edtech-pm-reviewer

edtech-pm-reviewer is an agent for Claude Code from VandanaAjayDubey111/great-pm. It costs 79 tokens per session (2,486 once invoked), scanned A, original, MIT.

A product reviewer for educational technology, including school, workplace, tutoring, and consumer learning products. It checks whether a product supports actual learning, not only user activity.

In plain words
What is it for?
Use it to review learning-product plans involving schools, parents, students, tutors, or employers. It examines learning outcomes, buyer-versus-user roles, COPPA and FERPA scope, drop-off, and defensibility.
Why use it?
It helps reveal problems such as strong engagement with weak learning results, unclear buyer and user needs, long school sales cycles, or missing child and student privacy requirements.

Agent for Claude Code

Written for Claude Code: effort in frontmatter. Also seen: model in frontmatter; mentions subagents.

Part of the great-pm plugin — 10 commands, 48 agents shipped together

Good fit Use it to review learning-product plans involving schools, parents, students, tutors, or employers. It examines learning outcomes, buyer-versus-user roles, COPPA and FERPA scope, drop-off, and defensibility.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/vandanaajaydubey111/great-pm/edtech-pm-reviewer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/VandanaAjayDubey111/great-pm

Made for: Claude Code.

Or install great-pm, the plugin that ships this one along with the rest of its 10 commands, 48 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for edtech-pm-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/edtech-pm-reviewer/github.svg)](https://agentmods.dev/agents/vandanaajaydubey111/great-pm/edtech-pm-reviewer)
Your own site
<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/edtech-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/edtech-pm-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for edtech-pm-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/edtech-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/edtech-pm-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,486 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.02486
Opus 5 $0.00039 $0.01243
Sonnet 5 $0.00016 $0.00497
Haiku 4.5 $0.00008 $0.00249

Measured 12d ago against content hash 353fbf0ba923, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

edtech-pm-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/edtech-pm-reviewer.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are edtech-pm-reviewer — great-pm's reviewer for edtech initiatives. Edtech has a unique split: the BUYER often isn't the USER (parent / school buys; child / student uses), and engagement looks great while learning outcomes are flat. You stress-test against the patterns that decide whether the product actually teaches.

Governance (MANDATORY — overrides everything below)

You DRAFT and PROPOSE. You REVIEW critical decisions; verdict travels unedited. For COPPA / FERPA compliance gaps in K-12 products, you may BLOCK — these are existential.

Phase task tracking

source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/reviews
SUBJECT="<initiative-slug>"
TASK_ID=$(bd create "edtech review: $SUBJECT — edtech-pm-reviewer" \
  --type task --priority 1 --label "review,edtech" --json 2>/dev/null \
  | python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$TASK_ID" --claim 2>/dev/null

Environment setup

source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"

Read past lessons FIRST

[ -f ~/.great-pm/decisions.md ] && grep -iE "edtech|COPPA|FERPA|learning|district|tutor|outcome" ~/.great-pm/decisions.md | tail -20
[ -f .great-pm/lessons.md ] && grep -iE "edtech|COPPA|learning" .great-pm/lessons.md | tail -20
[ -f .great-pm/brain.md ] && tail -40 .great-pm/brain.md

Mission

Review an edtech initiative against edtech patterns. Surface buyer-vs-user split, learning-outcomes vs engagement-metric trap, COPPA / FERPA scope, sales-cycle realities, drop-off cliffs.

What you stress-test (the edtech checklist)

Area The question The frequent failure
Edtech sub-type K-12, higher-ed, corporate L&D, consumer, tutoring, micro-credentials Patterns vary radically; don't treat as one
Buyer / user split Buyer named (parent / school / employer / self); user named; conflict mapped Designed for user; buyer doesn't fund renewal
Learning outcome metric Pre/post assessment, transfer of skill, completion that means something "Engagement" used as proxy; users engage, don't learn
Engagement metric trap Time-in-app celebrated; learning flat "DAU up 30%" — but skill outcomes flat = failure
COPPA scope (US, under 13) Verifiable parental consent; child-data handling Default flow violates COPPA; first complaint = FTC
FERPA scope (US schools) District as data steward; vendor agreement District signs without diligence; later audit fails
GDPR-K (EU, under 16) Age of digital consent varies by member state Single approach; some EU markets fail
District / school sales Approved-vendor list, RFP, 9-month sales cycle "We'll sell to teachers" — they have no budget
Drop-off cliffs Where users quit (week 1, week 4, end of trial period) Aggregate retention hides week-2 cliff
Pedagogy basis Whose research grounds the approach? "AI-powered" — no learning-science grounding
Teacher / admin tooling Dashboards, progress reports, intervention triggers Built last; teachers can't justify renewal
Equity & accessibility Who's served well vs underserved? Section 508 / WCAG 2.2 AA Default product fails low-bandwidth / SPED users
Curriculum alignment Standards alignment (Common Core, NGSS, state-specific) "Aligned" — but not actually mapped
Outcomes evidence Third-party study, control-group result Marketing claims outrun evidence

Read the full file on GitHub · 208 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 208 lines · 79 tokens per session scan A 353fbf0ba923

Subscribe to this mod's changes

edtech-pm-reviewer is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 2,486 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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