meta-content-audit

name	meta-content-audit
description	Use when reviewing an existing content history to decide what to keep, stop, test or improve. Produces content audit and prioritised improvement plan; use `meta-competitor-analysis` when that neighbouring contract is the closer match.
metadata	{"portable":true,"compatible_with":["claude-code","codex"]}

Content Audit

Use When

• Use this skill for reviewing an existing content history to decide what to keep, stop, test or improve.
• Confirm that meta-competitor-analysis is not the closer route before proceeding.

Do Not Use When

• Use meta-competitor-analysis when its narrower output is requested.
• Do not publish, spend, change a live account, certify compliance, or invent missing client evidence.

Required Inputs

Artefact	Source/provider	Required?	If absent
Content inventory and platform performance exports for a stated period	Client, approved systems, or dated platform exports	Yes	Stop the affected decision; request it or mark the field unknown and narrow the output.
Purpose, audience and approval boundary	Client brief or accountable owner	Yes	Return discovery questions; do not infer approval.

Outputs

Artefact	Consumer	Acceptance condition
Content audit and prioritised improvement plan	Client lead and next workflow owner	Every recommendation traces to an input, names an owner or next action, and marks assumptions and unassessed checks.

Evidence Produced

Evidence	Format	Acceptance condition
Decision and source register	Table in the deliverable	Each material claim records its source/date or is labelled unverified; missing evidence never becomes a pass.

Capability and permission boundary

Read and search access to the supplied artefacts are required; calculation or file-rendering capability is optional. This is read-only by default: inspect and report without changing source records, accounts, skills or campaigns. Editing the deliverable requires explicit authorisation; publishing, production mutation, destructive action, spend, and certification claims require separate explicit authority and evidence.

Degraded mode

If files, platform access, network, rendering, fonts, or calculation tools are unavailable, return the narrowest useful qualified content audit and prioritised improvement plan. Mark each blocked check not assessed, state the consequence, and provide the exact evidence needed to resume. Never convert an unavailable check into a pass.

Decision rules

Choice	Action	Failure or risk avoided
Content inventory and platform performance exports for a stated period is current and attributable	Produce the full content audit and prioritised improvement plan and cite the evidence used.	Decisions based on stale or unrelated evidence.
A material input is missing or contradictory	Stop that decision, request clarification, or issue a labelled partial result.	Fabricated precision and false confidence.
The requested outcome belongs to meta-competitor-analysis	Route there and hand over the verified inputs already collected.	Neighbour collision and duplicated work.

Workflow

1. Confirm the requested decision, consumer, market, period and permission boundary; route to meta-competitor-analysis if its contract is closer.
2. Inventory the required inputs and their provenance. Stop any decision whose critical evidence is absent; recover by requesting it or recording a bounded assumption.
3. Apply the domain method in the core sections below, following the decision table whenever evidence conflicts or scope changes.
4. Verify calculations, dates, named platforms and claims against the supplied sources; label inference and uncertainty.
5. Produce the content audit and prioritised improvement plan, decision/source register and explicit next owner. Do not mutate live systems without separate authority.
6. Run the repository anti-slop ship gate. If a blocking factual, permission or evidence defect remains, fix it or withhold release.
7. Apply Narrative, audience empathy, and content quality audit to representative items and publish the capped audit plus 95/100 remediation plan.

Quality Standards

The output is client-specific, uses British English and the stated market/currency, distinguishes observed fact from inference, exposes gaps, and gives a checkable acceptance condition. Recommendations must be feasible within the confirmed budget, capacity and permissions. Metric findings are paired with audience empathy, narrative clarity, hierarchy, readability/accessibility, evidence, permissions, AI transparency, and learning-value findings.

Anti-Patterns

• Using an undated benchmark as the client's result. Fix: use account evidence or label the benchmark as a provisional comparator.
• Producing the content audit and prioritised improvement plan without content inventory and platform performance exports for a stated period. Fix: stop the affected decision or issue a clearly bounded partial output.
• Treating missing access or data as a successful check. Fix: record not assessed, its risk and the recovery input.
• Absorbing meta-competitor-analysis into this workflow. Fix: route the neighbouring output and hand over verified inputs.
• Publishing, spending or editing a live account during planning or review. Fix: obtain separate explicit authority and retain action evidence.
• Calling content effective because engagement is high while the audience cannot understand the offer or next step. Fix: inspect the narrative and information hierarchy.
• Treating a missing render, source, permission, or native-language review as a pass. Fix: mark it not assessed, state the risk, and name the recovery evidence.

Worked example

Given verified content inventory and platform performance exports for a stated period, the skill produces a content audit and prioritised improvement plan with source dates and named assumptions. If that evidence cannot be accessed, it returns only the supported sections plus a recovery list; it does not fill gaps with East African defaults.

Read next

• meta-competitor-analysis for the neighbouring contract.
• anti-ai-slop during production.
• ai-slop-audit at the release checkpoint.
• narrative-empathy-and-content-quality.md for audience, story, visual, accessibility, and learning checks.

References

• Anti-AI slop production gate
• Follow the directly linked repository skills above and any domain references named in the core sections below. Verify current platform, price, legal and regulatory claims before use.

Required Input

Before generating the audit output, collect the following from the consultant:

• Client name and trading name (if different)
• Industry and sub-sector
• Country / city (default: Uganda / Kampala)
• Primary goal (e.g., increase enquiries, grow brand awareness, improve engagement)
• Platforms to audit (select all that apply: Facebook, Instagram, LinkedIn, TikTok, YouTube, X/Twitter, WhatsApp)
• Content data collected using the template in Section 1 below — do not proceed without this data
• Content pillars (if already established — from 10-content-pillars skill if available)
• Brand voice guide (from 04-brand-voice-intake skill if available)
• Audit period: default is the last 3 months of published content

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Step 1: Data Collection Template

Before running the audit, ask the consultant to collect data from each platform's native analytics. Provide this template to complete for every post in the audit period.

Recommended minimum sample: last 3 months of content, all posts. A minimum of 30 posts per platform is required for meaningful pattern analysis. If the client has fewer than 30 posts on a platform in 3 months, extend the period to 6 months.

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Post-Level Data Template

For each post, record:

Field	What to enter
Post date	DD/MM/YYYY
Platform	Facebook / Instagram / LinkedIn / TikTok / YouTube / X / WhatsApp
Content type	Image / Video / Carousel / Text / Story / Reel / Short / Broadcast
Content pillar	Enter pillar name if known; leave blank if not yet established
Topic / theme	2–4 words describing the subject
Reach / Impressions	Number from platform analytics
Engagement	Total: likes + comments + shares + saves
Engagement rate	Engagement ÷ Reach × 100 (%)
Paid boost?	Yes / No
Notable comments or reactions	Any comment worth noting (praise, criticism, question, complaint)
Consultant note	Why do you think this post worked or underperformed?

Where to find this data per platform:

• Facebook / Instagram: Meta Business Suite > Insights > Content
• LinkedIn: Company page > Analytics > Content
• TikTok: TikTok Business Centre > Analytics > Content
• YouTube: YouTube Studio > Analytics > Content
• X/Twitter: X Analytics > Tweets

Export data as a spreadsheet where possible. Sort by engagement rate descending before handing to the audit process.

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Output Structure

Generate all seven sections below in order, using the data provided.

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2. Content Performance Summary by Platform

For each platform in scope, produce the following summary table:

[Platform Name]

Metric	Value
Total posts audited
Audit period
Average engagement rate
Above-average posts	Count and % of total
Below-average posts	Count and % of total
Top-performing content type
Worst-performing content type
Highest single engagement rate
Lowest single engagement rate

Benchmark guidance for Uganda / East Africa:

• Facebook: 1–3% engagement rate is average; above 3% is strong
• Instagram: 2–4% is average; above 5% is strong
• LinkedIn: 0.5–2% is average; above 2% is strong
• TikTok: 4–8% is average; above 10% is strong
• YouTube: measure by watch-time completion % and click-through rate, not engagement rate alone

Note where the client sits relative to these benchmarks. If performance is below benchmark, flag it clearly.

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3. Top-Performing Content Analysis

Identify the top 5 posts across all platforms (ranked by engagement rate; where rates are equal, rank by absolute reach).

For each of the top 5 posts:

Post [N] — [Platform], [Date]

• What the post was: One sentence describing the content (topic, format, visual approach)
• Key metric: Engagement rate and absolute engagement number
• Why it worked — 3 factors: Select from: format fit, strong hook, timely topic, emotional resonance, audience relevance, clear call to action, paid amplification, comment engagement, shareability, visual quality
• What to replicate: One specific and actionable instruction the team can apply to future content

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4. Worst-Performing Content Analysis

Identify the bottom 3 posts across all platforms (ranked by engagement rate ascending; exclude posts with zero reach, as these indicate a technical issue rather than content failure).

For each of the bottom 3 posts:

Post [N] — [Platform], [Date]

• What the post was: One sentence describing the content
• Key metric: Engagement rate
• Why it underperformed — honest assessment: Identify the likely cause. Common causes: wrong format for platform, no clear hook, topic irrelevant to audience, posted at poor time, overly promotional, no visual, confusing call to action, or content was simply not interesting to this audience.
• What to change or avoid: One specific instruction

Do not soften this section. Honest diagnosis prevents repeated mistakes.

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5. Content Pillar Coverage Analysis

If content pillars are established:

List each pillar and calculate the percentage of audited posts assigned to it.

Content pillar	Posts assigned	% of total	Status
Pillar 1			Over-represented / Balanced / Under-represented
Pillar 2
Pillar 3
Pillar 4
Promotional (if tracked separately)

A balanced content mix follows the 10-4-1 rule (Bodnar and Cohen, 2012): for every 10 pieces of shared or educational content, 4 original posts, and 1 promotional post. Flag any pillar that exceeds 40% of the mix (over-reliance) or falls below 10% (neglected).

If no content pillars exist:

Cluster the audited posts by topic. Identify 3–5 natural categories that emerge from the data. These become the foundation for establishing pillars. Recommend these clusters as draft pillars to the client.

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6. Tone and Consistency Rating

Rate the client's content on three dimensions, each on a 1–10 scale. Provide a one-sentence justification for each score.

Dimension	Score (1–10)	Justification
Visual consistency		Do the posts look like they come from the same brand? (Colours, fonts, image style, logo placement)
Tone consistency		Do the posts sound like the same brand? (Vocabulary, sentence length, formality level, use of humour)
Posting consistency		Are posts distributed evenly across the period, or clustered in bursts with gaps?

Scoring guide:

• 8–10: Strong and consistent — maintain
• 5–7: Inconsistencies present but brand is recognisable — improve
• 1–4: Significant inconsistency — address before investing further in content production

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7. Priority Improvements — First 30 Days

Produce exactly 5 specific changes the client should make immediately, drawn from the audit findings. Use this format for each:

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Improvement [N]: [Title]

• What to change: Specific, actionable instruction (not a general principle)
• Why: The specific finding from this audit that supports this change (reference a post, a metric, or a pattern)
• Expected impact: What should improve if this change is made consistently over 30 days

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Prioritise the improvements by likely impact: highest impact first. At least one improvement must address content format or type, at least one must address consistency, and at least one must address content pillar balance.

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Quality Criteria

Output meets the standard if it:

• Uses only data provided by the consultant — does not invent metrics or estimate engagement rates without a stated basis
• Platform performance summaries include a comparison against Uganda / EA benchmarks, not just internal averages
• Top and bottom post analysis identifies specific and distinct reasons for performance — not the same generic factors repeated across every post
• Content pillar coverage section flags imbalances clearly with reference to the 10-4-1 rule
• Tone and consistency ratings are justified with evidence from the content — not assigned arbitrarily
• 30-day improvements are genuinely prioritised (most impactful first) and each is traceable to a specific audit finding
• Output is direct and honest — underperformance is named, not softened

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Framework Reference

Apply the 10-4-1 rule (Bodnar and Cohen, 2012) when assessing content pillar balance. Apply the RACE framework (Chaffey, 2024) when interpreting whether content is serving the right stage of the customer journey (Reach / Act / Convert / Engage).

Bodnar, K. and Cohen, J. (2012) The B2B Social Media Book. Hoboken: Wiley. Chaffey, D. (2024) Digital Marketing: Strategy, Implementation and Practice. 8th edn. Harlow: Pearson.