Skip to content
cccskills
FreePublished on GitHub

detect-zero-iteration-failures

Analyze agent runs that fail before any work begins. By checking execution counts, tool calls, outputs, and conversation logs, this skill classifies startup failures and directs investigation toward system setup, resources, or input processing.

OriginalShow the original description

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

See how to install

Good for

  • Investigating failed runs with zero execution steps
  • Classifying failures with no tool calls or outputs
  • Routing startup incidents to infrastructure teams
  • Reviewing resource limits and input processing

Files included (2)

  • SKILL.md5.1 KB
  • .skill_id44 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

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

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

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.

  • Match audio parts with different durations
  • Extend drums and melodic parts using suitable methods
  • Check audio specifications before mixing
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.

  • Align instrument tracks to a reference audio file
  • Choose how to extend tracks that are too short
  • Verify audio properties after generation and effects
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Build serverless endpoints that relay external API requests for a dashboard while keeping API keys on the server. Includes Vercel examples for input checks, caching, cross-origin access, and consistent JSON responses.

  • Connect a dashboard to external APIs without exposing API keys
  • Create endpoints for stock quotes and news
  • Standardize API responses and error handling
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.

  • Export a master track with separate audio parts
  • Apply filters and reverb to audio
  • Check duration and format across deliverables
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.

  • Continue drafting after repeated web retrieval failures
  • Preserve task requirements during network errors
  • Mark information that could not be externally verified
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.

  • Retry Python work through the shell after repeated sandbox failures
  • Run longer tasks with an adjusted timeout
  • Execute Python work that needs multiple files or system utilities
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

See all skills by HKUDS

Report a problem with this skill