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Process Audit

Community
davekilleen
process-audit

Use when an operational process needs its start, end, owner, outcome, representative sample, measured queues or handoffs, bottleneck evidence, or controlled improvement experiment made explicit.

Overview

Publisherdavekilleen
RepositoryDex
Skill nameprocess-audit
Stars
481
Forks
130
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by davekilleen on GitHub. Read the source before you install it.

Installation

Install the Process Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/_available/operations/process-audit .claude/skills/process-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Process Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Process Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Process Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Process audit

When to use

Use this skill when a repeatable process needs an evidence-backed health audit, measured handoff and rework analysis, bottleneck diagnosis, or a controlled improvement experiment.

Do not use it to change a live process, assign blame, impose a policy, or declare an improvement successful from opinions alone. Not for a process with no explicit start, end, owner, outcome, or observable sample unless those gaps are reported as unknown.

Inputs and source discipline

Define the process boundary with an explicit start event, end event, owner, and desired outcome. Record the review window, timezone, scope, as-of date/time, and source date for the process definition. If ownership or outcome is not evidenced, use TBD or unknown; do not infer it from a job title or the person who supplied notes.

Build a source ledger from representative records, timestamps, queue views, handoff logs, rework records, support or operational data, and participant observations. For each observation record the source, source date, as-of date/time, locator, timestamp and timezone, sampling frame, and freshness. Separate directly observed measures from participant interpretation.

Method

  1. State the start, end, owner, outcome, population, and review window. Make the intended outcome testable without assuming that the current process achieves it.
  2. Select a representative sample. Document the sampling frame, inclusion and exclusion rules, time window, selection method, sample count, and known coverage gaps. Include normal and exceptional cases when the frame supports them; do not cherry-pick anecdotes. Record observations with citations and confidence.
  3. Map the observed path from start to end. Measure active time, waiting time, queue size or age, handoff count and delay, rework count and reason, and outcome status using consistent units and timezone treatment. Keep missing measurements visible.
  4. Apply a bottleneck method: compare stage throughput and capacity with queue volume, wait time, handoff delay, and rework. Call a bottleneck only when the measured constraint explains the observed delay or accumulation; otherwise label it a hypothesis and state what would test it.
  5. Separate observed friction from inferred causes and list alternatives. Quantify impact only from the sample and source data; do not extrapolate beyond its coverage.
  6. Frame a controlled experiment before action: hypothesis, proposed change, baseline or comparison, population, duration, owner or TBD, guardrails, stop condition, follow-up date, and a defined success measure with unit and time window. Human authority must approve the experiment; a recommendation is not a human decision.

Truth and uncertainty rules

Label each boundary, observation, measure, and explanation as observed, inferred, unknown, stale, or contradictory. A queue measure is evidence of queue state, not by itself proof of cause; a participant opinion is not an observation. Preserve contradictory records and explain sampling or instrumentation limits.

Never invent dates, metrics, owners, intent, money, percentages, causes, status, or evidence. Do not invent a sample denominator, use an anecdote as representative, or claim an experiment succeeded before its success measure is observed and reconciled.

Output contract

Return an audit containing:

  • explicit start, end, owner, outcome, scope, timezone, review window, and as-of provenance;
  • sampling frame and representative-sample method, sample count, coverage limits, and cited observations;
  • an observed flow with measured queues, waits, handoffs, rework, units, and source dates;
  • bottleneck method, evidence, confidence, alternatives, unknowns, stale inputs, and contradictions;
  • a controlled experiment proposal with baseline/comparison, owner or TBD, guardrails, duration, and a measurable success measure; and
  • recommendations, clearly labelled as recommendations rather than human decisions, with sources, confidence, unknowns, and contradictions exposed.

Safety and write boundaries

The default is read-only. Do not alter workflow rules, queues, assignments, policies, records, or communications from an audit. For a requested experiment or other action, preview the exact change, scope, owner, audience, and success measure; obtain explicit confirmation from the human authority; then perform only the confirmed action. Do not represent a proposed experiment as approved or active.

Verification and recovery

Read back the audit and reconcile the start/end boundaries, owner, outcome, sample count, denominator, measured queue/handoff/rework totals, source dates, timezone, and success measure with the evidence ledger. After an experiment, read back its measured result and reconcile it with the baseline, comparison, window, and guardrails before claiming success.

If sampling, measurement, read, write, or reconciliation fails, stop and report the failed check and affected stage. Do not retry blindly, discard inconvenient records, or alter the denominator to make a result pass. Re-read the source and destination, mark the result unknown or stale as appropriate, and recover through a human-confirmed correction or a new controlled run with the failure retained.

Frequently asked questions

What does the Process Audit AI skill do?

Use when an operational process needs its start, end, owner, outcome, representative sample, measured queues or handoffs, bottleneck evidence, or controlled improvement experiment made explicit.

Why use Process Audit on TypingMind?

Because you install it once and use it with any model. Process Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Process Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/_available/operations/process-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Process Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Process Audit?

As many as you like. As long as a model supports skills, you can use Process Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Process Audit AI skill free?

It is published on GitHub by davekilleen. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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