Top Agent Skills

Measure pleasur.ai's AI-citation share across ChatGPT / Perplexity / Gemini / Google AI Overviews for the tracked query list, using SemRush (data API for AIO-presence screening + AI Toolkit via browser for share-of-voice / mention + citation gaps) and direct engine queries. Scores each query into the GEO ledger and surfaces the highest-leverage gaps to fix. The GEO Lead's core measurement loop.

Skeptical pushback on visual placement — both density and quality. Reads the annotated outline plus the visuals manifest and asks (a) whether the article hits the density target from editorial-principles-visuals.md, (b) whether each [VISUAL:...] earns its place, (c) whether sections without one would benefit. One revision pass on FAIL (BLOG_AGENT_VISUALS_REVISION_BUDGET, default 1).

Layer 1b of the keyword research pipeline. Finds keyword opportunities by comparing the brand's blog against competitors AND by expanding seeds + modifiers via Semrush (phrase_fullsearch / phrase_related). Auto-discovers competitors via domain_organic_organic when none are provided, derives the keyword gap via domain_domains, tags every row with `gap_mode`, and outputs a candidate-keyword CSV ready for downstream BID/AIO vetting.

Linkable-Asset Skill — data studies that pull links (Lesson 7)

Layer 6 of the keyword research pipeline. Organizes the vetted keyword queue into money clusters (from clusters.md), picks each cluster's keystone vs supporting articles, tracks coverage, and PROPOSES new clusters from high-business-value topics or live products not yet covered — so the blog strategy expands as the company grows. Emits cluster-tagged queue + cluster-map.md + cluster-proposals.md.

Layer 1d of the keyword research pipeline. Mines question-shaped keywords from Semrush phrase_questions (per surviving seed) plus People-Also-Ask strings from the SERP. Appends rows to keyword-ideas.csv with source=question_mining (or merges to source=both when the keyword already exists) and a question_subtype column. Cap of 100 rows per run.

Post-draft pass that expands a drafted article to capture the FULL keyword family — keywords the page already or could rank for, plus the Ahrefs Content Gap (keywords competitors rank for but we don't) — by weaving the worthwhile ones in as natural added paragraphs/sections. NOT keyword stuffing. Triggered after /draft, before /quality-check.

Knowledge base from "Contagious: Why Things Catch On" by Jonah Berger. Use when applying Berger's STEPPS framework for word of mouth, virality, social currency, triggers, emotion, social proof, practical value, and storytelling, studying the book, or referencing its concepts.

Off-page playbook (Lesson 9, link building). Mines our competitors' backlink profiles via Ahrefs to produce a VETTED link-prospect list — their "superfans" (sites linking to several of a competitor's best-by-links pages), their "power-linkers" (sites linking to multiple competitors but NOT to us), and their fresh new links — filtered for relevance and de-duped against sites that already link to us. The Link Intersect method, encoded. Output is a prospect queue for /outreach; it does NOT send anything.

After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.

Takes a curriculum framework and a statutory or accreditation requirement list; produces a coverage table, gap summary, and CSV showing which framework content covers each requirement and where gaps exist.

Surface beliefs, assumptions, stories, and values shaping a system. Use when deeper mental models need examining with care and evidence.

Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.

Review the week using accumulated session evidence — retrieval rates, hint depths, calibration accuracy, transfer and unassisted results. The learner identifies patterns and sets a strategy goal. Use weekly or after a multi-session period.

Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.

Predicts access barriers in a learning task before delivery, given a learner variability profile. Distinguishes between barriers addressable through design and those requiring specialist support.

Compares two or more band-tagged frameworks and produces a framework-neutral topic matrix showing coverage and gaps across all inputs, plus an optional reference-centric PLC crosswalk document when a reference framework is supplied.

Unpack a broad standard or competency descriptor into specific, assessable success criteria and sub-skills. Use when interpreting curriculum standards or writing learning objectives.

Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.

Design a lesson opening that activates prior knowledge and connects previous learning to today's content. Use when planning lesson starters, retrieval openers, or advance organisers.

Design a structured protocol for auditing AI-generated text against Ennis's six CT standards. Use when students need to critically evaluate AI output in any subject.

Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

Adapt a classroom task for specific learner needs while preserving the core learning objective intact. Use when differentiating for SEND, EAL, gifted, ADHD, dyslexia, or anxiety.

Design the deeper patterns, structures, artefacts, and mental models needed to grow a desired event. Use when a class imagines a better future.

Design a wise systems intervention from an existing analysis. Maps proposed actions against Meadows' leverage points, checks for unintended consequences, and generates alternatives.

Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.

Build students' capacity to read historical documents closely — attending to word choice, tone, and rhetoric as evidence of perspective. Use when students summarise sources without analysing language.

Build students' capacity to compare accounts across multiple historical sources — identifying agreements, contradictions, and gaps. Use when students treat individual documents as complete answers rather than partial perspectives.

Select evidence-based study strategies matched to material type, learning goal, and student habits. Use when advising students on revision techniques, homework, or independent study approaches.

After scaffolded practice, run an unassisted check — a problem with no AI help. Separates what the learner can do with support from what they can do independently. Critical for preventing phantom attainment.

Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.

Design a single-point rubric with one criterion and open columns for evidence. Use for student self-assessment, peer feedback, teacher formative feedback, or pre-task planning. Works with any learning target, with or without a band system.

When a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.

Map systemic forces shaping a wellbeing concern without individualising the problem. Uses Bronfenbrenner's ecological model and social determinants to generate structural interventions.

Design a scope and sequence showing vertical and horizontal curriculum coherence across a programme or year. Use when building new programmes, restructuring subjects, or ensuring progression.

Analyse the language demands of a classroom task to identify barriers for EAL and multilingual learners. Use when adapting tasks, planning support, or assessing linguistic accessibility.

Modify a classroom task with language scaffolds that preserve cognitive demand for EAL learners. Use when adapting existing tasks for students at different English proficiency levels.

Redesign a blocked topic sequence into an interleaved plan with mixed practice across related topics. Use when planning units, homework schedules, or revision programmes.

Design a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.

A high-rigour student-facing orchestrator that chains retrieval practice, metacognitive calibration, and unassisted verification into a cohesive learning session.

Tags harness-decomposed curriculum items (KUDs, LTs, criteria) with a school's developmental band metadata while preserving source voice and labels. Supply the band schema; the skill derives mapping rules from it.

Build a learning progression showing prerequisite-to-mastery steps for a target skill or understanding. Use when sequencing content, designing diagnostics, or mapping prerequisite gaps.

Design structured perspective-taking activities with anti-projection guardrails. Develops genuine understanding of complexity across history, social sciences, and literature — not performed empathy.

Map control, influence, and concern after systems analysis. Use when students need wise agency without being made responsible for everything.

Design a document set for a document-based lesson — selecting and sequencing sources for analytical tension around a central question. Use when assembling sources for a new lesson or when an existing set produces flat responses.

Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.

Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.

Analyse student work against criteria to identify specific gaps between current performance and learning objectives. Use when reviewing submissions, planning feedback, or diagnosing learning needs.

Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

Design a practitioner inquiry cycle from research question through data collection to evidence-informed action. Use when starting action research, teacher-led investigation, or professional inquiry.

Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.

Design an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.

Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.

Design a Concrete-Pictorial-Abstract learning sequence for a mathematical concept using manipulatives. Use when teaching maths through Singapore method or when students struggle with abstraction.

Design a structured goal-setting protocol using SMART or implementation-intention frameworks for students. Use when launching units, projects, or developing student self-direction habits.

Generate a scaffolded sequence of practice problems with graduated difficulty and strategic variability. Use when creating worksheets, homework sets, or independent practice materials.

Synthesise completed KUD charts into a developmental progression matrix and per-competency narrative sections. Use when you need a programme-level view of how knowledge, understanding, and performance develop across bands.

Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

Create a writing scaffold teaching the genre conventions specific to an academic discipline. Use when students write lab reports, historical essays, or other discipline-specific text types.

AL debugging and diagnostics for Business Central. Use when performing snapshot debugging, CPU profiling, analyzing telemetry, or troubleshooting runtime issues.