OpenSpace is a skill-management layer for AI agents that stores, retrieves, evaluates, shares, and improves reusable workflows. It is intended for people using multiple coding agents who want skills to be reused and refined based on task outcomes. The catalogue provides 200 skills for use with OpenSpace and the agents it supports.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HKUDS/OpenSpace --skill detect-zero-iteration-failuresgit clone --depth 1 https://github.com/HKUDS/OpenSpaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkuds/openspace/detect-zero-iteration-failures)<a href="https://agentmods.dev/skills/hkuds/openspace/detect-zero-iteration-failures"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/detect-zero-iteration-failures/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/openspace/detect-zero-iteration-failures"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/detect-zero-iteration-failures.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.01093 |
| Opus 5.5 | $0.00009 | $0.00437 |
| Sonnet 5.5 | $0.00005 | $0.00219 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
detect-zero-iteration-failures scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Iteration count is 0 - The agent completed zero iterations
- No tool usage - No tools were invoked during the execution
- No artifacts created - No files, documents, or outputs were produced
- 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:
| Category | Description | Investigation Focus |
|---|---|---|
| Initialization crash | Agent failed during setup | System logs, environment config |
| Environment issue | Missing dependencies or resources | Infrastructure, permissions |
| Prompt parsing error | Input could not be processed | Prompt format, encoding |
| Resource exhaustion | Quota or limits exceeded | System 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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 152 lines · 23 tokens per session scan A 963d701eb2e6
detect-zero-iteration-failures is a skill published in the GitHub repository HKUDS/OpenSpace (7,760 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 1,093 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
incident-response-incident-response
Coordinate production incident triage, mitigation, investigation, recovery, and follow-up. Use when responding to outages or other incidents that require structured operational coordination.
cwv-optimizer
Diagnose and fix Core Web Vitals issues on AEM Edge Delivery Services pages. Goes deeper than generic CWV advice by understanding EDS-specific performance patterns including the 100KB LCP budget, E-L-D loading phases, block rendering behavior, and third-party script impact. Produces specific fixes for LCP, CLS, and…