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-report-enhanced-4bc5c2git 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-enhanced-4bc5c2)<a href="https://agentmods.dev/skills/hkuds/openspace/write-file-fallback-report-enhanced-4bc5c2"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/write-file-fallback-report-enhanced-4bc5c2.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 271 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 | $0.02368 |
| Opus 5 | $0.00013 | $0.01184 |
| Sonnet 5 | $0.00005 | $0.00474 |
| Haiku 4.5 | $0.00003 | $0.00237 |
Grade A, and why
context-anchored-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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context-Anchored Fallback Report Generation
Critical Warning: Context Drift Prevention
DO NOT ABANDON THE ORIGINAL TASK when tools fail. The most common failure mode is switching to an unrelated task after encountering tool errors.
CONTEXT_DRIFT_WARNING:
IF tool_failures occur (read_webpage, search_web, execute_code_sandbox)
THEN you MUST stay on the ORIGINAL task
AND you MUST NOT switch to unrelated tasks
AND you MUST generate output for the original goal using write_file
When to Use This Skill
Activate this workflow when multiple primary data source tools fail during document/report generation:
Trigger Decision Tree
START: Attempting to generate document/report
│
├─→ Tool #1 fails (read_webpage/search_web/read_file/execute_code_sandbox)?
│ │
│ ├─→ YES → Count = 1, Continue attempting
│ │
│ └─→ NO → Proceed normally with retrieved data
│
├─→ Tool #2 fails (same or different tool)?
│ │
│ ├─→ YES → Count = 2, TRIGGER FALLBACK NOW
│ │ │
│ │ ├─→ LOCK to original task goal (write it down)
│ │ ├─→ Stop attempting failing tools
│ │ └─→ Generate document with write_file + embedded knowledge
│ │
│ └─→ NO → Continue with 1-2 more attempts max
│
└─→ 2+ consecutive failures without document output?
│
└─→ CRITICAL ERROR: You are drifting from task. STOP and apply this skill.
Tool Failure Indicators
FALLBACK_TRIGGERS = {
"read_file": "returns binary/image data instead of text, or file not found",
"search_web": "returns unknown error, empty results, or rate limited",
"read_webpage": "returns unknown error, connection timeout, or blocked access",
"execute_code_sandbox": "fails with unknown error or timeout repeatedly",
"any_retrieval_tool": "2+ consecutive failures on data retrieval attempts"
}
Activation threshold: 2+ failures from the above triggers = IMMEDIATE fallback activation.
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 · 318 lines · 26 tokens per session scan A 385155f5f515
context-anchored-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 2,368 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.
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