Input Contracts: Making Messy Notes, CRM Fields, and Transcripts Usable

When an output lacks context, evidence, or required fields, improving the prompt rarely solves it. Start by checking whether the workflow received the sources the job actually depends on.

Name each input, its purpose, owner, source system, freshness requirement, and whether it is required. This gives the operator a preparation checklist and gives the workflow a reliable boundary.

Define the smallest source bundle that can produce a useful, reviewable output. Keep it narrow enough to run consistently, then add optional material only when it changes a meaningful decision.

For each optional input, state whether the workflow can continue, which section changes, and how the absence is labeled. Optional does not mean invisible; a missing item can still affect confidence.

Specify accepted transcript quality, speaker labels, date, scope, and how unclear audio or missing segments are handled. A transcript without reliable attribution cannot support a claimed commitment.

List the fields, definitions, allowed values, currency or date conventions, source freshness, and precedence when CRM data conflicts with a current call. Do not silently normalize ambiguity into a completed field.

Name supported file types and the minimum content expected in each. Reject or request conversion for files that cannot be read reliably rather than pretending a partial extraction is complete.

Define freshness thresholds and a precedence order: for example, approved current records may outrank a stale CRM field, while unresolved conflicts become a visible question for the owner.

A missing-input response should say exactly what is needed, why it matters, who can provide it, and whether the rest of the output can proceed. Vague “need more context” messages waste the operator’s time.

Write the refusal rule in plain language: do not infer absent facts, names, dates, budgets, commitments, or root causes. Leave a marked unknown or route to the responsible human.

Use a short checklist for source completeness, date range, permissions, required fields, redactions, and output audience. A pre-run check prevents avoidable failures from becoming reviewer repair.

Use deterministic automation to collect, format, and validate data where possible. Use the skill to apply the job’s judgment and output rules; mixing the two makes it harder to diagnose failure.

Minimize inputs, redact unnecessary sensitive data, enforce access rules, and use approved tools before the workflow receives the bundle. Data handling is an input rule, not a post-generation cleanup step.

A good pack has named current sources, required fields, clear audience, and evidence references. An unusable pack is a broad paste of stale notes with no scope, ownership, or source hierarchy.

Include incomplete, contradictory, poorly labeled, and stale inputs in the test suite. The contract is reliable only if it correctly asks, flags, or stops on the data team members actually encounter.

When a new CRM, ticketing, or transcript source joins the process, update the contract, validate mapping and precedence, rerun relevant cases, and publish an operator note.

Show one good input pack, one insufficient pack, and the reason for the difference. Practical examples reduce random context dumping far more effectively than a generic request for clean data.

A workflow should not search every available file for a plausible answer. Broad scavenging increases privacy risk, source conflict, and unsupported claims; use named, authorized sources only.

Document trigger, required inputs, optional inputs, formats, source owners, freshness, precedence, privacy rules, validation, missing-data response, and escalation. Keep the template beside the skill.

Grokkit uses real examples to map the trigger, available sources, missing-input failures, exceptions, and reviewer checks before building. The contract becomes part of the workflow, test set, and deploy guidance.