A high-integrity debugging skill that mandates evidence-based bug localization through the unified analysis of source code, databases, server logs, and platform queries.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install multi-capability-bug-closure
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 multi-capability-bug-closure 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 Multi-Capability Bug Closure?
Multi-Capability Bug Closure is a rigorous diagnostic framework designed for AI agents within the Openclaw Skills ecosystem. Unlike standard debugging that relies solely on static code analysis, this skill enforces a closed-loop verification process. It requires the agent to actively call upon real-world data sources—including production logs and database records—to build an immutable chain of evidence before proposing any fix.
By integrating multiple technical layers, this skill ensures that conclusions are not just theoretical but are backed by actual runtime behavior. It is specifically engineered for scenarios where users demand high-confidence root cause analysis based on authentic data rather than architectural assumptions.
Multi-Capability Bug Closure Use Cases
- Investigating intermittent production issues that cannot be replicated by code review alone.
- Performing root cause analysis (RCA) that requires correlating server logs with database state.
- Validating business logic errors across multi-platform integrations.
- Generating audit-ready bug closure reports with a complete chain of evidence.
How Multi-Capability Bug Closure Works
- Problem Structuring: The skill extracts key identifiers such as trace IDs, SNs, service names, and error timestamps.
- Log Evidence Collection: It fetches raw logs within the target window to identify exception frequencies and patterns.
- Database Verification: The agent executes read-only SQL queries to check object states and cross-reference relationships.
- Mechanism Interpretation: Source code is analyzed to explain the logic path triggered by the observed data.
- Platform Validation: External API states or business configurations are verified to ensure platform-side alignment.
- Evidence Synthesis: All data points are merged into a unified diagnostic chain.
- Conclusion & Remediation: A final cause is determined with a specific confidence level and actionable fix suggestions.
Multi-Capability Bug Closure Setup
To use this Openclaw Skills capability, ensure your environment has access to the following four core systems. Use the following commands to initialize required MCP (Model Context Protocol) tools if they are missing:
# Example: Adding database capability for Postgres
openclaw install mcp-postgres
# Example: Adding server log analysis capability
openclaw install server-log-analysis
Prerequisites Checklist:
- Read access to at least one core service/route file.
- Ability to execute
SELECT 1or equivalent on the target database. - Access to the target server log directories or sample logs.
- Connectivity to relevant business platform APIs.
Multi-Capability Bug Closure Data Schema & Taxonomy
The skill organizes findings into a structured evidence chain. The following schema is used for metadata taxonomy:
| Evidence Layer | Data Type | Key Attributes |
|---|---|---|
| Logs | Time-series | Frequency, Service Name, Exception Stack |
| Database | Relational | Table State, Timestamp, Relationship Integrity |
| Code | Static/Logic | Branch Conditions, Error Enums, Logic Paths |
| Platform | Configuration | API Status, Business Rules, Env Variables |
All files generated during analysis follow a 定位 -> 论证 -> 结论 -> 建议 (Location -> Argument -> Conclusion -> Recommendation) lifecycle.
Multi-Capability Bug Closure Advanced Features
- Closed-Loop Verification: Automatically flags a lack of evidence and pauses conclusions until all data gaps are filled.
- Confidence Scoring: Assigns High/Medium/Low confidence levels to root cause theories based on the strength of the data chain.
- Security-First Handling: Supports secret management for sensitive credentials, ensuring no plaintext keys are leaked in output logs.
- Multi-Agent Support: Can coordinate with specialized MCP tools for Postgres, Redis, or SQLite to perform cross-cluster verification.
SKILL.md
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