Agent skill

Web Tool Fallback

by HKUDS in HKUDS/OpenSpace

Implement fallback strategies when web-reading tools fail simultaneously

MITAuto-check passed

Install Web Tool Fallback

skills CLI
$ npx skills add HKUDS/OpenSpace --skill web-tool-fallback -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUDS/OpenSpace web-tool-fallback --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUDS/OpenSpace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/benchmarks/gdpval/skills/web-tool-fallback .claude/skills/web-tool-fallback && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
web-tool-fallback
GitHub stars
7.7k
Token cost
~1.9k tokens
SKILL.md length
461 words
Files
2
Skills in repo
199
Repo updated
First seen
Licence
MIT

At a glance

Implement fallback strategies when web-reading tools fail simultaneously

  • Works in 5 steps: Attempt Alternative Sources → Use execute_code_sandbox for Embedded… → Use execute_code_sandbox for Embedded… → …
  • SKILL.md covers When to Use This Skill, Recognition Criteria, Fallback Procedure and Decision Tree, plus 3 more sections
  • Calls python3; reaches web.archive.org

What it does

Web Tool Fallback is an agent skill from HKUDS/OpenSpace. Implement fallback strategies when web-reading tools fail simultaneously

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.

It works with Python. The repository describes itself as: "OpenSpace: The Skill Management Layer for AI Agents" -- https://open-space.cloud/. The licence is MIT.

Example prompts

  • “/web-tool-fallback”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Attempt Alternative Sources
  2. Use execute_code_sandbox for Embedded Knowledge
  3. Use execute_code_sandbox for Embedded Knowledge (May Fail)
  4. Create Document with Placeholder Citations
  5. Document the Limitation for Stakeholders

What it can do on your machine

Read from SKILL.md and the folder at commit 3827781. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • web.archive.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Web Tool Fallback loads about 1.9k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 461 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/OpenSpace at commit 3827781, republished under its MIT licence (© HKUDS). 461 words, ~1,898 tokens.

Download SKILL.mdSave it as .claude/skills/web-tool-fallback/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
web-tool-fallback
description
Implement fallback strategies when web-reading tools fail simultaneously

Web Tool Fallback Strategy

When to Use This Skill

Apply this skill when all web-reading tools (read_webpage, search_web) fail simultaneously with unknown errors during a research or information-gathering task.

Recognition Criteria

Confirm this pattern before applying fallbacks:

  • Multiple web tool calls have failed (not just one isolated failure)
  • Errors are unknown/unexpected (not expected 404s or rate limits that can be handled normally)
  • The information is still needed to complete the task

Fallback Procedure

Critical: execute_code_sandbox may also fail during system-wide issues. Always be prepared to immediately fall back to run_shell if sandbox execution returns errors.

Step 1: Attempt Alternative Sources

Before abandoning web access, try these alternatives:

  1. Alternative URLs:

    • Archive.org / Wayback Machine versions
    • Alternative domains (e.g., .org instead of .com)
    • Different subdomains or paths
  2. Simplified Requests:

    • Try reading just the domain root
    • Remove query parameters from URLs
    • Try HTTP instead of HTTPS (or vice versa)
python
# Example: Generate alternative URL formats
original_url = "https://example.com/research/report?id=123"
alternatives = [
    "https://example.com/research/report",
    "http://example.com/research/report?id=123",
    "https://web.archive.org/web/*/https://example.com/research/report"
]
Step 2: Use execute_code_sandbox for Embedded Knowledge
Step 2: Use execute_code_sandbox for Embedded Knowledge (May Fail)

When web access is unavailable, generate content from reliable embedded knowledge. Note: execute_code_sandbox can fail during cascading system issues—if it returns errors, immediately proceed to Step 2b.

python
code = '''
# Generate structured information from embedded knowledge
evaluation_frameworks = {
    "Kirkpatrick Model": ["Reaction", "Learning", "Behavior", "Results"],
    "Bloom's Taxonomy": ["Remember", "Understand", "Apply", "Analyze", "Evaluate", "Create"],
    "SMART Criteria": ["Specific", "Measurable", "Achievable", "Relevant", "Time-bound"]
}

# Create comprehensive reference material
for framework, levels in evaluation_frameworks.items():
    print(f"## {framework}\\n")
    for i, level in enumerate(levels, 1):
        print(f"{i}. {level}")
    print()
'''

If execute_code_sandbox returns any error (unknown error, timeout, execution failure):

  • Do not retry execute_code_sandbox multiple times
  • Immediately proceed to Step 2b (run_shell fallback)
  • Document the sandbox failure in your Research Access Notes
python
- Established frameworks and methodologies
- Standard best practices
- Well-documented technical concepts
- Historical information that doesn't change frequently

### Step 2b: Fall Back to run_shell if execute_code_sandbox Fails
### Step 2b: Fall Back to run_shell if execute_code_sandbox Fails (REQUIRED)

**When to use**: execute_code_sandbox returns any error OR fails to produce output within reasonable time.

**Action**: Immediately switch to run_shell with direct Python execution. Do not continue attempting execute_code_sandbox.

1. **Use run_shell with direct Python execution** (simplest, most reliable):

1. **Use run_shell with direct Python execution**:
   ```bash
   python3 -c "print('content from embedded knowledge')"
  1. Use heredoc for multi-line scripts:

    bash
    python3 << 'EOF'
    # Your Python code here
    frameworks = {"Kirkpatrick": ["Reaction", "Learning", "Behavior", "Results"]}
    for name, levels in frameworks.items():
        print(f"## {name}")
        for level in levels:
            print(f"- {level}")
    EOF
  2. Capture output to file for further processing:

    bash
    python3 -c "print('structured content')" > output.txt

Why run_shell works when execute_code_sandbox fails:

  • Direct system Python execution bypasses sandbox restrictions
  • No additional abstraction layer that could fail
  • More reliable during cascading tool failures
  • Should be your go-to when sandbox is compromised
Show full SKILL.md (199 more words)Show less
Step 3: Create Document with Placeholder Citations

For tasks requiring formal documentation:

  1. Draft the document using available knowledge
  2. Mark uncertain citations clearly:
    [WEB_ACCESS_LIMITED: URL https://example.com/source could not be accessed]
  3. Use placeholder format for references:
    [Reference needed: Topic - URL attempted but inaccessible]
Step 4: Document the Limitation for Stakeholders

Always create a transparent "Research Access Notes" section:

markdown
## Research Access Notes

**Access Limitation**: All web-reading tools failed simultaneously during research.

**Fallback Actions Taken**:
- [ ] Attempted alternative URLs: [Yes/No]
- [ ] Used embedded knowledge generation: [Yes/No]
- [ ] Created placeholders for citations: [Yes/No]

**Confidence Level**: [High/Medium/Low] - based on reliance on embedded knowledge vs. current web sources

**Recommendation**: Verify critical claims with current web sources when access is restored.

Decision Tree

All web tools failed?
├─ Yes → Try alternative URLs
│   ├─ Success → Complete task normally
│   └─ Failed → Can content be generated from embedded knowledge?
│       ├─ Yes → Try execute_code_sandbox (expect potential failure)
│       │   ├─ Success → Use output + document limitation
│       │   └─ Failed/Errors → **IMMEDIATELY** use run_shell + document limitation
│       └─ No → Create document with placeholders + notify Executive Director
│       └─ No → Create document with placeholders + notify Executive Director
└─ No → Handle individual failures normally

Task-Type Adaptations

Task TypePrimary FallbackDocumentation Required
Research/Analysisexecute_code_sandboxAccess notes section
Document CreationPlaceholder citationsLimitation notice to Executive Director
Time-SensitiveProceed with available methodsBrief limitation note
Compliance/LegalEscalate immediatelyFull access failure report

Example Application

python
# When gathering reference materials fails
try:
    # Primary: read_webpage attempts (all failed)
    pass
except:
    # Fallback 1: Try alternative sources
    alternative_content = execute_code_sandbox(generate_from_knowledge())
    
    # Fallback 2: Create document with transparency
    document = create_with_placeholders(
        content=alternative_content,
        citation_markers="[WEB_ACCESS_LIMITED]",
        stakeholder_note="Executive Director: Web access unavailable, content from embedded knowledge"
    )

Best Practices

  1. Be transparent: Always document when web access failed
  2. Preserve attempt records: Note which URLs/tools failed for future reference
  3. Distinguish knowledge types: Clearly separate embedded knowledge from current web-sourced information
  4. Prioritize critical info: If certain information is essential and cannot be generated, escalate rather than guess
  5. Enable verification: Make it easy for stakeholders to verify claims when access is restored
  6. Never stack failures: If execute_code_sandbox fails once, switch to run_shell immediately—do not retry the sandbox multiple times
  7. Complete all steps: Step 4 (Document Limitation) is mandatory, not optional—even if fallback succeeds

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in benchmarks/gdpval/skills/web-tool-fallback of HKUDS/OpenSpace.

  • SKILL.md
  • .skill_id

Open the folder on GitHubat commit 3827781

Compare with similar skills

Web Tool Fallback next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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NotebookLM Research AssistantPleasePrompto/notebooklm-skill7.8k13 repos~2.4kAutomated safety check: NotesMIT
Manim Video Productionbrowser-use/video-use28k6 repos~3kAutomated safety check: PassMIT
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT

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Works with

Questions about Web Tool Fallback

What does Web Tool Fallback do?

Implement fallback strategies when web-reading tools fail simultaneously. Web Tool Fallback is an agent skill from HKUDS/OpenSpace.

How do I install Web Tool Fallback in Claude Code?

Run `npx skills add HKUDS/OpenSpace --skill web-tool-fallback -a claude-code`. Or copy the skill folder (benchmarks/gdpval/skills/web-tool-fallback in HKUDS/OpenSpace) into .claude/skills/web-tool-fallback in your project. Claude Code loads it when a task matches its description.

How do I install Web Tool Fallback in Codex?

Run `npx skills add HKUDS/OpenSpace --skill web-tool-fallback -a codex`. Or copy the skill folder (benchmarks/gdpval/skills/web-tool-fallback in HKUDS/OpenSpace) into .agents/skills/web-tool-fallback in your project. Codex loads it when a task matches its description.

Can I use Web Tool Fallback in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/OpenSpace --skill web-tool-fallback -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/web-tool-fallback, .gemini/skills/web-tool-fallback, .github/skills/web-tool-fallback and .opencode/skills/web-tool-fallback in your project.

What does Web Tool Fallback need to run?

Going by SKILL.md and its folder, Web Tool Fallback needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Web Tool Fallback access the network?

SKILL.md names 1 domain. In commands or code: web.archive.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Web Tool Fallback safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Web Tool Fallback use?

Web Tool Fallback is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Web Tool Fallback use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Web Tool Fallback?

Skills that share tags, products or a category with Web Tool Fallback: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Web Tool Fallback?

HKUDS (a GitHub organization) maintains it in HKUDS/OpenSpace, which has 7,743 GitHub stars. The repository holds 199 skills in this directory. The repository was last updated on August 12, 2026.

Source: HKUDS/OpenSpace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.