---
name: create-knowledge-capture-note-template-and-distribute
description: A lesson form helps only if your team can find and use it.
category: Knowledge System Maintenance
stage: —
definitive_article: /knowledge-system-maintenance
status: gap
---

# Create Knowledge Capture Note template and distribute

A lesson form helps only if your team can find and use it. This guide puts the form where the work happens. Start with the checked form, then try it with the people who need it.

**The path:** Checked form → Workflow links → Real trial → Adoption review

**Start when:** A checked lesson form exists and the intended team needs a usable rollout and adoption check.

## Inputs

- [The checked four-field form](https://local-service-spotlight.github.io/task-library/?task=provide-knowledge-capture-note-template#task-provide-knowledge-capture-note-template) and a real filled example.
- Shared storage, the team’s three capture workflows, and their owners.
- Authority for training, source updates and any team notice; an agreed adoption-review date.

## Steps

1. Inspect the existing form before creating another. It needs source, insight, destination and priority, short hints and a real sourced example. Use the authoring task if these are missing.
2. Confirm one canonical saved copy and the receiving team’s access. Keep a revision and point copies back to it.
3. Use [the workflow-integration task](https://local-service-spotlight.github.io/task-library/?task=add-knowledge-capture-to-existing-workflows#task-add-knowledge-capture-to-existing-workflows) to put the form at call wrap-up, campaign review and audit closeout. Preserve already working links and route process changes through review.
4. Run an agreed walkthrough with one genuine lesson. Show how to cite the source, state the insight, choose a destination and set priority. A sample demonstration is training, not a completed client task.
5. Over the first two weeks, inspect real opportunities to capture lessons, saved notes and their timestamps. Check both use and quality; no notes may mean no relevant work, inaccessible storage or a missed capture.
6. Record adoption as observed, partial or untested for each workflow. Route repeated problems to a reviewed form/workflow change and retain who owns the next check.

## Definition of done (QA checklist)

[Quality assurance (QA)](https://localservicespotlight.com/article-guidelines/) means checking the work against the agreed result.

- [ ] Canonical form and example are accessible at all three in-scope capture points.
- [ ] A real walkthrough and its participants/output are recorded.
- [ ] The two-week review measures actual notes and opportunities, with blockers retained.

## Example(s)

**Fictional teaching example. This is not a client result or proof of a completed run.**

A fictional rollout links the form from three checklists. During two weeks, calls produce four usable notes, an audit produces one, and no campaign review occurs. Report adoption evidence for calls/audits and campaign adoption untested. Five notes do not prove all three workflows are working.

## Handoff and Content Factory context

This work supports the [Content Factory, our four stages of using real content](https://blitzmetrics.com/content-factory/): Produce → Process → Post → Promote. Use the specific inputs and next task below to place the work; a maintenance task does not manufacture transcripts, clips or other stage outputs it does not call for.

Capture owners use [Capture a lesson within 24 hours](https://local-service-spotlight.github.io/task-library/?task=generate-knowledge-capture-note-within-24-hours#task-generate-knowledge-capture-note-within-24-hours); the rollout owner retains adoption evidence and [Draft a process-change proposal](https://local-service-spotlight.github.io/task-library/?task=create-sop-amendment-proposal-500-words#task-create-sop-amendment-proposal-500-words) handles accepted changes.

## Run with an agent

Give the AI worker this recipe, the real Inputs above, the intended result and the actions already authorized. Ask it to return the saved output, checks, evidence and remaining owner. Check its work against this guide; loading a skill does not prove access, installation of a job, or successful execution. Keep media muted with volume at zero if playback is needed.

For recurring work, keep the actual trigger, owner and runtime in the job record. Scheduling and observed firings are separate. Do not create a schedule merely because this guide mentions a review interval.

## Record the real execution

Open the run record when the work starts. Keep the exact starting recipe revision, one execution ID, source evidence and actual state. Write a [meta article, the record of one run](https://blitzmetrics.com/meta-article-prompt/) with decisions, results, checks, failures and next owner. Link it to this task and register it through the [Task Library](https://local-service-spotlight.github.io/task-library/) execution process. Writing is part of the work; public release follows existing authority.

Reuse the same execution ID for revisions, QA, meta writing and retries within that run. A blocked run stays open with its dependency and next owner; do not invent a finish time. Use supported findings to propose and verify a better recipe. A historical public-example count without distinct run IDs remains dated article volume, not verified execution frequency.


<!-- factory-layer:start -->

## Factory chain (do not treat this as isolated)

- **Phase:** —
- **Importance:** 2/5 `●●○○○` — frequency 2, revenue 1, gating 2 (supporting)
- **Suggested previous station:** —
- **Suggested next station:** —
These stations are generated neighbors, not verified prerequisites or handoffs. Follow the task’s actual Inputs and Handoff sections.
- **Handoff:** Save this task’s actual output, source, date and checks where the receiving owner can read them. Follow the Inputs and Handoff sections for its next step.

## Model routing (same factory, any engine)

- **This task's lane:** `any` — Any capable chat model (Claude, ChatGPT, Grok, Gemini). SOP following, drafts, checklists.
- **Single-engine path:** Use the capable AI app you already have. Check its required tools and access before starting. Pass the actual result and proof to the next task.
- **Optional multi-engine:** A tested local model may draft; a stronger model may review facts and voice. Use supported tools for approved actions. Pick models from observed quality and cost; a model name does not prove it can run this task.
- **Never** store the working state in one vendor's memory. Files in the Content Library are the bridge.

<!-- factory-layer:end -->
## Definitive article & links

- [Maintained source guide](https://blitzmetrics.com/knowledge-system-maintenance/)
- [This task in the Task Library](https://local-service-spotlight.github.io/task-library/?task=create-knowledge-capture-note-template-and-distribute#task-create-knowledge-capture-note-template-and-distribute)
- [Article Guidelines](https://localservicespotlight.com/article-guidelines/)
- [current article guide](https://blitzmetrics.com/definitive-article-guide/)
- [How recipes and run records work together](https://localservicespotlight.com/meta-articles/)

## Review and evidence still needed

The inherited contributor status is `gap`. It is preserved, not promoted by this rewrite. That label alone does not verify document readiness, a client outcome, access or an executed task.

A real execution still needs its own source, reviewer, saved result and handoff evidence. The fictional example teaches the method; a real example with relevant proof is still needed where required. The task-specific flow above is source guidance; its visible presentation and the full document need a named reviewer and desktop/mobile checks.