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 write-file-fallback-reportgit 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/write-file-fallback-report)<a href="https://agentmods.dev/skills/hkuds/openspace/write-file-fallback-report"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/write-file-fallback-report.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.00950 |
| Opus 5 | $0.00013 | $0.00475 |
| Sonnet 5 | $0.00005 | $0.00190 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
write-file-fallback-report 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 5d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write-File Fallback Report Generation
When to Use This Skill
Use this workflow when attempting to generate a document or report, but multiple primary data source tools fail simultaneously:
read_filereturns binary/image data instead of text (common with PDFs)search_webreturns errors or no resultsexecute_code_sandboxfails unexpectedly- Other data retrieval tools are unavailable
Key insight: Rather than getting stuck on failed data retrieval, pivot immediately to generating the document directly with write_file using professionally structured content and embedded domain knowledge.
Step-by-Step Instructions
Step 1: Detect Tool Failure Pattern
Recognize when you're in a fallback scenario:
TOOL_FAILURE_INDICATORS = [
"read_file returns binary or image data",
"search_web returns unknown error or empty results",
"execute_code_sandbox fails repeatedly",
"Multiple consecutive tool failures on data retrieval"
]
Decision point: If 2+ indicators are present, proceed to Step 2.
Step 2: Pivot to Write-File-First Approach
Stop attempting to fix the failing tools. Instead:
- Acknowledge the limitation briefly in your output
- Commit to generating the document with available knowledge
- Use
write_fileas your primary tool (not a last resort)
Step 3: Structure the Document Professionally
Create a well-organized markdown document with:
# [Document Title]
## Executive Summary
[Brief overview of key findings/content]
## Background
[Context and scope - use embedded knowledge]
## Main Content
[Organized sections with headers, lists, tables as appropriate]
## Limitations & Notes
[Transparent about data source limitations if relevant]
## Recommendations/Next Steps
[Actionable guidance based on available information]
Step 4: Leverage Embedded Domain Knowledge
When external data is unavailable:
- Use general domain knowledge appropriately
- Clearly distinguish between verified facts and general guidance
- Include actionable frameworks rather than specific unverified data
- Add placeholder notes where specific data would enhance the document
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.
- 5d ago First seen · 149 lines · 26 tokens per session scan A e24528efde13
write-file-fallback-report is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 26d ago), licensed MIT. It adds 26 tokens to every session and 950 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
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
baoyu-youtube-transcript
A tool for downloading the written captions, subtitles, chapter information, speaker labels, and cover image from a YouTube video using its URL or ID.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
read
Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local…
overleaf-sync
A two-way connection between a local paper folder and Overleaf, a web-based LaTeX editor for writing research papers. It lets you move changes between the local files and the shared Overleaf project.