Live delivery change control
Assess incoming requests against the signed scope in minutes so decisions are not deferred to the next steering meeting.
— Operations & Process
Assesses a change request against the current SOW, quantifies scope, cost, timeline and risk deltas, scores materiality, and produces the matching assessment or approval note.
2–3 days → ~15 minutes
For one complete, review-ready pass
No coding required
— USE CASES
Assess incoming requests against the signed scope in minutes so decisions are not deferred to the next steering meeting.
Quantify cost and timeline deltas before agreeing to something that quietly erodes the fee.
Produce the same approval wording and rationale every time, ready for the client's governance process.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Assessment happens against criteria you control and weight, so the same standard applies on every run.
The path taken depends on what the analysis found, so thin cases and strong cases are handled differently by design.
Findings are written up as a document that reads like professional output rather than raw model text.
Not every change request deserves a formal impact assessment, but deciding which do is where scope creep hides. In practice it shows up as live delivery change control: assess incoming requests against the signed scope in minutes so decisions are not deferred to the next steering meeting. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 2–3 days of capacity for it.
Here the same job runs as a Skill. Your material goes in; scope, cost, timeline and risk deltas made explicit comes out, alongside a materiality score that decides the depth of assessment. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. Net effect: 2–3 days down to ~15 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Change-request impact assessment 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.
Automation opportunity map
Maps team or function activities to automation potential, computing addressable effort and bucketing opportunities into a prioritised roadmap.
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.
Process diagnostic
Inventories every process step with its owner, system, effort and wait time, tests each against bottleneck, duplication, handoff and control-gap lenses, and returns a prioritised 30/60/90 improvement backlog.