An open-source library of 111 evidence-based pedagogical skills for curriculum design, lesson planning, and assessment — usable today by any educator with access to Claude, and engineered for AI agent orchestration.
I'd love to hear your thoughts. If you have suggestions, find bugs, or want to contribute:
- Email: gareth.manning@gmail.com
- X: https://x.com/worldteacherman
- LinkedIn: https://www.linkedin.com/in/gareth-manning-a404b387/
- Open a Pull Request or Issue on GitHub
I'm an educator — start here No setup required. Install the plugin and start teaching.
I'm a developer or AI builder — start here YAML schemas, typed inputs and outputs, chaining metadata, live MCP server.
- Classroom teachers who want evidence-based lesson and assessment design without hours of research
- University lecturers and professors who received little or no teacher training and want practical, research-grounded support for their teaching
- Curriculum designers and heads of learning building programmes, units, and assessments
- School leaders in innovative and alternative education contexts — international schools, Montessori, project-based, democratic and nature-based schools
- Innovators reimagining education — people building new school models, alternative programmes, and next-generation learning environments. Evidence-based constraints don't limit creative redesign of education; they deepen it.
- EdTech developers and AI builders who need a structured, programmatically accessible education knowledge layer
- Education researchers interested in how evidence translates into AI-mediated practice
AI is arriving in education fast. Whether it improves learning outcomes or simply scales mediocre practice depends almost entirely on what it is built on.
Most AI education tools are built on convention, habit, and assumption — on what educators have always done, rather than on what the research says actually works. Learning styles. Rigid lesson structures. Wellbeing programmes disconnected from learning theory. As AI expands in education, so does the risk of scaling ineffective practice.
This library exists to build something different: a credible, rigorous foundation for AI in education. One that is anchored in named research, honest about its limitations, and designed especially for the educators working at the frontier — building the next generation of schools, not optimising existing ones.
The potential is real. Personalised, evidence-grounded learning support at a scale that was never previously possible. But only if what is powering it is the actual evidence.
The benefit is not only personalised learning. It is teaching quality and workload. An educator who would otherwise spend hours researching, designing, and second-guessing gets structured, evidence-grounded support in minutes — which means more time for the parts of teaching that only a human can do.
That is one use case. The same library can power school-wide curriculum audits, personalised professional development pathways for teachers, or orchestrated end-of-term assessment reviews. The skills are the foundation. The architecture below describes the layers that make this possible.
Add the MCP server URL under Settings → Connectors:
https://mcp-server-sigma-sooty.vercel.app/mcp
That's it. On first use: click the + icon in the message bar → select Connectors → find Claude Education Skills and toggle it on. After that, skills activate automatically in every conversation — no manual activation needed.
Run the server locally without any account or deployment:
npx claude-education-skillsOr add it permanently to your Claude Code config:
{
"mcpServers": {
"claude-education-skills": {
"command": "npx",
"args": ["claude-education-skills"]
}
}
}/plugin marketplace add GarethManning/claude-education-skills
/plugin install claude-education-skills
Skills load with progressive disclosure — metadata only until a skill is actually needed.
https://mcp-server-sigma-sooty.vercel.app/mcp
The library is now compliant with the Agent Skills 1.0 open standard. What this means in practice:
- One-command install — no manual setup, no copy-pasting prompts
- Progressive disclosure — skill metadata loads first; full skill content loads only when activated, keeping context lean
- Auto-activation — skills trigger when your conversation matches their description, without explicit invocation
- Machine-readable registry —
registry.jsonindexes all skills with descriptions, tags, chaining metadata, and domain grouping for programmatic consumption - Backward compatible — the MCP server, direct prompt use, and all existing workflows continue to work unchanged
Install the plugin, then tell Claude what you need in plain language. The skills activate automatically.
Example: Say "I'm planning a Year 9 science unit on cells — 6 weeks, 3 lessons a week."
Claude runs the Backwards Design Unit Planner, the Spaced Practice Scheduler, and the Retrieval Practice Generator in parallel. In under 90 seconds you get a complete lesson-by-lesson plan with spaced retrieval built in, evidence-grounded sequencing, and ready-to-use formative assessment activities — all calibrated to the timeline and topic list you provided.
No API key. No technical setup. No dependencies.
- Open any skill file in the repository (under
skills/) - Copy the prompt block
- Paste it into Claude and fill in the fields for your class or context
Example: Open skills/memory-learning-science/spaced-practice-scheduler/SKILL.md and provide:
- Topics: Cell structure, Cell transport, Cell division, Enzymes, Biological molecules
- Timeline: 8-week term, starting 3 February
- Lessons per week: 3
Claude returns a complete week-by-week schedule showing when to teach new content and when to revisit previous topics at expanding intervals — with specific retrieval activities for each review slot. The schedule follows Cepeda et al.'s (2006) meta-analysis on optimal spacing intervals, includes interleaving across topics, and comes with practical guidance on what to do when review reveals gaps.
Evidence is the filter — including knowing what to exclude. Every skill is grounded in named research: specific authors, specific studies, specific findings. Frameworks that lack empirical support — including learning styles, VAK, and other widely-circulated but poorly-evidenced approaches — are not included. The library documents exactly what was excluded and why in EXCLUSIONS.md. For any school or faculty trying to separate evidence from convention, that document is worth reading on its own.
Evidence strength is rated transparently.
| Rating | What it means |
|---|---|
| Strong | Multiple meta-analyses or systematic reviews with consistent findings |
| Moderate | Solid experimental evidence with some contextual variation |
| Emerging | Promising research base with limited replication or practitioner translation |
| Original | Practitioner framework; clearly labelled, not claimed as research-backed |
Where original frameworks are included (Domain 14), they are labelled honestly. One important limitation: the skills encode research-grounded prompts, but the prompts themselves have not been empirically validated as AI interventions. That work is ongoing.
Built by an educator with 20 years of international school experience. The pedagogical judgements embedded in every prompt, every output structure, and every known-limitations section reflect real classroom and curriculum design practice — not a reading of the literature.
Designed for orchestration from day one. YAML schema headers, typed input and output fields, chaining metadata, and composable outputs are built into every skill. This is not a prompt collection with metadata bolted on. It is a skill library engineered for programmatic use.
| # | Domain | Skills | Focus |
|---|---|---|---|
| 1 | Memory & Learning Science | 8 | Retrieval practice, spacing, interleaving, cognitive load, dual coding, elaborative interrogation, feedback |
| 2 | Self-Regulated Learning & Metacognition | 5 | Self-regulation scaffolds, metacognitive prompts, goal-setting, study strategy selection, error analysis |
| 3 | Explicit & Direct Instruction | 5 | Gradual release sequences, checking for understanding, lesson openings, think-alouds, practice design |
| 4 | Questioning, Discussion & Dialogue | 4 | Socratic questioning, discussion protocols, dialogic teaching moves, hinge questions |
| 5 | Literacy, Writing & Critical Thinking | 7 | Argument structure, disciplinary writing, reading comprehension, source evaluation, text complexity, media literacy, critical thinking |
| 6 | EAL/D & Language Development | 5 | Language demand analysis, vocabulary tiering, scaffolded task modification, sentence frames, sheltered instruction |
| 7 | Curriculum Design & Assessment | 15 | Backwards design, competency unpacking, rubric generation, assessment validity, formative assessment, differentiation, gap analysis, learning progressions, PBL, threshold concept translation |
| 8 | Wellbeing, Motivation & Student Agency | 12 | Motivation diagnostics, self-efficacy, wellbeing-learning connections, agency scaffolds, belonging, and related practices |
| 9 | Professional Learning & Teacher Development | 9 | Lesson observation, reflective practice, PD session design, data interpretation, and related practices |
| 10 | Global & Cross-Cultural Pedagogies | 9 | Variation theory, CPA sequences, phenomenon-based learning, culturally responsive teaching, Ubuntu, place-based inquiry, Reggio documentation, emergent projects, cross-cultural validity |
| 11 | Environmental & Experiential Learning | 6 | Outdoor learning, biophilic design, ecological inquiry, experiential learning cycles, interdisciplinary connections, service learning |
| 12 | AI Learning Science | 14 | Adaptive hints, erroneous examples, digital worked examples, spacing algorithms, AI feedback, tutoring dialogue, learning analytics, collaborative learning, cognitive tutoring, self-explanation, metacognitive monitoring, productive failure, worked example transitions, formative assessment loops |
| 13 | Montessori & Alternative Evidence-Based Approaches | 4 | Three-part lessons, prepared environment design, mixed-age learning, uninterrupted work cycles |
| 14 | Original Frameworks | 5 | SEEDS regenerative inquiry, developmental band systems, learning target authoring, rubric logic, self-determined project design |
| 15 | Curriculum Alignment | 4 | Coverage audit, KUD chart authoring, developmental band translation, scope and sequence |
The library is Layer 1 of a three-layer system. See ARCHITECTURE.md for the full design.
Layer 1 — Skill Library (this repository, complete and available now) 111 skills across 15 domains. Each skill encodes a specific, evidence-grounded instructional or curriculum design decision. Works standalone today.
Layer 2 — Context Engine (in design) Holds persistent information about students, classes, curriculum sequences, and assessment history. When connected, skill outputs become personalised — not generic advice about retrieval practice, but a specific retrieval schedule for this class based on what they have already learned and been assessed on.
Layer 3 — Orchestrator (in design) Allows an educator to state a high-level goal — "Design a 6-week unit on climate change for my Year 8 class" — and receive a complete, personalised plan assembled from multiple skills. The orchestrator proposes; the educator decides.
Every skill opens with a machine-readable header. Here is a real example:
---
skill_id: "memory-learning-science/spaced-practice-scheduler"
skill_name: "Spaced Practice Schedule Builder"
domain: "memory-learning-science"
version: "1.0"
evidence_strength: "strong"
evidence_sources:
- "Cepeda et al. (2006) — Meta-analysis of 254 studies on distributed practice"
- "Kornell & Bjork (2008) — Spacing and interleaving effects on learning"
- "Dunlosky et al. (2013) — Distributed practice rated high-utility learning strategy"
input_schema:
required:
- field: "topics"
type: "array"
description: "List of topics or concepts to be spaced across the schedule"
- field: "timeline"
type: "string"
description: "Available teaching period (e.g. '6-week half-term')"
- field: "lessons_per_week"
type: "integer"
description: "Number of lessons per week for this subject"
optional:
- field: "assessment_date"
type: "string"
description: "From context engine: summative assessment date"
- field: "student_profiles"
type: "array"
description: "From context engine: class-level retention data from prior assessments"
output_schema:
type: "object"
fields:
- field: "schedule"
type: "array"
description: "Week-by-week schedule of new teaching and spaced review slots"
- field: "review_activity_suggestions"
type: "array"
description: "Specific retrieval activities for each review slot"
- field: "teacher_guidance"
type: "string"
description: "Implementation guidance and how to handle gaps"
chains_well_with:
- "retrieval-practice-generator"
- "formative-assessment-technique-selector"
- "interleaving-unit-planner"
teacher_time: "5 minutes"
tags: ["spacing", "memory", "planning", "forgetting-curve", "distributed-practice"]
---An orchestrator calls search_skills("retrieval practice"), gets back candidates, calls get_skill on the best match, and injects the prompt into its workflow.
The skill library is available as a live MCP server. Any MCP-compatible client can discover, search, and invoke all 111 skills programmatically.
Production URL: https://mcp-server-sigma-sooty.vercel.app/mcp
Connect from Claude.ai by adding the URL under Integrations > MCP Servers. Connect from Claude Desktop:
{
"mcpServers": {
"education-skills": {
"type": "streamable-http",
"url": "https://mcp-server-sigma-sooty.vercel.app/mcp"
}
}
}The server exposes:
- 115 tools (111 skills + 4 discovery tools:
list_skills,find_skills,suggest_skills,get_skill_details) - 109 prompts (for clients that surface MCP prompts)
Source code, local setup, and development instructions: mcp-server/
See CONTRIBUTING.md for inclusion criteria. The standard is high intentionally — every skill must be grounded in named evidence, honestly rated, and practically useful. The library's value depends on its rigour.
After creating or editing a SKILL.md, run these steps before committing:
# 1. Regenerate the registry
python3 scripts/generate-registry.py
# 2. Rebuild the MCP server bundle — required after every skill addition or revision
cd mcp-server && npm run bundle-skills && cd ..
# 3. Stage both generated files alongside the skill
git add skills/<domain>/<skill-name>/SKILL.md registry.json mcp-server/src/skills.jsonWhy the bundle step matters: the MCP server running on Vercel does not read SKILL.md files at deploy time. It serves a pre-built snapshot at mcp-server/src/skills.json. If you add or revise a skill without rebuilding the bundle and committing the result, the change will not appear in the live server — even after a Vercel redeploy. CI will catch this and fail the build.
Built by Gareth Manning — educator, curriculum designer, and learning systems designer. 20 years of international education experience across 27 countries.
CC BY-SA 4.0. Open. Forkable. Share alike.