Formative Assessment Loop Designer for AI Systems Agent Skill

Design an adaptive assessment loop where each student response triggers the next instructional move.

Context cost
7.1k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 28.5 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

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

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

adaptiveassessment-loopblack-wiliamfeedbackformative-assessmentinner-loopouter-loopvanlehn

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md28.5 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: formative-assessment-loop-designer disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-learning-science/formative-assessment-loop-designer" skill_name: "Formative Assessment Loop Designer for AI Systems" domain: "ai-learning-science" version: "1.0" evidence_strength: "strong" evidence_sources: - "Black & Wiliam (1998) — Assessment and classroom learning (seminal meta-analysis)" - "Black & Wiliam (2009) — Developing the theory of formative assessment" - "Wiliam (2011) — Embedded formative assessment" - "VanLehn (2006) — The behavior of tutoring systems (inner loop vs. outer loop)" - "Shute & Zapata-Rivera (2012) — Adaptive educational systems" input_schema: required: - field: "learning_objective" type: "string" - field: "current_assessment_approach" type: "string" optional: - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "ai_system_capabilities" type: "string" - field: "class_size" type: "string" - field: "assessment_frequency" type: "string" output_schema: type: "object" fields: - field: "assessment_loop_design" type: "object" - field: "elicitation_strategies" type: "array" - field: "interpretation_framework" type: "object" - field: "response_actions" type: "object" chains_well_with: - "adaptive-hint-sequence-designer" - "ai-feedback-design-principles" - "learning-analytics-interpretation-guide" - "cognitive-tutoring-architecture-designer" teacher_time: "5 minutes" tags: ["formative-assessment", "Black-Wiliam", "assessment-loop", "adaptive", "feedback", "VanLehn", "inner-loop", "outer-loop"] ---
70# Formative Assessment Loop Designer for AI Systems
71
72## What This Skill Does
73
74Designs a complete formative assessment loop for an AI-enabled learning environment — the continuous cycle of eliciting evidence of student understanding, interpreting that evidence, and using it to adjust instruction in real time. Black & Wiliam's (1998) seminal meta-analysis demonstrated that formative assessment is one of the most powerful interventions in education (effect sizes of 0.40-0.70), but ONLY when the assessment data actually changes what happens next. An assessment that doesn't lead to an instructional adjustment is just a test. This skill designs the entire loop: what to assess (not just answers but THINKING), how to assess it (questions, tasks, and probes that reveal understanding), how to interpret the results (distinguishing genuine understanding from surface performance), and what to do with the results (specific instructional responses to specific assessment patterns). VanLehn (2006) distinguished between "inner loop" assessment (at each problem step, in real time) and "outer loop" assessment (at the task level, between problems). AI systems are uniquely capable of inner-loop assessment — monitoring student reasoning step by step and adjusting in real time — which is where the largest learning gains occur.
75
76## Evidence Foundation
77
78Black & Wiliam (1998) conducted the most influential review of formative assessment research, analysing over 250 studies and finding effect sizes ranging from 0.40 to 0.70 — larger than most educational interventions. They defined formative assessment as "all those activities undertaken by teachers, and/or by their students, which provide information to be used as feedback to modify the teaching and learning activities in which they are engaged." The critical insight: the ASSESSMENT is not the valuable part — the MODIFICATION is. Data collection without instructional adjustment is summative assessment with a different label. Black & Wiliam (2009) developed a more refined theoretical framework, identifying five key strategies of formative assessment: (1) clarifying and sharing learning intentions and criteria for success, (2) engineering effective classroom discussions and tasks that elicit evidence of learning, (3) providing feedback that moves learners forward, (4) activating students as instructional resources for one another, and (5) activating students as owners of their own learning. All five strategies involve a cycle: elicit → interpret → act. Wiliam (2011) translated this framework into practical classroom strategies, emphasising that formative assessment must be EMBEDDED in instruction — not an add-on activity but a continuous process of checking, adjusting, and responding. He argued that the biggest barrier to effective formative assessment is not data collection but DATA USE: teachers often collect data but don't change their teaching in response to it. VanLehn (2006) analysed the behaviour of tutoring systems and identified two levels of assessment loop. The "outer loop" operates between problems: after a student completes a problem, the system decides what to do next (another similar problem, a harder problem, a review, or a new topic). The "inner loop" operates within problems: at each step, the system assesses the student's response and provides feedback, hints, or scaffolding. VanLehn found that the inner loop was the more important determinant of ITS effectiveness — systems that assessed and responded at the step level dramatically outperformed systems that only assessed at the problem level. Shute & Zapata-Rivera (2012) reviewed adaptive educational systems and found that the most effective systems combined continuous assessment with immediate instructional adaptation — creating a "tight" formative assessment loop where the time between assessment and response was minimised.
79
80## Input Schema
81
82The teacher must provide:
83- **Learning objective:** What students are trying to learn. *e.g. "Understanding the difference between area and perimeter — when to use each, how to calculate each, and why they are independent (a shape can have a large area but small perimeter, and vice versa)" / "Writing effective topic sentences that make a clear claim, not just state a fact or announce a topic" / "Understanding supply and demand — how price changes affect quantity supplied and demanded, and how equilibrium price is determined"*
84- **Current assessment approach:** How understanding is currently checked. *e.g. "I check at the end of the lesson with an exit ticket — 3 questions. If most students get them right, I move on. If not, I reteach next lesson" / "I mark essays at the end of the week and give written feedback" / "The AI system gives a quiz after every 10 problems, but doesn't change what happens next based on the results"*
85
86Optional (injected by context engine if available):
87- **Student level:** Year group and proficiency
88- **Subject area:** The curriculum subject
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md4,085 words
Files1
Licencenot declared
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈800
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
6,325
on trigger
The instruction body, read only when the skill fires.
3.6%
of a 200k window
Ten skills this size would take about 36% of the window before you open a file.
050k100k150k200k context window

7.1k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

1 file, 28.5 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md28.5 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 1 file you can review in full before installing.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Formative Assessment Loop Designer for AI Systems · 7.1k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/formative-assessment-loop-designer-for-ai-systems/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/formative-assessment-loop-designer-for-ai-systems" ] && { [ ! -e .claude/skills/formative-assessment-loop-designer-for-ai-systems ] || { mv .claude/skills/formative-assessment-loop-designer-for-ai-systems "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/formative-assessment-loop-designer-for-ai-systems" .claude/skills/ && echo "Installed .claude/skills/formative-assessment-loop-designer-for-ai-systems"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/formative-assessment-loop-designer-for-ai-systems

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/formative-assessment-loop-designer-for-ai-systems/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/formative-assessment-loop-designer-for-ai-systems/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/formative-assessment-loop-designer-for-ai-systems" ] && { [ ! -e ~/.claude/skills/formative-assessment-loop-designer-for-ai-systems ] || { mv ~/.claude/skills/formative-assessment-loop-designer-for-ai-systems "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/formative-assessment-loop-designer-for-ai-systems" ~/.claude/skills/ && echo "Installed ~/.claude/skills/formative-assessment-loop-designer-for-ai-systems" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/formative-assessment-loop-designer-for-ai-systems/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as formative-assessment-loop-designer in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, formative-assessment-loop-designer-for-ai-systems.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Version1.0
Publishedno release date on file
PriceFree
Referencegarethmanning/formative-assessment-loop-designer-for-ai-systems

Versions

v1.0 is what is on the shelf; no release here carries a date. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

v1.0
  • No earlier releases have been published to the marketplace.
Pinning

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