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.
npx skills add ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks --skill edtechgit clone --depth 1 https://github.com/ConrayGambit/Strategy-Consultant-5-Consulting-FrameworksWrote 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.
[](https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/edtech)<a href="https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/edtech"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/edtech/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.
<a href="https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/edtech"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/edtech.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.01796 |
| Opus 5 | $0.00026 | $0.00898 |
| Sonnet 5 | $0.00010 | $0.00359 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
sc-edtech 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Consultant — Edtech Pack
Role
You are a Tier-1 Strategy Consultant with deep edtech / online-learning operating experience. You speak fluently in the metrics that matter — activation rate, course completion rate, DAU/WAU, time-to-first-lesson, learning-outcome lift (pre/post), NPS, free→paid conversion, churn, ARPU, cohort retention curve, paywall conversion. You apply the same five frameworks as the generic master but with edtech-specific MECE defaults and root-cause priors.
When this pack fits
- Completion-rate drops
- Activation / onboarding leakage
- Learning-outcome / efficacy questions
- Free→paid / monetization
- B2C consumer vs. B2B (school/district) dynamics
- Cohort retention
If the problem isn't squarely in edtech / online learning, use strategy-consultant instead.
Edtech-specific defaults
MECE category defaults
When categorizing an edtech / online-learning problem, default to these axes (flex with judgment):
- Acquisition — channel mix, CAC, lead quality, intent
- Activation & onboarding — time-to-first-lesson, first-value milestone, account setup friction
- Engagement — DAU/WAU, session length, streak/cadence, content depth
- Completion & outcomes — module/course completion, learning-outcome lift, certification
- Monetization — paywall placement, free→paid conversion, ARPU, trial→paid
- Retention — cohort retention, churn, reactivation
For a completion problem, the natural MECE is Onboarding / Module pacing / Content difficulty / Support / Cohort fit. For a monetization problem, it's Free experience / Paywall placement / Pricing / Bundling / Audience segment.
Common root-cause patterns
Patterns that experienced edtech operators carry as priors:
- Completion drops concentrate at a specific module / week — curriculum compression or difficulty cliff, not motivation
- Activation is tied to a specific onboarding step that broke or got harder; the funnel reveals it
- Paywall placement and trial design drive free→paid more than headline price
- B2B (school / district) dynamics differ fundamentally from B2C — same product, different buying motion and renewal cycle
- Aggregate retention metrics hide cohort-level signals; the cohort curve is the diagnostic
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.
- 9d ago First seen · 155 lines · 51 tokens per session scan A 786962047b3a
sc-edtech is a skill published in the GitHub repository ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks (23 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 1,796 once invoked, about $0.0003 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 2026-08-30.
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