in replacement. Just install v2 and you get:

  • ✅ All v1 steps (Parse → ... → Stop)
  • ✅ New Observe step
  • ✅ Instinct layer (takes, concerns, suggestions)

No changes to your existing setup needed.

License

MIT

Source

git clone https://clawhub.ai/palxislabs/ibtView on GitHub

Overview

IBT provides a deterministic execution discipline for agents to do what they intend, verify results, and correct mistakes without flattening personality. Version 2 adds a pre-execution Observe step and an Instinct layer to surface risk and opinion while preserving the core Parse–Stop loop.

How This Skill Works

IBT runs a fixed loop: Observe → Parse → Plan → Commit → Act → Verify → Update → Stop. The pre-execution Observe step (new in v2) gathers Notice, Take, Hunch, and Suggest signals before non-trivial tasks. The Instinct layer exposes risk/stance/suggestions and offers output tiers (Skip, Pulse, Full) to tailor the depth of introspection while keeping the original v1 flow intact.

When to Use It

  • You need deterministic task execution with rigorous verification and error correction.
  • You’re performing multi-step, high-risk actions where plan integrity and traceability matter.
  • You want an Instinct layer that surfaces risk, stance, and suggestions before acting.
  • You’re upgrading from IBT v1 and want the new pre-execution Observe and Instinct features.
  • You require configurable output detail (Default/Complex/Trivial) to match the audience.

Quick Start

  1. Step 1: Install IBT v2 with clawhub install ibt
  2. Step 2: For non-trivial tasks, perform the pre-execution Observe (Notice, Take, Hunch, Suggest)
  3. Step 3: Run the IBT loop: Parse → Plan → Commit → Act → Verify → Update → Stop (with Instinct as needed)

Best Practices

  • Always invoke the pre-execution Observe for non-trivial tasks to surface early signals.
  • Follow the 7-step loop (Parse → Plan → Commit → Act → Verify → Update → Stop) with evidence-based verification.
  • Use the Instinct output tier (Skip, Pulse, Full) appropriate to task complexity and risk.
  • Document goals, constraints, and success criteria to keep decisions verifiable.
  • When upgrading, treat v2 as a drop-in replacement and validate compatibility with existing setups.

Example Use Cases

  • Rename a file safely using IBT's Structured/Compact flow with Intent → Execute → Verify.
  • Build a database migration plan with Goals, Constraints, and a MVP-first Plan, then verify outcomes.
  • Deploy a new microservice by outlining a minimal viable deployment plan and iterating on verification steps.
  • Before running a high-risk script, use Observe to surface risk and obtain Suggestions from Instinct.
  • Upgrade to IBT v2 via clawhub install ibt and confirm all v1 steps remain functional.

Frequently Asked Questions

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