---
name: separate-methodology-layer-what-why-from-implementation-layer-how
description: A tool may change while the job stays the same.
category: Knowledge System Maintenance
stage: —
definitive_article: /knowledge-system-maintenance
status: needs-work
---

# Separate methodology layer (what/why) from implementation layer (how)

A tool may change while the job stays the same. This guide keeps the method clear and the current tool steps easy to update. Start with one task guide and keep the details people need to do it.

**The path:** Current guide → Method and tool map → Layered copy → Reader check

**Start when:** A task guide is being written or revised and its method is mixed with product-specific steps.

## Inputs

- The current process guide with its source revision, examples and media.
- A reviewer who understands the task’s inputs, decisions and expected output.
- The [knowledge maintenance method](https://blitzmetrics.com/knowledge-system-maintenance/) two-layer rule and authority for proposed source changes.

## Steps

1. Classify every instruction and image: method means what to do, why and how to judge it; implementation means this product’s controls, commands, versions or screenshots.
2. Put method steps first or pair each with a clearly marked implementation block. Split mixed sentences instead of deleting the practical instructions.
3. Preserve the task’s actual constraints. If a named service is the required target of the job, say so; portability does not mean pretending a WordPress-only task has no WordPress dependency.
4. In a review copy, hide implementation blocks and check whether a reader understands inputs, sequence, decisions, pass/fail and next owner. Then restore the blocks and check today’s concrete steps still work as documented.
5. Explain which details need rechecking when a tool changes. Some capability changes can also alter the method; use evidence and review rather than assuming separation guarantees an unchanged process forever.
6. Prepare the exact restructure through the change protocol. Preserve media, links and examples, then compare the saved revision after authorized release.

## Definition of done (QA checklist)

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

- [ ] All instructions are assigned to a clear layer; no useful tool detail is lost.
- [ ] Method clarity and current implementation are separately checked.
- [ ] Real platform constraints and unresolved compatibility stay explicit.

## Example(s)

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

A fictional guide says “Click Tool A’s blue words to fix the transcript.” Split it into “Compare the transcript with the recording and correct errors” and a dated Tool A control note. If another app lacks the needed export, that is a real implementation gap; the plain method does not make the export exist.

## 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.

[The library reviewer](https://local-service-spotlight.github.io/task-library/?task=review-existing-sops-for-portability-compliance#task-review-existing-sops-for-portability-compliance) receives the pattern; use [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) for source 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=separate-methodology-layer-what-why-from-implementation-layer-how#task-separate-methodology-layer-what-why-from-implementation-layer-how)
- [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 `needs-work`. 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.