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 yugash007/edu-agent-skills --skill revision-modegit clone --depth 1 https://github.com/yugash007/edu-agent-skillsWrote 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/yugash007/edu-agent-skills/revision-mode)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/revision-mode"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/revision-mode/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/yugash007/edu-agent-skills/revision-mode"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/revision-mode.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 | $0.00028 | $0.00825 |
| Opus 5 | $0.00014 | $0.00413 |
| Sonnet 5 | $0.00006 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
revision-mode 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 11d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Run a structured, prioritized revision session targeting topics most likely to decay, most frequently missed, or most important for an upcoming deadline. Not random review — weighted prioritization to maximize learning per minute.
Activation
- Upcoming deadline (interview, exam, demo) within 1–5 sessions. Learner requests review after a learning period.
weak-area-trackershows accumulated weak areas. 3+ sessions since last review. - Skip if: learner needs new material first. Goal is project implementation →
build-with-me. Only one topic to review → usecheck-understanding/challenge-generatordirectly. - Routing: pull priorities from
weak-area-tracker. Usecheck-understandingandchallenge-generatoras execution vehicles. After 3 consecutive clean revision passes on a topic: mark revision-complete and de-prioritize.
Inputs
- Topics covered (from
learning-memory), active weak areas, upcoming deadline + type, session time budget, self-assessed confidence per topic (optional).
Priority Scoring (1–10)
- Weak area severity: 0–3 (from tracker). Recency (days since last review): 0–3. Deadline relevance: 0–2. Low self-reported confidence: 0–2.
- Score 7+ = must-revise. Score 4–6 = should-revise. Below 4 = defer.
Workflow
- Scope — Gather topics + weak areas. Ask: "Deadline? Least confident topics?" Apply priority scoring.
- Plan — Present focused list: must-revise first, then should-revise if time permits. Cap at 4–5 topics max. State approach per topic: concept check / challenge / flashcard drill.
- Recap — For each topic: ask learner to summarize in 2–3 sentences. Don't re-explain unless summary reveals a gap. 2–3 min cap per recap.
- Test — 1–2 exercises per topic via
check-understandingorchallenge-generator. Mix modes. Run 3–5 due flashcards per topic if available. - Check Exit — Clean pass = correct response with correct reasoning on first attempt. After 3 clean passes across sessions: exit rotation. Below 50% pass rate this session: escalate to
misconception-detector. - Close — Summarize: which topics are revision-complete, which need another round. Update
weak-area-tracker. Recommend next session if deadline still approaching.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 59 lines · 28 tokens per session scan A 58707cf6a62d
revision-mode is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 825 once invoked, about $0.0001 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.
Other skills, from other repositories
learning-coach
A Chinese-language personal coaching guide for learning programming, AI agents, writing, languages, exams, research, or career skills through real projects and tasks. It tracks progress and teaches one useful action at a time.
understand-explain
Use when you need a deep-dive explanation of a specific file, function, or module in the codebase.
ctf-misc
Provides miscellaneous CTF challenge techniques for problems that do not cleanly fit the main categories. Use for encoding puzzles, pyjails, bash jails, RF/SDR, DNS oddities, unicode tricks, esoteric languages, QR or audio puzzles, constraint solving, game theory, unusual sandbox escapes, and hybrid logic puzzles.…
respond-to-eval
Turn student course evaluations (free-text + numeric) into an actionable teaching-improvement plan — the teaching analogue of /respond-to-referees. Clusters comments into themes, separates signal from noise, classifies each theme Keep / Change / Investigate / Out-of-scope, and drafts concrete changes mapped to the…
slide-excellence
Multi-agent comprehensive slide review (visual + pedagogy + proofreading, plus TikZ / parity / substance conditionally). Use when user says "full review", "excellence pass", "comprehensive check", "review everything", "pre-release review", "slide excellence", or before teaching / shipping a deck. Fanout wrapper — for…
create-lecture
Create a new Beamer lecture .tex from source papers and materials, with notation consistency checks and the project's preamble wired in. Use when user says "create a lecture on X", "new lecture from these papers", "start a deck on topic Y", "scaffold a new Beamer file", "build me a lecture from these PDFs". Scaffolds…