A systematic framework to perform Correction of Error (COE) and postmortem analysis for recurring failures, silent successes, and fragile automation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install coe-root-cause
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 coe-root-cause 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 Correction of Error (COE) Root Cause Analysis?
The COE Root Cause skill is a structured framework designed to help development teams and autonomous agents run rigorous postmortems and Correction of Error (COE) analyses. Instead of settling for surface-level explanations, this tool forces a deep dive into systematic failures like false successes, silent data loss, and fragile automation pipelines. By implementing this skill, developers can isolate the exact technical mechanisms of a failure, establish robust corrective actions, and verify resolutions to prevent regression.
As part of the Openclaw Skills ecosystem, this postmortem tool is optimized to generate comprehensive markdown-based COE reports that outline timelines, the 5 Whys method, concrete evidence logs, and explicit verification gates. It provides a highly action-oriented approach to maintaining system stability and automation reliability.
Correction of Error (COE) Root Cause Analysis Use Cases
- Investigating recurring or systemic pipeline failures that standard debugging loops fail to permanently resolve.
- Diagnosing instances of "false success" where a workflow silently reported a successful execution despite missing required outputs.
- Conducting postmortem reviews following user-visible misses or critical data loss in automated workflows.
- Transforming fragile, ad-hoc scripts and brittle commands into durable, guardrail-protected skills.
- Generating formalized, stakeholder-ready postmortem documentation with precise timelines and action items.
How Correction of Error (COE) Root Cause Analysis Works
- Classify the Failure Mode: Identify the exact failure profile (e.g., visible failure, false success, incomplete work) and identify contributing conditions such as rate limits, schema drift, or absent verification gates before changing any system configuration.
- Compile the Evidence Packet: Gather a minimal, high-signal packet containing user expectations, actual behavior, first bad observable result, and affected code paths while strictly redacting sensitive customer data.
- Execute the 5 Whys Analysis Loop: Build a sequential timeline of events and iterate through the 5 Whys methodology to separate immediate proximate causes from deep, structural root causes.
- Formulate Corrective Actions: Define ownership-mapped safeguards, expected future detection signals, and clear status fields for every planned systemic fix.
- Apply Verification Gates: Execute a dry run, a targeted regression test, or a static validation gate to ensure the corrective action functions properly and prevents repeat failures.
- Generate the Postmortem Report: Auto-generate a markdown report based on the official COE template for the team's repository.
Correction of Error (COE) Root Cause Analysis Setup
To install and enable this skill within your workspace, configure it as an active workflow script or load it into your agent's runtime environment.
# Download the COE skill definition into your agent workspace
mkdir -p .agent/skills
curl -o .agent/skills/coe-root-cause.md https://raw.githubusercontent.com/openclaw/skills/main/coe-root-cause.md
Ensure that your agent is configured to recognize the trigger keywords such as "COE", "Correction of Error", "postmortem", or "why did this recur". No secondary third-party dependencies or API keys are required to use this analysis workspace.
Correction of Error (COE) Root Cause Analysis Data Schema & Taxonomy
The skill organizes postmortem data using a highly structured, plain-text markdown schema. Below is the anatomy of the generated COE report format:
| Section | Description | Target Data/Requirements |
|---|---|---|
| Metadata | Baseline status | High/Medium/Low severity, overall status (done/planned/blocked), and date |
| Summary & Impact | Executive summary | One paragraph on what failed and why it mattered, specifying exact affected scopes |
| Timeline | Chronological map | Timestamped sequence of events leading up to and following the failure |
| Failure Classification | Failure profiling | Defined primary failure mode and verified contributing factors |
| Evidence | High-signal data | References to sanitized logs, error output, or code repositories |
| Root Cause & 5 Whys | Deep-dive analysis | Step-by-step breakdown using the 5 Whys to uncover the core system vulnerability |
| Corrective Actions | Resolution roadmap | Table containing tasks, owners, implementation status, and verification evidence |
| Verification & Residual Risk | Closeout verification | Proof that the fix was validated and a summary of remaining edge-case risks |
This format ensures all analytical steps are permanently archived alongside your codebase.
Correction of Error (COE) Root Cause Analysis Advanced Features
- Automatic Failure Classification: Programmatically maps issues into core taxonomies to prevent superficial diagnoses like "human error" or "model mismatch".
- Sanitized Evidence Collection: Enforces automated redaction of API secrets, personal data, and internal workspace credentials before generating public-facing postmortems.
- Strict Rule-Based Analysis Gates: Rejects generic, non-actionable root causes (e.g., "the agent forgot") in favor of durable fixes like structural validators or workflow gates.
- Verification Enforcement: Every corrective action maps directly to verifiable, automated tests, dry-runs, or static validation scripts ensuring no issue is closed out unproven.
- Seamless Integration with Openclaw Skills: Easily couples with adjacent diagnostic or code-generation skills to automatically build and run the regression tests identified during the analysis.
SKILL.md
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