AI Feedback Design Principles Agent Skill

Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact.

Context cost
5.8k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 23.1 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.

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.

ai-feedbackautomated-feedbackfeedbackformativehattiellmnarcissshute

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.md23.1 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: ai-feedback-design-principles disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-learning-science/ai-feedback-design-principles" skill_name: "AI Feedback Design Principles" domain: "ai-learning-science" version: "1.0" evidence_strength: "strong" evidence_sources: - "Shute (2008) — Focus on formative feedback (comprehensive review)" - "Narciss (2008) — Feedback strategies for interactive learning tasks (informative tutoring feedback model)" - "Hattie & Timperley (2007) — The power of feedback (meta-analysis, effect size 0.73)" - "Dai et al. (2023) — Can large language models provide useful feedback on research papers? A large-scale empirical analysis" - "Kluger & DeNisi (1996) — The effects of feedback interventions on performance: A historical review and a meta-analysis" input_schema: required: - field: "feedback_scenario" type: "string" - field: "current_feedback_design" type: "string" optional: - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "feedback_goals" type: "string" - field: "system_constraints" type: "string" output_schema: type: "object" fields: - field: "feedback_evaluation" type: "object" - field: "improved_feedback" type: "object" - field: "feedback_type_analysis" type: "object" - field: "implementation_guidance" type: "object" chains_well_with: - "adaptive-hint-sequence-designer" - "formative-assessment-loop-designer" - "intelligent-tutoring-dialogue-designer" - "self-explanation-prompt-designer" - "technological-pedagogical-content-knowledge-developer" teacher_time: "4 minutes" tags: ["feedback", "AI-feedback", "formative", "Shute", "Narciss", "Hattie", "LLM", "automated-feedback"] ---
68# AI Feedback Design Principles
69
70## What This Skill Does
71
72Evaluates a proposed AI feedback design against research criteria for effective automated feedback and suggests specific improvements. This skill takes a feedback scenario (what the student did) and the current or proposed AI response (what the system says), then analyses the feedback against principles from Shute (2008), Narciss (2008), Hattie & Timperley (2007), and emerging LLM feedback research (Dai et al., 2023). The output includes a diagnosis of what's working and what isn't, a redesigned version of the feedback, and practical implementation guidance. The core challenge is that most AI feedback falls into one of two failure modes: it's either too vague to be actionable ("Good effort! Try to improve your argument.") or too specific and does the thinking for the student ("Your thesis should be: Climate change is the defining challenge of our generation because…"). Effective feedback lives in the narrow space between these extremes — specific enough that the student knows what to do, but not so specific that it bypasses the cognitive work that produces learning. AI is specifically valuable here because it can generate feedback at scale, but this makes the design principles even more critical: bad feedback at scale is worse than no feedback at all.
73
74## Evidence Foundation
75
76Hattie & Timperley (2007) conducted a meta-analysis finding that feedback has an average effect size of 0.73 — making it one of the most powerful influences on learning. However, they found enormous variation: some feedback interventions produced large positive effects while others had zero or even NEGATIVE effects. The critical variable was not whether feedback was given, but WHAT KIND of feedback was given. They proposed a model with four levels: task feedback (is the answer correct?), process feedback (what strategies can improve the work?), self-regulation feedback (how can you monitor your own learning?), and self feedback (you're a great student!). Task and process feedback were most effective; self feedback ("Good job!") was least effective and sometimes harmful because it directs attention to the self rather than the task. Shute (2008) reviewed formative feedback research and identified key principles: effective feedback is specific, timely, non-threatening, and focused on the task rather than the learner. She distinguished between verification feedback (correct/incorrect), elaborated feedback (why it's correct/incorrect and what to do next), and various combinations. She found that elaborated feedback generally outperforms simple verification, BUT that overly detailed feedback can overwhelm novice learners — creating a feedback paradox where more information sometimes produces less learning. Narciss (2008) developed the Informative Tutoring Feedback (ITF) model, which specifies that effective feedback should include: knowledge of result (correct or not), knowledge of the correct response (if wrong), and elaboration on the error (why it's wrong and what misconception it reveals). Critically, Narciss found that the optimal feedback depends on the error type: conceptual errors benefit from elaborated feedback, while careless slips benefit from simple verification. Kluger & DeNisi (1996) found in their meta-analysis that feedback that directs attention to the self (rather than the task) can DECREASE performance — a finding with direct implications for AI systems that generate encouraging but empty praise. Dai et al. (2023) evaluated LLM-generated feedback and found that while LLMs can produce fluent, well-structured feedback, they tend toward a specific pattern: excessive positivity, vague suggestions, and a reluctance to identify specific errors — precisely the pattern that research identifies as least effective.
77
78## Input Schema
79
80The teacher must provide:
81- **Feedback scenario:** What the student did. *e.g. "Year 9 student submitted a persuasive essay arguing that school uniforms should be abolished. The argument is passionate but relies entirely on personal anecdotes — no evidence, no counterargument addressed, weak logical structure" / "Year 7 student solved 3x + 5 = 20 and got x = 7 (incorrect — should be x = 5)" / "A-level student wrote a lab report with correct data but a conclusion that doesn't follow from the results"*
82- **Current feedback design:** What the AI currently says or plans to say. *e.g. "Great essay! You clearly feel strongly about this topic. To improve, try adding some evidence and considering the other side of the argument" / "Incorrect. The answer is x = 5. Try again" / "Your conclusion needs work. Think about what your data actually shows"*
83
84Optional (injected by context engine if available):
85- **Student level:** Year group and proficiency
86- **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.md3,377 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.
4,975
on trigger
The instruction body, read only when the skill fires.
2.9%
of a 200k window
Ten skills this size would take about 29% of the window before you open a file.
050k100k150k200k context window

5.8k 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, 23.1 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md23.1 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.

# AI Feedback Design Principles · 5.8k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/ai-feedback-design-principles/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-feedback-design-principles" ] && { [ ! -e .claude/skills/ai-feedback-design-principles ] || { mv .claude/skills/ai-feedback-design-principles "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-feedback-design-principles" .claude/skills/ && echo "Installed .claude/skills/ai-feedback-design-principles"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/ai-feedback-design-principles

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/ai-feedback-design-principles/ 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/ai-feedback-design-principles/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-feedback-design-principles" ] && { [ ! -e ~/.claude/skills/ai-feedback-design-principles ] || { mv ~/.claude/skills/ai-feedback-design-principles "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-feedback-design-principles" ~/.claude/skills/ && echo "Installed ~/.claude/skills/ai-feedback-design-principles" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/ai-feedback-design-principles/ 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.

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/ai-feedback-design-principles

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

The install command always fetches the release that is current the day you run it — v1.0 today — and cannot ask for an earlier one. The copy on your disk does not change until you run it again.

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