PBC and request-list tracking
Map a client's uploaded folder against the request list to see what is genuinely satisfied before you start testing.
— Risk & Compliance
Audit evidence mapping
Maps an uploaded evidence set against a list of assertions or PBC items to produce a coverage matrix, flagging unsupported assertions and orphaned documents.
- Risk & Compliance
- Traceable reasoning
- Runs anywhere
1 week → ~10 minutes
For a typical multi-document review workflow
No coding required
- Input
- The full evidence folder as a document set
- Output
- A document-by-assertion coverage matrix
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1 week per run
— USE CASES
What people use Audit evidence mapping for
Controls walkthrough preparation
Establish coverage per assertion so fieldwork time goes to the gaps rather than to re-reading complete files.
Evidence hygiene reviews
Surface stale or orphaned documents that support no assertion and can be dropped from the file.
What it works from
- The full evidence folder as a document set
- The audit assertions or request items in scope
- Any naming or referencing conventions you use
What you get back
- A document-by-assertion coverage matrix
- A named list of unsupported assertions
- A list of evidence that maps to nothing
— HOW IT BEHAVES
How Audit evidence mapping produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Coverage, both directions
A coverage pass shows what is supported and what is not — including requirements with nothing behind them and material that supports nothing.
Whole document sets at once
A folder of material is processed as one set, so cross-document patterns surface instead of being read one file at a time.
Composed as work product
Findings are written up as a document that reads like professional output rather than raw model text.
Why this is expensive by hand
The slow part of fieldwork is proving which document supports which assertion. PBC and request-list tracking is the typical trigger — map a client's uploaded folder against the request list to see what is genuinely satisfied before you start testing. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 1 week of manual work.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces a document-by-assertion coverage matrix plus a named list of unsupported assertions. 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. The practical effect: 1 week of manual work becomes a ~10 minutes run, held to an identical standard on the tenth engagement as on the first.
Who it's for
- Independent consultants codifying their own methodology
- Strategy and transformation teams standardising delivery
- Internal advisory functions under pressure to produce faster
- Operators who need defensible output, not a one-off chat answer
Run it in Skillsize — or export it anywhere
Audit evidence mapping 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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