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Detect Zero Iteration Failures

OrganizationPopular
HKUDS
detect-zero-iteration-failures

Identify and handle pre-execution agent failures occurring before any iterations or tool usage

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill namedetect-zero-iteration-failures
Stars
7.7K
Forks
918
Bundled files
Instructions only
LicenseMIT
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 HKUDS on GitHub. Read the source before you install it.

Installation

Install the Detect Zero Iteration Failures 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/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/detect-zero-iteration-failures .claude/skills/detect-zero-iteration-failures
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Detect Zero Iteration Failures 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 Detect Zero Iteration Failures 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 Detect Zero Iteration Failures 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.

Detect Zero-Iteration Failures

This skill helps identify and properly categorize agent failures that occur before any task execution begins. These failures require system-level investigation rather than agent-level debugging.

When to Apply

Use this skill when analyzing agent task executions where:

  1. Iteration count is 0 - The agent completed zero iterations
  2. No tool usage - No tools were invoked during the execution
  3. No artifacts created - No files, documents, or outputs were produced
  4. Minimal or no conversation log - Only the user instruction exists, with no agent responses

Identification Checklist

Check the following indicators to confirm a zero-iteration failure:

[ ] Iterations reported: 0
[ ] Tool invocations: None
[ ] Files created: None  
[ ] Conversation turns: 1 (user instruction only)
[ ] Agent self-report: Indicates failure before execution began

Failure Categories

Zero-iteration failures typically indicate one of these pre-execution issues:

CategoryDescriptionInvestigation Focus
Initialization crashAgent failed during setupSystem logs, environment config
Environment issueMissing dependencies or resourcesInfrastructure, permissions
Prompt parsing errorInput could not be processedPrompt format, encoding
Resource exhaustionQuota or limits exceededSystem capacity, rate limits

Diagnostic Steps

Step 1: Verify Zero-Iteration Indicators

python
def is_zero_iteration_failure(execution_log):
    """Check if execution shows zero-iteration failure pattern."""
    indicators = {
        'iterations': execution_log.get('iterations', 0) == 0,
        'tools_used': len(execution_log.get('tool_calls', [])) == 0,
        'artifacts': len(execution_log.get('files_created', [])) == 0,
        'conversation_length': len(execution_log.get('messages', [])) <= 1
    }
    return all(indicators.values())

Step 2: Check System-Level Indicators

Look for these error patterns in system logs:

  • Environment errors: Missing packages, permission denied, path not found
  • Initialization errors: Connection failures, timeout during setup
  • Resource errors: Memory exceeded, quota limits, rate limiting
  • Parsing errors: Invalid JSON, encoding issues, malformed input

Step 3: Route Investigation Appropriately

IF zero-iteration failure detected:
    └── Do NOT debug agent logic or prompt instructions
    └── DO investigate:
        ├── System initialization logs
        ├── Environment configuration
        ├── Resource allocation and limits
        └── Input parsing and validation

Response Actions

For Analysts

  1. Flag as system-level issue - Do not attribute to agent behavior
  2. Check infrastructure health - Verify system components are operational
  3. Review recent changes - Look for deployments, config updates, or quota changes
  4. Escalate appropriately - Route to infrastructure/platform team, not agent developers

For Automated Systems

python
def categorize_failure(execution_log):
    """Categorize failure type for routing."""
    if is_zero_iteration_failure(execution_log):
        return {
            'category': 'PRE_EXECUTION_FAILURE',
            'severity': 'HIGH',
            'investigation_team': 'INFRASTRUCTURE',
            'agent_debug_required': False,
            'recommended_actions': [
                'Check system initialization logs',
                'Verify environment configuration',
                'Review resource quotas and limits',
                'Validate input parsing pipeline'
            ]
        }
    else:
        return {
            'category': 'EXECUTION_FAILURE',
            'severity': 'MEDIUM',
            'investigation_team': 'AGENT_DEVELOPMENT',
            'agent_debug_required': True
        }

Example Analysis

Zero-Iteration Failure Example:

Task ID: 69a8ef86-phase1
Iterations: 0
Tools Used: None
Files Created: None
Messages: [User instruction only]
Agent Report: "Failed before executing any iterations"

Analysis: PRE_EXECUTION_FAILURE
- No agent logic was executed
- Failure occurred during initialization
- Action: Investigate system environment, not agent prompts

Normal Execution Failure (for contrast):

Task ID: abc123
Iterations: 3
Tools Used: [read_file, write_file, shell_agent]
Files Created: [output.txt]
Messages: [User instruction, Agent response x3]
Agent Report: "Could not complete task due to X"

Analysis: EXECUTION_FAILURE
- Agent logic was executed
- Failure occurred during task performance
- Action: Debug agent reasoning and tool usage

Key Takeaways

  1. Zero iterations = Pre-execution failure - The agent never got to work
  2. System-level, not agent-level - Debug infrastructure, not prompts
  3. High severity - Indicates potential systemic issues affecting multiple tasks
  4. Distinct failure mode - Treat separately from normal execution failures

Frequently asked questions

What does the Detect Zero Iteration Failures AI skill do?

Identify and handle pre-execution agent failures occurring before any iterations or tool usage

Why use Detect Zero Iteration Failures on TypingMind?

Because you install it once and use it with any model. Detect Zero Iteration Failures 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 Detect Zero Iteration Failures in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/detect-zero-iteration-failures. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Detect Zero Iteration Failures?

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 Detect Zero Iteration Failures?

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

Is the Detect Zero Iteration Failures AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. 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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