What packaging actually does
It takes the knowledge in your best person's head — the context they supply, the steps they follow, the format they produce — and makes it the default for everyone rather than something each person rediscovers.
Most organisations have one person who gets consistently better output from an AI assistant than everyone else. Packaging is how that stops being a personal skill.
The symptom this addresses
| What you observe | What is actually happening |
|---|---|
| Output quality varies by person | Context and instructions vary by person |
| Everyone pastes the same background | Shared context is being re-supplied every time |
| Results are inconsistent in format | No specified output structure |
| New joiners take weeks to get value | The knowledge is tacit |
| One person is far more effective | They have worked out a method nobody wrote down |
What goes into a packaged workflow
- The process — what steps, in what order, with what checks.
- The context — your terminology, your standards, your constraints.
- The output format, specified precisely enough to be consistent.
- The edge cases — what to do when input is incomplete or ambiguous.
- The boundaries — what to escalate rather than attempt.
- Tools, where needed — access to systems the workflow depends on.
The boundaries matter as much as the process. A packaged workflow that knows when to stop and ask is trusted; one that confidently handles cases it should have escalated is used once.
Building one
- Watch your best person do it — actually observe, rather than asking them to describe it. The description always omits things.
- Write down what they supply as context and what they check in the output.
- Draft the package encoding that process explicitly.
- Test on real historical cases where you know what good output was.
- Have a second person use it without coaching. Where they struggle is where the package is underspecified.
- Refine and release to the team.
- Review after a month — what did people work around?
Test with historical cases where you already know the right answer. This is the only way to evaluate quality objectively. Judging output by whether it reads well is how packages that are confidently wrong get shipped.
Good candidates and poor ones
Package this
- Frequent and repetitive
- Has a definable good output
- Quality currently varies by person
- Someone reviews the result
- The process is stable
Not this
- Requires judgement you cannot articulate
- Different every time
- Consequential and unreviewed
- The process is about to change
- Nobody agrees what good looks like
Where packages and connectors meet
A packaged workflow using only the information a user pastes in is useful. The same workflow able to look up live data is considerably more so:
| Workflow | Without data access | With it |
|---|---|---|
| Support response drafting | Consistent tone and structure | Plus the actual account history |
| Sales call summary | Consistent format | Plus writing it to the CRM |
| Order query handling | Generic guidance | Plus the real order status |
| Reporting commentary | Structure only | Plus the current figures |
Package first, connect second. Packaging is cheap and reveals which data the workflow actually needs. Building connections first means guessing — and connector work is the expensive half.
Keeping packages honest over time
- Assign an owner — the person who owns the underlying process.
- Review when the process changes, not annually.
- Collect the cases where it produced poor output and use them as tests.
- Retire packages nobody uses rather than leaving them to mislead.
Have a workflow worth capturing? Describe what your best person does differently. See our Claude plugin service, rolling out to teams, and plugin vs MCP server.