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Developmental Band Translator

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GarethManning
developmental-band-translator

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.

Overview

PublisherGarethManning
Repositoryeducation-agent-skills
Skill namedevelopmental-band-translator
Stars
847
Forks
122
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by GarethManning on GitHub. Read the source before you install it.

Installation

Install the Developmental Band Translator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/GarethManning/education-agent-skills/tree/main/skills/curriculum-alignment/developmental-band-translator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GarethManning/education-agent-skills.git /tmp/education-agent-skills
mkdir -p .claude/skills
cp -r /tmp/education-agent-skills/skills/curriculum-alignment/developmental-band-translator .claude/skills/developmental-band-translator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Developmental Band Translator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Developmental Band Translator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Developmental Band Translator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

What This Skill Does

This skill tags an external curriculum — already decomposed by the curriculum harness into KUD items, learning targets, and (optionally) criteria — with a school's developmental band labels. The school's band schema is supplied as an input; the skill reads it, derives a mapping table from the source's age ranges to the school's band ranges, and applies that mapping across every item. Every item receives a school_band value, a confidence level, and diagnostic flags. The source's own band labels are preserved verbatim alongside the new school band tags, so the output can always be traced back to, and interpreted against, the original framework.

The skill is a metadata operation, not a content operation. It does not rewrite KUDs, LTs, or criteria, and it does not reinterpret the source's intent. Its only job is to answer the question: "If a teacher planning for a given school band opened this framework, which of its content is relevant to them?" The skill is deliberately conservative where a source band spans two school bands: it assigns both candidate bands, sets an ambiguity flag, and records a rationale. Downstream skills — especially the Curriculum Crosswalk skill and the human planning process — resolve these ambiguities with teacher judgement.

The skill handles three common source-structure scenarios. Most sources have an explicit band structure (Welsh CfW Progression Steps, NZ Curriculum Levels, IB PYP/MYP/DP phases) which maps cleanly with occasional boundary ambiguity. Some have a formal-but-compressed structure (UK RSHE has four Key Stage labels but only two actual age bands in the statutory document), where the skill applies the source's real 2-band structure first and records the formal label via source_band_preserved. A minority have no band structure at all (CASEL, some competency frameworks), where the skill maps by developmental descriptor rather than age and flags every item for teacher review.

Evidence Foundation

Wiggins & McTighe (2005, 2011) — Understanding by Design establishes KUD (Know / Understand / Do) as the unit-level architecture through which framework content becomes teachable, and positions curriculum alignment as a backward-design operation from stated outcomes. The Developmental Band Translator operates on exactly the KUD layer that UbD specifies, and treats school-band assignment as one step in a backward-design pipeline whose final consumer is the classroom teacher.

Vygotsky (1978) — The Zone of Proximal Development grounds the concept that developmental banding is not a pure age-partition. A learner in a given band is defined by what they can do with support, not only by their chronological age. This justifies the skill's use of both age-range and developmental-descriptor evidence when tagging, and its willingness to assign a school band from descriptor-based reasoning alone when age evidence is absent.

Heritage (2008) — Learning progressions literature reinforces that progressions are developmental trajectories, not calendars. The skill treats school bands as trajectory-waypoints. Where a source's native progression is coarser than the school's (e.g. RSHE's 2-band structure vs a 6-band school schema), the skill preserves the coarseness honestly via ambiguity flags rather than forcing false precision.

Webb (1997) — The canonical alignment framework defines four alignment dimensions: categorical concurrence, depth-of-knowledge consistency, range-of-knowledge correspondence, balance of representation. Band translation addresses primarily the range dimension (do the age spans match?) with partial touch on categorical concurrence. The skill does not claim to assess depth or balance alignment; this limit is reported in skill_flags and in Known Limitations.

Input Schema

Required inputs. The skill needs four inputs:

  • band_schema — The school or programme's developmental band definitions. Must include band labels, approximate age or grade ranges, and any named groupings. The skill reads this to build the mapping table; it does not assume any particular band system. Example: "REAL School Budapest uses Bands A–F — A (Water+Air Dragons, K–2, ages 5–7), B (Earth Dragons, G3–4, ages 7–9), C (Fire Dragons, G5–6, ages 9–11), D (Metal+Light Dragons, G7–8, ages 11–13), E (G9–10, ages 13–15), F (G11–12, ages 15–17)."
  • kud_json — The KUD file. Required fields per item: item_id, content_statement, knowledge_type, source_block_id. The skill uses source_block_id to look up the block's native band label in the progression structure.
  • lts_json — The LT file. Required fields per LT: lt_id, lt_name, lt_definition, kud_item_ids, knowledge_type. LTs inherit their band from their constituent KUD items using a max-span rule (see Prompt).
  • progression_structure_json — The progression structure. Required: band_labels, source_type, and either a top-level age_range_hint or per-band approximate_age_range. If both age-range fields are absent, the skill returns request_age_range_context in skill_flags and halts.

Optional inputs.

  • criterion_bank_json — If supplied, criteria are also tagged. Criteria inherit their parent LT's band unless their content narrows to a more specific age-range signal.
  • source_name — A friendly name for output metadata.
  • user_age_range_override — A user-supplied age-range mapping, used when the source has no native band structure and the first invocation halted with request_age_range_context.

Worked example of required inputs. For a REAL School Budapest run against uk-statutory-rshe: band_schema = "REAL School Budapest uses Bands A–F — A (Water+Air Dragons, K–2, ages 5–7), B (Earth Dragons, G3–4, ages 7–9), C (Fire Dragons, G5–6, ages 9–11), D (Metal+Light Dragons, G7–8, ages 11–13), E (G9–10, ages 13–15), F (G11–12, ages 15–17)"; kud_json = the 279-item KUD file; lts_json = the 44-LT file; progression_structure_json = a structure declaring band_labels: ["End of Primary (Year 6)", "End of Secondary (Year 11)"], source_type: "national_statutory_curriculum", and age ranges 5–11 and 11–16 respectively.

Prompt

You are a curriculum band-translation assistant. Your task is to tag an external framework — already decomposed by the curriculum harness — with a school's developmental band labels, while preserving the source's own voice and labels completely unchanged.

School band schema

Read the supplied band_schema carefully. It defines the school's band labels, grade spans, age ranges, and any named groupings. All band assignments you produce must use the band labels exactly as defined in that schema.

Procedure

  1. Read the progression structure. Note source_type, band_labels, and age ranges. If both age_range_hint and per-band approximate_age_range are absent, emit request_age_range_context in skill_flags, do not tag anything, and stop.
  2. Build a band-mapping table for this source. Using the band_schema and the source's per-band age ranges, identify which school bands overlap with each source band. Where a source band's age range spans multiple school bands, list all overlapping school bands as candidates and note the ambiguity. Where the source band maps cleanly to one school band, record low ambiguity. Record this mapping table in your reasoning before proceeding; every tagging decision in steps 3–5 must trace back to it.
  3. Tag every KUD item.
    • Look up its source_block_id to find the source band.
    • Apply the band-mapping table to get candidate school bands.
    • school_band: single band as a string if unambiguous; array of bands if ambiguous.
    • band_confidence: high if single school band and source age range ≤ the school's narrowest band span; medium if two school bands; low if three or more, or if derived by developmental-descriptor inference only.
    • source_band_preserved: the exact native label from the source (e.g. "Key Stage 2", "End of Primary", "Progression Step 3").
    • source_voice_preserved: always true. If you would need to rewrite the content to make it fit a school band, do NOT rewrite — instead set this to false and add a skill flag. This field exists to prove no rewriting occurred.
    • ambiguity_flag: true if school_band is an array.
    • teacher_review_flag: true when (a) school_band spans three or more bands, (b) confidence is low, (c) source_type is a dispositional/competency framework with no age-banding (e.g. CASEL), or (d) the content statement contains developmental language that would narrow the band beyond what the source label allows.
    • band_rationale: one sentence explaining the assignment.
  4. Tag every LT. An LT's school band is the union of its constituent KUD items' bands (max-span rule). If any constituent is ambiguous, the LT is ambiguous. teacher_review_flag fires if any constituent has it or if the LT aggregates across three or more school bands.
  5. Tag criteria (if criterion_bank_json supplied). Each criterion inherits its parent LT's band unless its content narrows it. Same field set as LTs.
  6. Emit summary counts and skill flags. Count items per school band, ambiguity flags, teacher-review flags, and low-confidence tags. Emit a skill flag for any source where >50% of items are ambiguous (signals a structural mismatch worth reviewing).

Source-voice preservation rule (hard constraint)

You are tagging content, not rewriting it. Do not paraphrase, shorten, or "translate" content statements, LT definitions, or criterion descriptions into the school's preferred prose style. Keep source content exactly as written. The only new text you generate is band_rationale.

CASEL / flat-framework handling

If source_type indicates a competency or dispositional framework with no age structure (e.g. CASEL), and no user_age_range_override was supplied:

  • Set teacher_review_flag: true on every item.
  • Set band_confidence: low on every item.
  • Base school_band on the developmental sophistication of the descriptor, mapped to the school bands from the supplied band_schema: early-developmental content (emotion-labeling, basic self-regulation) → earliest bands in the schema; mid-developmental content (perspective-taking, collaborative problem-solving) → mid-range bands; late-developmental content (systems-level social awareness, abstract ethical reasoning) → later bands in the schema.
  • Emit skill_flag: casel_style_framework_full_teacher_review_required.

Inputs

  • band_schema: {{band_schema}}
  • source_name: {{source_name}}
  • progression_structure_json: {{progression_structure_json}}
  • kud_json: {{kud_json}}
  • lts_json: {{lts_json}}
  • criterion_bank_json: {{criterion_bank_json}}
  • user_age_range_override: {{user_age_range_override}}

Produce the band-tagged output as a single JSON object conforming to the output schema. Do not rewrite source content. Do not invent bands the source cannot support.

Example Output

Context: REAL School Budapest band schema supplied as band_schema. Source: UK DfE Statutory RSHE (July 2025), progression_structure.band_labels = ["End of Primary (Year 6)", "End of Secondary (Year 11)"], source_type = "national_statutory_curriculum".

Inputs supplied:

json
{
  "band_schema": "REAL School Budapest uses Bands A–F — A (Water+Air Dragons, K–2, ages 5–7), B (Earth Dragons, G3–4, ages 7–9), C (Fire Dragons, G5–6, ages 9–11), D (Metal+Light Dragons, G7–8, ages 11–13), E (G9–10, ages 13–15), F (G11–12, ages 15–17). Bands are defined primarily by grade level; ages are approximate.",
  "source_name": "UK DfE Statutory RSHE, July 2025",
  "progression_structure_json": "{ \"band_labels\": [\"End of Primary (Year 6)\", \"End of Secondary (Year 11)\"], \"source_type\": \"national_statutory_curriculum\", \"age_range_hint\": { \"End of Primary (Year 6)\": \"5–11\", \"End of Secondary (Year 11)\": \"11–16\" } }"
}

Output:

json
{
  "source_metadata": {
    "source_name": "UK DfE Statutory RSHE, July 2025",
    "source_type": "national_statutory_curriculum",
    "source_band_labels": ["End of Primary (Year 6)", "End of Secondary (Year 11)"],
    "skill_version": "1.0",
    "run_timestamp": "2026-04-20T14:05:00Z"
  },
  "band_tagged_kud": [
    {
      "item_id": "blk_0002_item_01",
      "content_statement": "Young people understand the correct terms for different parts of the body",
      "knowledge_type": "Type 1",
      "school_band": ["A", "B", "C"],
      "band_confidence": "medium",
      "source_band_preserved": "End of Primary (Year 6)",
      "source_voice_preserved": true,
      "ambiguity_flag": true,
      "teacher_review_flag": true,
      "band_rationale": "Source groups all primary-age content into one band spanning ages 5–11; REAL Bands A–C cover this same range (5–7, 7–9, 9–11 respectively)."
    },
    {
      "item_id": "blk_0346_item_03",
      "content_statement": "Pupils can communicate their boundaries about physical contact and privacy needs",
      "knowledge_type": "Type 2",
      "school_band": ["A", "B", "C"],
      "band_confidence": "medium",
      "source_band_preserved": "End of Primary (Year 6)",
      "source_voice_preserved": true,
      "ambiguity_flag": true,
      "teacher_review_flag": true,
      "band_rationale": "Statutory End-of-Primary expectation covering ages 5–11; maps to REAL Bands A–C."
    },
    {
      "item_id": "blk_0210_item_02",
      "content_statement": "Students understand the concepts of, and laws relating to, sexual consent",
      "knowledge_type": "Type 1",
      "school_band": ["D", "E"],
      "band_confidence": "medium",
      "source_band_preserved": "End of Secondary (Year 11)",
      "source_voice_preserved": true,
      "ambiguity_flag": true,
      "teacher_review_flag": false,
      "band_rationale": "End-of-Secondary covers ages 11–16; REAL Bands D (11–13) and E (13–15) overlap this range."
    }
  ],
  "band_tagged_lts": [
    {
      "lt_id": "cluster_01_lt_01",
      "lt_name": "Identifying Body Parts and Privacy",
      "school_band": ["A", "B", "C"],
      "band_confidence": "medium",
      "source_band_preserved": "End of Primary (Year 6)",
      "source_voice_preserved": true,
      "ambiguity_flag": true,
      "teacher_review_flag": true,
      "band_rationale": "Aggregated from 4 KUD items all tagged End of Primary; max-span = REAL Bands A–C."
    }
  ],
  "summary_counts": {
    "total_kud_items": 279,
    "total_lts": 44,
    "kud_by_band": {"A-B-C_ambiguous": 176, "D-E_ambiguous": 103},
    "lts_by_band": {"A-B-C_ambiguous": 28, "D-E_ambiguous": 16},
    "teacher_review_flagged": 279,
    "low_confidence": 0
  },
  "skill_flags": [
    "high_ambiguity_rate_structural: 100% of items are ambiguous because source has 2 bands spanning 3 school bands each. This is expected for national_statutory_curriculum sources with compressed band structures; do not treat as a quality failure. Downstream planning must resolve via teacher judgement."
  ]
}

Known Limitations

The skill does not do construct alignment. Two items tagged to the same school band are not, by that fact alone, teaching the same thing. Webb's (1997) categorical-concurrence and depth-of-knowledge dimensions are out of scope. The skill answers "is this age-relevant?" not "does this match the school's learning intentions?" The Curriculum Crosswalk skill addresses relationships between frameworks but still requires human judgement for construct alignment.

Ambiguity is not resolvable by this skill. When a source's band structure is coarser than the school's — particularly UK RSHE's 2-band structure or any framework with fewer bands than the school schema — the skill deliberately preserves the ambiguity rather than guessing. High ambiguity rates (up to 100% for RSHE against a 6-band schema) are an honest report of the source's granularity, not a quality failure. They are resolved downstream by teacher judgement, which is why teacher_review_flag exists.

Developmental-descriptor inference (CASEL case) is approximate. When a framework has no age-banding, the skill maps by descriptor using the supplied band_schema as a guide — early-developmental descriptors → earliest bands in the schema; systems-level descriptors → later bands. These are defensible starting points drawn from developmental-psychology consensus, but they are not framework-specific determinations. Content tagged this way should be treated as a scaffold for teacher review, not a validated mapping.

The skill assumes harness-validated input. It does not re-validate the source decomposition. If the upstream harness produced a broken KUD or LT file (cycles in prerequisite graph, content_statement missing, etc.), the skill will pass those errors through as band-tagged nonsense. Run the harness's own validation gates before invoking this skill.

Frequently asked questions

What does the Developmental Band Translator AI skill do?

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.

Why use Developmental Band Translator on TypingMind?

Because you install it once and use it with any model. Developmental Band Translator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Developmental Band Translator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GarethManning/education-agent-skills/tree/main/skills/curriculum-alignment/developmental-band-translator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Developmental Band Translator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Developmental Band Translator?

As many as you like. As long as a model supports skills, you can use Developmental Band Translator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Developmental Band Translator AI skill free?

It is published on GitHub by GarethManning. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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