autopunk-media-skills

Generates the complete set of metadata fields — tags, categories, keywords, slug, and summary — needed to publish a piece of content in a content management system, ready to copy and paste into each field.

Takes a finished piece of content and produces a complete, archive-ready metadata record — including descriptive tags, subject classifications, rights notes, and an archival summary — for entry into a content management or digital archive system.

Drafts a GDPR compliance note for a specific piece of journalistic content or data collection activity — documenting the lawful basis for processing personal data, what data is held, how long it is retained, and who has access.

Reviews the details of a specific image and flags potential rights clearance issues before publication, identifying which permissions you likely need and which questions to bring to your legal or picture desk.

Reviews a story brief, draft article, or broadcast script and flags potential legal risks — including defamation, privacy, contempt of court, and data protection — so you know what to check before publication.

Drafts a formal, professional request letter to a music rights holder asking for permission to use a track in a specific media production under defined terms.

Produces a correctly formatted bibliography or source list from details you provide, supporting AP, Chicago (Notes-Bibliography), and APA citation styles.

Reads a batch of raw audience comments, emails, or survey responses about a piece of content and produces a structured summary of what readers valued, what confused them, and what they want more of.

Takes one original headline and produces five distinct tested variants, each using a different proven approach to drive clicks, shares, or engagement — ready to load directly into a CMS or A/B testing tool.

Recommends the best platforms for distributing a finished piece of content, with a ranked list and a brief rationale for each recommendation based on the content type, format, length, and intended audience.

Recommends the best day and time to publish a piece of content based on the content type, platform, and intended audience — without requiring access to live analytics data.

Reads a batch of reader comments and produces a structured summary of recurring themes, unanswered questions, emotional tone, and actionable editorial signals — so editors and journalists can understand what their audience actually took from a piece.

Generates two or three distinct social media post variants for the same article or piece of content, each testing a different angle, hook, or emotional register, so you can run a controlled comparison and learn what resonates with your audience.

Produces a clear, specific description of who a piece of content is written for — including what that audience already knows, what they care about, and what they will do with the information — so editors, writers, and distributors can make better decisions at every stage of production.

Performs and explains basic statistical calculations — percentages, percentage changes, per-capita rates, averages, medians, and ratios — from data you provide, showing the working so you can verify it and use the figures confidently in a story.

Identifies unusual values, unexpected patterns, and potential stories hidden in a dataset by systematically checking for statistical outliers and contextual anomalies.

Identifies the newsworthy story or stories hidden inside a dataset before any writing begins — surfacing angles, outliers, trends, and comparisons that are genuinely publishable.

Produces a structured summary of what a dataset contains, what it covers, what its limitations are, and what story angles it could plausibly support — written as a briefing document for a journalist or editor.

Analyses poll or survey results and writes a clear, accurate, publication-ready explanation for a general audience — covering the headline finding, key subgroup breakdowns, methodology caveats, and what the numbers do and do not mean.

Rewrites a complex statistic — including percentage changes, relative risk, confidence intervals, medians, and index numbers — in plain language that a general audience can understand without sacrificing factual accuracy.

Writes clear, step-by-step instructions for cleaning a messy or inconsistent dataset — specifying exactly what needs to be standardised, corrected, or removed to make the data ready for analysis and publication.

Drafts a Freedom of Information request specifically targeting datasets, databases, or digital records — written to maximise the chance of receiving complete, machine-readable data rather than summary PDFs or partial tables.

Produces a structured extraction plan and clean spreadsheet template for pulling tabular data out of a PDF document — identifying the table structure, defining column headers, flagging extraction pitfalls, and providing a ready-to-use template that ensures the data lands in a consistent, analysable format.

Writes a clear, technical brief describing exactly what data needs to be collected from a website or set of web pages, how it should be structured, and what edge cases and legal/ethical considerations apply — for handoff to a developer or data team.

Writes a precise, publication-standard corrections notice for a data error published in an article — stating clearly what was wrong, what the correct figure is, and how the error affected the reported story.

Writes precise, publication-standard footnotes for data claims in a finished article — citing sources accurately, explaining how figures were derived where necessary, and flagging any caveats that qualified readers need to know.

Writes a plain-language explanation of a data journalism methodology — what data was used, where it came from, how it was processed, and what the analysis found — suitable for publication alongside a data story.

Writes a precise, accessible text description of a data visualization — covering the chart type, what it shows, the key finding, and the data range — suitable for publication as a caption, alt-text, or standfirst alongside the chart.

Writes clear, precise axis labels, legends, annotations, and source lines for a chart — the text layer that makes a graphic publishable.

Recommends the most effective chart type for your data and editorial goal, explains why it works, and warns you about common misrepresentations.

Formats raw or messy data into a clean, publication-ready table with appropriate headers, sorted rows, consistent number formatting, and a source note — ready to drop into an article, report, or web page.

Writes a detailed, production-ready brief for an infographic designer from raw data or findings — specifying the story, the key figures, the recommended visual approach, the hierarchy of information, and the format requirements.

Rewrites AI-flavoured copy into a publishable register by stripping the language tells of an LLM draft — buzzwords, throat-clearing, false-inclusive openers, and the "not just X — Y" rhetorical tic — and supplies the canonical banned-list that runtime hooks read to flag drafts before they ship.

Copy-edit a piece of text for clarity, consistency, style, and readability — going beyond proofreading to tighten prose, improve sentence structure, and enforce consistent style throughout, while flagging substantive issues for the author's attention.

Reviews a draft for statements that carry a credible risk of defamation, producing a flagged checklist of red-flag passages with a plain-language explanation of why each is potentially problematic.

Takes a draft written by two or more contributors and rewrites it as a single, consistent voice — smoothing over clashing tones, register shifts, and stylistic inconsistencies while preserving every author's factual content and reported material.

Proofread a piece of text and return it with all spelling, grammar, punctuation, and typographical errors corrected — preserving the author's voice and intentional style choices throughout.

Scans a draft for repeated ideas, redundant phrases, and unnecessary word clusters, then delivers a flagged report and a tightened version of the text without altering meaning or voice.

Checks a script against a specified style guide and returns a prioritised list of violations with corrections.

Analyses a draft for logical sequence, narrative coherence, and transitions, then reports exactly where the structure breaks down and why.