---
name: senior-team-member-reviews-weekly
description: A proposed fix needs a clear answer and a next step.
category: Knowledge System Maintenance
stage: —
definitive_article: /knowledge-system-maintenance
status: needs-work
---

# Senior team member reviews weekly

A proposed fix needs a clear answer and a next step. This guide helps a reviewer judge the proof and wording each week. Start with the real queue and the current guides it affects.

**The path:** Queued changes → Evidence review → Decision → Verified release or return

**Start when:** The agreed weekly review is due and actual pending proposals need decisions or next actions.

## Inputs

- The actual proposal queue, prior pending decisions and review cutoff.
- The current affected guide revisions and the 500-word/four-part proposal standard.
- A named reviewer with decision authority and the supported change/publication workflow.

## Steps

1. Open the queue at the agreed review time. Record the pending scope and any inaccessible items. An empty view is not a proven empty queue until filters/access are checked.
2. Check format: problem, affected guide/section, exact wording and two real examples within 500 words. Return missing parts with a specific reason instead of pretending the request was substantively rejected.
3. Read the examples and current source. Judge whether the change fixes the problem and preserves adjacent steps, source authority and tool/method separation.
4. Record approved, rejected, returned or decision pending with reviewer, date and reason. Do not force every item to a final decision just to end with an empty queue.
5. For an approved change, use the authorized owning-source workflow. Record the version/change history and verify the saved/public result separately. An approval awaiting deployment stays approved, not released.
6. For returns/rejections, send the reason only through the authorized project channel and verify that notice. Keep the item findable and name any next action.
7. Flag aged pending items and repeated issues for the process owner. Preserve original submission dates and decision latency for later analysis.

## Definition of done (QA checklist)

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

- [ ] Every in-scope proposal has a justified decision or an explicit pending dependency.
- [ ] Approvals and actual releases are separate, with changed-version/readback evidence.
- [ ] Reasons and next owners remain visible; overdue work is not hidden.

## Example(s)

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

A fictional queue holds three proposals. One is approved but awaits the source owner’s release; one lacks a second example and is returned; one is rejected because the current guide already covers it. The receipt records three distinct outcomes, not three published changes.

## 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 source owner releases approved changes. [The pattern-review task](https://local-service-spotlight.github.io/task-library/?task=review-past-6-months-of-sop-amendment-proposals-for-patterns#task-review-past-6-months-of-sop-amendment-proposals-for-patterns) uses the retained decisions and timings.

## 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=senior-team-member-reviews-weekly#task-senior-team-member-reviews-weekly)
- [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.