An AI-driven operational workflow skill that leverages learned insights from evaluation videos to execute step-by-step automation sequences.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install speak-69378f
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install speak-69378f using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is speak-69378f?
The speak-69378f skill is a specialized automation component designed for the OpenCode platform ecosystem. It acts as an operational blueprint synthesized directly from multimedia learning sources, specifically targeting evaluation breakdowns, comprehensive product unboxing reviews, and structured workflows. By analyzing behavioral metadata and practical application steps from curated sources, this skill allows AI agents to replicate structured, step-by-step logic frameworks in real time.
Built natively as part of Openclaw Skills, this asset bridges the gap between passive video-based technical content and active agent execution. It encapsulates core concepts, key execution techniques, and reference demonstrations into machine-readable triggers, helping developers scale automated evaluations, interface workflows, and user assistance pipelines with minimal friction.
speak-69378f Use Cases
- Automated Product Evaluation: Replicating granular unboxing, benchmarking, and real-world testing processes based on user reviews.
- Dynamic Contextual Assistance: Providing targeted assistance and operational steps when a user requests system help regarding technical evaluation processes.
- Iterative Workflow Adaptation: Executing continuous optimization loops derived from multi-source video intelligence and automated structural merges.
How speak-69378f Works
- Trigger Monitoring: The skill listens for explicit execution phrases or context matches, including designated keywords and evaluation topics.
- Conceptual Parsing: Maps the active incoming request to the underlying core concepts parsed during the video intelligence compilation phase.
- Step-by-Step Execution: Executes an operational sequence spanning foundational comprehension, key technique injection, and reference demonstration mapping.
- Adaptive Refinement: Pulls localized multi-source knowledge (such as merged variant intelligence profiles) to dynamically resolve ambiguities and finalize the output.
speak-69378f Setup
Integrate speak-69378f into your local agent workspace by adding it to your Openclaw Skills manifest. Execute the following CLI commands to install and register the workflow package:
# Initialize the skill within your Openclaw workspace
openclaw install speak-69378f
# Run a compatibility check with your local opencode runtime environment
openclaw check --skill speak-69378f
# Test execution across primary intent triggers
openclaw test --trigger "speak"
speak-69378f Data Schema & Taxonomy
The speak-69378f skill relies on an integrated metadata taxonomy that aggregates knowledge blocks from multiple multimedia channels. It organizes its operational structure based on the following framework:
| Attribute | Description | Data Type / Value |
|---|---|---|
name |
Unique Identifier for the automation asset | speak-69378f |
compatibility |
Host system runtime standard | opencode |
trigger |
Registered intent matching keywords | String / Array Array |
tags |
Functional categorizations for service routing | [workflow, speak, video-learned] |
Core Knowledge Merging Schema
The data model uses unified reference trees derived from automatic merging routines (speak-69378f-d825d8), appending source parameters such as author metadata, timestamps, and platform links (bilibili) to enrich execution authenticity.
speak-69378f Advanced Features
- Multi-Source Cross-Merging: Automatically merges separate video learning profiles to synthesize complementary execution pathways under a single trigger matrix.
- Intent Variant Redirection: Routes runtime requests gracefully across multiple linguistic variants and colloquial phrasing defaults.
- Contextual Knowledge Injection: Embeds explicit external tracking states and video reference URLs directly into runtime variables for real-time validation checks.
SKILL.md
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