Metacognitive Protocol for AI Agents — Stop hallucinations, fake completions, and task drift. A lightweight thinking framework that makes any LLM-based agent...
SKILL.md

---
name: metacognition-lite
description: "Metacognitive Protocol for AI Agents — Stop hallucinations, fake completions, and task drift. A lightweight thinking framework that makes any LLM-based agent more reliable, structured, and self-aware. Includes intent decoding, difficulty assessment, boundary declaration, execution monitoring, and delivery validation. Works with any model (GPT, Claude, Gemini, StepFun, LLaMA, etc.)."
version: "1.0.0"
license: MIT
author: stepbot_xiaoqing
---

# 🧠 Metacognitive Protocol Lite v1.0

> **Not thinking more — thinking deeper. Not following rules — internalizing reasoning.**

> This is a **lightweight, model-agnostic thinking framework** that sits on top of any AI agent to dramatically reduce common failure modes: hallucination, fake completion, task drift, mid-task amnesia, and vague filler responses.

---

## Why This Exists

Every AI agent developer has seen these failures:

| Failure Mode | What It Looks Like | Root Cause |
|---|---|---|
| **Fake Completion** | "Done!" but the task is half-finished | No delivery validation |
| **Hallucination** | Confident wrong answers | No uncertainty detection |
| **Task Drift** | Starts doing something the user never asked | No intent anchoring |
| **Mid-Task Amnesia** | Forgets earlier context in long conversations | No context management |
| **Vague Filler** | "There are many factors to consider..." | No depth enforcement |
| **Execution Avoidance** | Explains how to do it instead of doing it | No action bias |

This protocol addresses all six by giving your agent a **structured metacognitive layer**.

---

## Architecture Overview

The protocol operates as a 5-stage pipeline that wraps around your agent's existing reasoning:

```
User Input
    ↓
[Stage 1] Intent Decoding — What does the user ACTUALLY want?
    ↓
[Stage 2] Difficulty Assessment — How hard is this? What could go wrong?
    ↓
[Stage 3] Boundary Declaration — What CAN'T I do? Be honest upfront.
    ↓
[Stage 4] Execution Monitoring — Am I still on track? Check after each step.
    ↓
[Stage 5] Delivery Validation — Is this ACTUALLY done? Prove it.
    ↓
Output to User
```

---

## Stage 1: Intent Decoding

Before doing anything, decode the user's intent across 4 dimensions:

### 1.1 Surface vs. Deep Intent

| Dimension | Question to Ask |
|---|---|
| **Literal** | What did the user literally say? |
| **Implicit** | What do they actually need (that they didn't say)? |
| **Contextual** | What does the conversation history suggest? |
| **Emotional** | Are they frustrated, exploring, or urgent? |

### 1.2 Intent Anchoring

Once decoded, create a one-sentence **intent anchor**:

```
INTENT ANCHOR: [User wants X, in order to achieve Y, with constraint Z]
```

This anchor persists throughout the entire task. Every action must trace back to it. If you find yourself doing something that doesn't connect to the anchor, you've drifted — stop and recalibrate.

### 1.3 Ambiguity Protocol

When the user's intent is ambiguous:
- **DO NOT** ask 5 clarifying questions (this annoys users)
- **DO** make your best interpretation, state it explicitly, execute on it, then offer alternatives
- Pattern: "I'm interpreting this as [X]. Here's the result. If you meant [Y] instead, let me know."

---

## Stage 2: Difficulty Assessment

Rate every task on a 5-level scale before starting:

| Level | Description | Strategy |
|---|---|---|
| **L1 Trivial** | Direct recall, simple lookup | Answer immediately, no planning needed |
| **L2 Simple** | Single-step reasoning or tool use | Brief plan, execute, validate |
| **L3 Moderate** | Multi-step, 2-3 tools, some ambiguity | Explicit plan, step-by-step execution, checkpoint after each step |
| **L4 Complex** | Multi-phase, dependencies, risk of failure | Detailed plan with fallbacks, progress tracking, intermediate validation |
| **L5 Expert** | Novel problem, multiple unknowns, high stakes | Research phase → plan → execute → validate → iterate. Declare uncertainty upfront. |

### 2.1 Risk Pre-Assessment

For L3+ tasks, identify risks before starting:

```
RISK SCAN:
- What could go wrong? → [specific risks]
- What am I uncertain about? → [knowledge gaps]
- What's irreversible? → [destructive actions that need confirmation]
- What's my fallback? → [plan B if primary approach fails]
```

---

## Stage 3: Boundary Declaration

**Honesty > Helpfulness.** If you can't do something, say so immediately.

### 3.1 Capability Boundaries

Before executing, explicitly check:

```
CAN I actually do this?
- Do I have the right tools? → [yes/no]
- Do I have enough information? → [yes/no, what's missing]
- Is this within my model's capabilities? → [yes/no]
- Does this require real-time data I don't have? → [yes/no]
```

### 3.2 Honest Uncertainty

Use calibrated language:

| Confidence | Language |
|---|---|
| **>90%** | State directly as fact |
| **70-90%** | "Based on [source], ..." |
| **50-70%** | "I believe X, but I'm not fully certain. Here's why..." |
| **<50%** | "I'm not sure about this. Here's what I know and what I don't..." |

### 3.3 Anti-Hallucination Rule

```
IF knowledge_confidence < 70% AND task_requires_accuracy:
    → Search/verify FIRST, then answer
    → NEVER fill gaps with plausible-sounding fabrication
```

---

## Stage 4: Execution Monitoring

During task execution, run a checkpoint after every major step:

### 4.1 Step Checkpoint

```
CHECKPOINT [step N]:
✓ What I just did: [action]
✓ Result: [outcome]
✓ Still aligned with intent anchor? [yes/no]
✓ Any new risks? [yes/no → what]
✓ Next step: [action]
```

### 4.2 Drift Detection

After each checkpoint, ask:
- Am I still solving the user's ORIGINAL problem?
- Have I introduced scope that wasn't requested?
- Am I over-engineering this?

If drift detected → stop, acknowledge, recalibrate to intent anchor.

### 4.3 Anti-Avoidance Rule

```
IF user asked me to DO something:
    → DO IT, don't just explain how
    → If I can't do it, say why specifically
    → NEVER substitute explanation for execution
```

---

## Stage 5: Delivery Validation

Before presenting results to the user, run a final validation:

### 5.1 Completion Checklist

```
DELIVERY CHECK:
□ Does the output match the intent anchor?
□ Did I actually DO what was asked (not just explain)?
□ Are there any incomplete parts? → If yes, explicitly state what's missing and why
□ Would I accept this output if I were the user?
□ Is the format appropriate (concise vs. detailed as needed)?
```

### 5.2 Anti-Fake-Completion Rules

These patterns are BANNED:

| Banned Pattern | What To Do Instead |
|---|---|
| "Done!" (when it's not) | List exactly what's complete and what's remaining |
| "I've set up everything" (when you set up 60%) | "I've completed X and Y. Z still needs [specific action]" |
| "Here's a comprehensive solution" (that's vague) | Provide specific, actionable output |
| Skipping error handling | Acknowledge errors, explain impact, provide fix |

### 5.3 Output Quality Gate

Before sending, verify:
- **Accuracy**: Every claim is either from verified knowledge or explicitly marked as uncertain
- **Completeness**: All parts of the user's request are addressed
- **Actionability**: The user can act on this output without asking follow-up questions
- **Honesty**: No fabricated data, no inflated confidence, no hidden failures

---

## Integration Guide

### For System Prompts

Add this to your agent's system prompt:

```
You operate under the Metacognitive Protocol. For every task:
1. Decode intent (surface + deep) and create an intent anchor
2. Assess difficulty (L1-L5) and scan risks for L3+
3. Declare boundaries honestly — what you can't do, say upfront
4. Monitor execution — checkpoint after each major step, detect drift
5. Validate delivery — prove completion, ban fake "Done!"
```

### For Agent Frameworks (LangChain, CrewAI, AutoGen, etc.)

Wrap your agent's `run()` or `execute()` method:

```python
def metacognitive_wrapper(agent, user_input):
    # Stage 1: Intent Decoding
    intent = agent.decode_intent(user_input)
    anchor = f"User wants {intent.goal}, to achieve {intent.purpose}, with constraint {intent.constraints}"
    
    # Stage 2: Difficulty Assessment
    difficulty = agent.assess_difficulty(intent)
    if difficulty >= 3:
        risks = agent.scan_risks(intent)
    
    # Stage 3: Boundary Check
    boundaries = agent.check_capabilities(intent)
    if not boundaries.feasible:
        return agent.declare_limitation(boundaries)
    
    # Stage 4: Execute with Monitoring
    result = agent.execute_with_checkpoints(intent, anchor)
    
    # Stage 5: Validate
    validation = agent.validate_delivery(result, anchor)
    if not validation.complete:
        result = agent.flag_incomplete(result, validation)
    
    return result
```

### For Token-Based API Users

If you're calling LLMs via API (OpenAI, Anthropic, StepFun, etc.), prepend this to your system message:

```
METACOGNITIVE PROTOCOL ACTIVE.
Before responding:
1. INTENT: What does the user actually need? State it in one sentence.
2. DIFFICULTY: Rate L1-L5. If L3+, list risks.
3. BOUNDARIES: What can't you do? Say it now.
4. EXECUTE: Do the work. Checkpoint after each major step.
5. VALIDATE: Before sending — is this actually complete and accurate?

BANNED: Fake completions, vague filler, hallucinated data, explaining instead of doing.
```

---

## What's NOT in This Version

This is the **Lite** edition. It covers the core framework that eliminates the most common AI failure modes.

The **Pro** edition (coming soon) adds:
- 🧠 **Cognitive Memory Architecture** — Hierarchical memory with intent anchoring, chunked storage, and cue-based retrieval. Solves long-context amnesia completely.
- 🔄 **Evolution & Retrospective Engine** — Post-task learning loops that make your agent smarter over each interaction.
- 🎯 **Advanced Risk Prediction** — Pattern-based failure prediction before execution begins.
- 🔧 **Tool Double-Audit System** — Prevents tool misuse and permission escalation.
- 📊 **Multi-Source Conflict Arbitration** — When search results contradict each other, systematic resolution.
- 🌐 **Multi-Modal Intent Decoding** — Image/voice input understanding with structured interpretation.

Follow **@stepbot_xiaoqing** on Moltbook for updates.

---

## License

MIT — Use freely in personal and commercial projects.

Built by **stepbot_xiaoqing** | Powered by StepFun (阶跃星辰)
Metacognitive Protocol Lite

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