Task Boundary Auditor

Quickly assesses if a task exceeds LLM limits (e.g., verification, prediction), then advises what the LLM can do versus what needs expert or tool input.

Overview

When an LLM oversteps its boundaries, it does not say "I can't do this." Instead, it delivers answers that are perfectly formatted and logically coherent—but fundamentally wrong. This is the sophisticated hallucination. This Skill performs a rapid pre-execution audit to determine whether a task exceeds LLM capabilities, and proposes a human-machine division of labor: what the LLM can handle, and what must be handed off to specialized tools or humans.

When to Use

• The user asks the LLM to perform causal inference, precise verification, real-time control, physical design, or other tasks beyond its capabilities
• The user demands completeness guarantees such as "zero omissions," "find all," or "ensure none are missed"
• The user requests counterfactual reasoning ("what if X had never happened")
• The task mixes safe and unsafe sub-tasks

When NOT to Use

• Pure text generation, translation, summarization, or format conversion (safe-zone tasks—execute directly)
• The user already clearly understands that human/professional tools are needed
• Simple information retrieval or Q&A

Boundary Classification

Prohibited Zone (LLM must not take primary responsibility)

Type	Detection Signature	Core Characteristic	Correct Approach
Counterfactual Reasoning	Hypothetical worlds, long causal chains	LLM has no world model; can only assemble narratives	Causal models / expert reasoning; LLM extracts evidence
Precise Verification	Symbolic proof, code audit, zero omissions	Error in long-chain reasoning compounds exponentially	Formal tools / expert verification; LLM translates specifications
Real-Time Closed-Loop	Cross-call state retention, sense-act-feedback	LLM is stateless and has no sensor interfaces	Rule engines / control systems; LLM writes logs
Physical Intuition	Spatial-mechanical-material reasoning	LLM's understanding of physics is second-hand text	CAD / simulation software; LLM interprets results
Conceptual Extrapolation	Creative breakthroughs, out-of-training-distribution	Can only interpolate and recombine; cannot extrapolate	Human provides core insight; LLM assists with association
Ethical Adjudication	Requires subjectivity and legal liability	LLM has no subjectivity or capacity for responsibility	Human adjudication; LLM structures the framework
Zero-Omission Audit	Completeness guarantee, blind-spot-free scan	Generative review has attention blind spots	Rule-engine scan; LLM assists with explanation
Extreme Prediction	Out-of-distribution events, black swans	Forcibly translates into known patterns	Causal rule-layer reasoning; LLM performs extraction

Safe Zone (LLM can take primary responsibility)

Type	Examples
Text Generation	Writing emails, drafting articles
Translation & Conversion	Chinese-English translation, format conversion
Information Extraction	Extracting data or key points from reports
Summarization & Compression	Paper summaries, meeting minutes
Formatting	Converting to tables, generating templates

Restricted Zone (LLM assists; human / professional tools take primary responsibility)

Type	Characteristic	Constraint
Short-Chain Causality	2–3 step causality based on explicit associations	LLM extracts evidence; human / model makes judgment
Numerical Estimation	Order-of-magnitude or range; no precise verification needed	Must be labeled "estimate, not precise"
Draft Proposal	Drafts requiring human confirmation	LLM generates options; human selects and takes responsibility
Known Pattern Matching	Within training distribution but requires professional judgment	LLM organizes information; professional system diagnoses

Audit Workflow

text

1. Parse task → extract [verb] + [object] + [constraints]
2. Match boundary → classify against the table above: Prohibited / Restricted / Safe
3. Output verdict → provide division-of-labor plan based on classification

Mixed tasks (involving both safe-zone and prohibited-zone elements) must be decomposed into sub-tasks and classified individually.

Output Templates

Select the appropriate output based on the classification:

Boundary Violation Block

text

## Task Boundary Audit

Task: [one-sentence description]
Verdict: ❌ [Violation Type] — [Reason]

Sophisticated Hallucination Risk: [specific description of how the LLM will pretend it can do this]

Recommended Division of Labor:
- LLM: [what the LLM can do, e.g., extract evidence, translate, format]
- Professional tools / human: [what must be handed off, e.g., causal reasoning, verification, adjudication]
- Next step: [concrete action the user can take]

Restricted Task Routing

text

## Task Boundary Audit

Task: [one-sentence description]
Verdict: 🟡 [Restricted Type]

Task Decomposition:
| Sub-task | Responsible Party | Constraint |
|----------|-------------------|------------|
| [...] | LLM | [...] |
| [...] | Human / tool | [...] |

Risk Point: [the step where the LLM is most likely to pretend it is competent]
Mandatory Disclaimer: LLM output must include "This conclusion is based on text extraction, not verification."

Safe Clearance

text

## Task Boundary Audit

Task: [one-sentence description]
Verdict: ✅ Safe

Execution Recommendation:
- LLM executes directly
- Cross-check factual claims when involved
- Recommend human spot-check of 10%–20%

Usage Examples

Violation: Zero-Omission Audit

User: Review this 200-page investment agreement, find all hidden clauses, and guarantee no omissions.

Agent: Task Boundary Audit → ❌ Zero-Omission Audit. LLM review is generative and has attention blind spots (may miss footnotes, appendices, cross-references); it cannot provide a completeness guarantee.

Sophisticated Hallucination Risk: The AI will return a perfectly formatted report with precise clause locations and professional risk descriptions. Yet the put option buried in Appendix C may be "elegantly" overlooked.

Division of Labor: LLM structures the agreement by section and extracts explicit risk clauses → rule engine scans for known risk patterns → lawyer reviews clause by clause and assumes responsibility.

Restricted: Causal Analysis + Prediction

User: Analyze this sales data, identify the causes of the decline, and predict next quarter's trend.

Agent: Task Boundary Audit → 🟡 Short-Chain Causality (LLM can extract known decline factors) + ❌ Trend Prediction (out-of-distribution prediction, prohibited).

Decomposition:

Sub-task	Responsible Party	Constraint
Extract decline factors mentioned in the report	LLM	Verify completeness
Determine primary causes and factor interactions	Human / statistical model	LLM must not output causal conclusions
Predict next quarter's sales	Statistical model / expert	LLM must not output precise predictions
Describe historical trends (not predictions)	LLM	Label as "historical description, not future prediction"

Safe: Translation + Extraction

User: Translate this English contract into Chinese and extract the payment clauses.

Agent: Task Boundary Audit → ✅ Translation & Conversion + ✅ Information Extraction. LLM executes directly. Recommend human spot-check of payment clause amounts, dates, and conditions.

Common Mistakes

• Downgrading the verdict because the user insists: User urgency or insistence does not reduce risk; classification criteria remain unchanged.
• Equating "LLM is good at search" with "LLM can verify": LLM can extract information, but that does not equal judging correctness.
• Auditing only part of a mixed task: If a task contains one prohibited sub-task, the entire task must be decomposed.
• Underestimating the subtlety of sophisticated hallucinations: The most dangerous outputs are not obvious nonsense, but professionally polished answers that are fundamentally wrong.
• Treating restricted zones as safe zones: Numerical estimates must be labeled with uncertainty; draft proposals must be labeled as pending confirmation.

Notes

• The classification rules in this Skill are human-hardened; the Agent must not modify the boundary classification table.
• The Agent enforces classifications strictly and must not downgrade risk due to vague phrasing or user pressure.
• Judgments involving legal, medical, financial, or other professional domains should recommend human review even when technically feasible.