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 verify-success-after-unknown-errorgit 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/verify-success-after-unknown-error)<a href="https://agentmods.dev/skills/hkuds/openspace/verify-success-after-unknown-error"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/verify-success-after-unknown-error.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 127 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00026 | $0.00976 |
| Opus 5 | $0.00013 | $0.00488 |
| Sonnet 5 | $0.00005 | $0.00195 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
verify-success-after-unknown-error 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 4d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify Success After Unknown Error
Purpose
When execute_code_sandbox or run_shell return "unknown error" messages, the underlying task may have actually succeeded. This skill provides a systematic approach to verify actual completion before assuming failure and retrying unnecessarily.
When to Apply
Use this pattern when:
execute_code_sandboxreturns "unknown error" but your code may have completedrun_shellfails with unclear error messages- File creation, modification, or transformation tasks report errors
- The error message is generic/unspecified rather than a clear failure reason
Verification Steps
Step 1: Check Expected Output Files
After receiving an unknown error, immediately verify if expected files were created:
# Example: Verify file creation after execute_code_sandbox
from tools import list_dir, read_file
# List directory to check if files exist
files = list_dir(path="/workspace/output")
print(files)
# Check specific file existence
expected_files = ["report.pdf", "data.xlsx"]
for f in expected_files:
try:
content = read_file(filetype="pdf", file_path=f"/workspace/output/{f}")
print(f"✓ {f} exists and is readable")
except:
print(f"✗ {f} not found or unreadable")
Step 2: Validate File Content/State
Don't just check existence — verify the files have expected content:
# For spreadsheets
file_content = read_file(filetype="xlsx", file_path="/workspace/output/schedule.xlsx")
# Verify expected sheets, columns, or data exist
# For text/json files
file_content = read_file(filetype="txt", file_path="/workspace/output/result.json")
# Parse and validate structure
# For directories
dir_contents = list_dir(path="/workspace/output")
# Verify expected number of files or specific files exist
Step 3: Decision Logic
IF expected files exist AND content is valid:
→ Task succeeded despite error message
→ Proceed to next step without retry
ELIF files exist but content is incomplete:
→ Partial success, may need targeted fix
ELSE (files missing or corrupted):
→ True failure, retry or debug required
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.
- 4d ago First seen · 137 lines · 26 tokens per session scan A 2d886251aa7e
verify-success-after-unknown-error is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 26d ago), licensed MIT. It adds 26 tokens to every session and 976 once invoked, about $0.0001 per session on Opus 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 2026-09-03.
Other skills, from other repositories
material-ui-nextjs
Integrates Material UI with Next.js App and Pages routers using @mui/material-nextjs, Emotion cache providers, next/font, CSS layers with Tailwind/CSS Modules, Link component prop patterns, CSS theme variables SSR notes, and App Router useSearchParams + Suspense. Use when setting up or debugging MUI in a Next.js app.
RootCauseAnalysis
Structured incident investigation using Five Whys, Fishbone, blameless Postmortem, Fault Tree, Kepner-Tregoe, and FMEA — traces failures to systemic root causes rather than blaming humans. USE WHEN root cause, RCA, 5 whys, fishbone, postmortem, incident analysis, fault tree, why does this keep failing, blameless…
Vitals
Read-only macOS performance inspection: a deterministic CLI gathers the numbers, Interpretation.md turns them into a diagnosis instead of a data dump.
benchmark-workflow
Run, diagnose, or change Xberg extraction benchmarks, quality scoring, benchmark fixtures, artifact contracts, and independently sourced ground truth. Load for the Benchmarks workflow or benchmark-harness work, not ordinary unit tests.
debugging-executions
Debug failed or wrong-output workflow executions using executions tools. Load when the user reports execution failures, unexpected node output, empty parameter values after a successful run, or a node showing a red or failed expression error.
ai-development-guide
Applies language-agnostic and backend technical decision criteria, anti-pattern detection, debugging, and quality gates. Use when reviewing general/backend implementation choices, code smells, failures, or implementation completeness.