Prioritising control remediation
Identifies processes where failed controls coincide with recorded incidents. Gives teams an evidence-based view of residual risk hotspots to support remediation priorities.
— Operations & Process
Control failure → incident RCA pattern engine
Joins control test results to incident logs by process to show where control failures are actually producing incidents, grouped by systemic weakness with residual risk quantified.
- Operations & Process
- Traceable reasoning
- Runs anywhere
1 week → ~25 minutes
For a full population, not a sample
No coding required
- Input
- Control test results, including control, process, test date, result and deficiency
- Output
- Process-level analysis linking control failures and incident evidence
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1 week per run
— USE CASES
What people use Control failure → incident RCA pattern engine for
Reviewing recurring operational incidents
Groups control deficiencies by the systemic weaknesses they suggest and relates those patterns to incident root causes. Helps teams assess whether repeated incidents point to broader control weaknesses.
Briefing risk oversight teams
Provides a consolidated diagnosis of control failures, associated incidents and concentrated residual risk. Gives oversight discussions a clear account of the patterns that warrant attention.
Assessing process control effectiveness
Examines control test results alongside incident frequency and severity within each process. Highlights where recorded failures are associated with operational harm rather than test deficiencies alone.
What it works from
- Control test results, including control, process, test date, result and deficiency
- Incident records, including incident, date, process, root cause and severity
- Process identifiers shared across control and incident records
What you get back
- Process-level analysis linking control failures and incident evidence
- Systemic weakness assessments for failed controls
- Grouped analysis of recurring weakness patterns
- Quantified residual risk hotspots with supporting visualisations
— HOW IT BEHAVES
How Control failure → incident RCA pattern engine produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Every row of the end-to-end process
Each row of your export is processed on the same basis, so no process step is skipped however long the table is.
The pattern charted
Movement across the end-to-end process is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the end-to-end process are written up as a document that reads like professional output, with each claim tied back to a process step.
Each process step enriched in place
Derived columns are added row by row, keeping your source data and the judgement about each process step side by side.
Why this is expensive by hand
Control testing and incident reviews often sit apart, making it difficult to distinguish isolated deficiencies from weaknesses associated with recurring operational harm. Prioritising control remediation is the typical trigger — identifies processes where failed controls coincide with recorded incidents. Gives teams an evidence-based view of residual risk hotspots to support remediation priorities. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 1 week of experienced attention.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is process-level analysis linking control failures and incident evidence, with systemic weakness assessments for failed controls. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. Net effect: 1 week down to ~25 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Who it's for
- Operations and continuous improvement leads
- Transformation and automation teams
- Service delivery and shared services managers
- Consultants running process diagnostics
Run it in Skillsize — or export it anywhere
Control failure → incident RCA pattern engine exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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