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Cascade Fail Recovery

OrganizationPopular
HKUDS
cascade-fail-recovery

Handle cascading data retrieval tool failures by falling back to embedded knowledge generation

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill namecascade-fail-recovery
Stars
7.7K
Forks
918
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Cascade Fail Recovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/cascade-fail-recovery .claude/skills/cascade-fail-recovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cascade Fail Recovery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cascade Fail Recovery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cascade Fail Recovery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cascade Failure Recovery

Purpose

When multiple data retrieval tools (read_webpage, search_web) fail in succession, immediately fall back to generating content from embedded knowledge rather than attempting repairs or abandoning the task. This preserves task context and ensures completion.

When to Apply

Use this pattern when you observe:

  • 2+ consecutive failures from data retrieval tools
  • Network/access errors preventing web content retrieval
  • Risk of task abandonment due to tool errors

Recovery Procedure

Step 1: Recognize Cascading Failure

Identify the failure pattern:

Iteration N: read_webpage failed - connection error
Iteration N+1: search_web failed - timeout/unavailable
Iteration N+2: [risk] Agent may abandon task or switch objectives

Step 2: Preserve Task Context

Before switching strategies, explicitly restate the original objective:

ORIGINAL OBJECTIVE: [Restate the core task goal]
CONTEXT PRESERVED: [Key requirements, constraints, deliverables]

Step 3: Invoke Fallback Strategy

Immediately switch to embedded knowledge generation:

  1. Acknowledge the limitation: Note that external data sources are unavailable
  2. Activate internal knowledge: Use pre-trained knowledge relevant to the task
  3. Generate content: Use write_file to create the deliverable from available knowledge
  4. Document the fallback: Note in the output what information could not be verified externally

Step 4: Execute write_file

Generate the required document:

python
# Fallback to generating from embedded knowledge
write_file(
    path="output/document.md",
    content="[Generate content from internal knowledge base]"
)

Example Application

Scenario: PACT Act veterans benefits document needed, but web access failing

Wrong approach (observed failure):

Iter 11: read_webpage failed - access error
Iter 12: search_web failed - unavailable
Iter 13: [ABANDONED] Switched to unrelated musician payroll task

Correct approach (with this skill):

Iter 11: read_webpage failed - access error
Iter 12: search_web failed - unavailable
Iter 13: CASCADE FAIL DETECTED - invoking fallback
Iter 14: write_file - generate PACT Act document from embedded knowledge
      - Note: "External verification unavailable; content based on training knowledge"

Guidelines

  1. Threshold: Trigger fallback after 2 consecutive retrieval failures
  2. No endless retries: Do not attempt more than 1 repair/retry cycle
  3. Preserve objective: Never switch to unrelated tasks when tools fail
  4. Transparency: Clearly mark any content that lacks external verification
  5. Document limitations: Note what could not be verified due to tool failures

Related Skills

  • write-file-fallback: Generate documents when data sources unavailable
  • task-context-preservation: Maintain objective continuity through errors

Frequently asked questions

What does the Cascade Fail Recovery AI skill do?

Handle cascading data retrieval tool failures by falling back to embedded knowledge generation

Why use Cascade Fail Recovery on TypingMind?

Because you install it once and use it with any model. Cascade Fail Recovery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cascade Fail Recovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/cascade-fail-recovery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cascade Fail Recovery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cascade Fail Recovery?

As many as you like. As long as a model supports skills, you can use Cascade Fail Recovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cascade Fail Recovery AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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