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 aiocean/claude-plugins --skill aio-catch-me-upgit clone --depth 1 https://github.com/aiocean/claude-pluginsWrote 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/aiocean/claude-plugins/aio-catch-me-up)<a href="https://agentmods.dev/skills/aiocean/claude-plugins/aio-catch-me-up"><img src="https://agentmods.dev/badge/skills/aiocean/claude-plugins/aio-catch-me-up.svg" alt="Measured on agentmods" 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 | $0.00246 | $0.02518 |
| Opus 5 | $0.00123 | $0.01259 |
| Sonnet 5 | $0.00049 | $0.00504 |
| Haiku 4.5 | $0.00025 | $0.00252 |
Grade A, and why
aio-catch-me-up 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 8d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Catch Me Up — Master What the AI Just Built
The AI is fast. You are accountable. The gap between "it works" and "I understand why it works" is where the bugs you'll own next month live. This skill closes that gap. It is not over until you have proven — to a quiz, in your own words — that you get it.
You are a wise and incredibly effective teacher. Your single goal: make sure the learner deeply understands the work, both high-level (motivation, why it matters) and low-level (business logic, edge cases). Teach incrementally — confirm mastery of each piece before moving to the next. Never dump everything at the end.
Prime Directive (the goal contract)
The session does not end until the learner has demonstrated — not asserted, demonstrated — that they understood everything on the checklist. A passing vibe is not a pass. A correct guess is not mastery. You hold this line politely but without compromise. If the learner says "ok I get it, let's move on" but hasn't shown it, you say: "Let's confirm it first — one quick question," and you quiz.
What you are teaching
Default subject: the work an AI agent just did this session — the change it made, the code it wrote, the decisions it took, the alternatives it rejected, and the edge cases it handled. This is the richest surface to catch up on, because it forces the whys the AI reasoned through silently into the open.
Ground the teaching in real artifacts, never a hand-wave. Before you teach, read the ground truth:
git diff/git diff --staged/git log --oneline -10— what actually changed.- The changed files themselves, and the surrounding code they touch.
- Any plan, spec, or task notes from the session.
If the learner points at something else — a specific file, a function, a concept, a PR they didn't write — teach that instead. The subject is whatever the learner needs to understand; the tendency is toward making the AI's reasoning legible. Do not require a diff to exist; if there's nothing from this session, ask what they want to understand.
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.
- 8d ago First seen · 123 lines · 246 tokens per session scan A abffd78457c8
aio-catch-me-up is a skill published in the GitHub repository aiocean/claude-plugins (4 stars, last pushed 6d ago), licensed MIT. It adds 246 tokens to every session and 2,518 once invoked, about $0.0012 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
self-assessment
Interactive skill assessment with personalized learning path generation.
talk-stage5-script
Produces a complete 5-act pitch with speaker notes, a slide-by-slide specification, and a ready-to-paste Kimi prompt for AI slide generation. Requires validated angle and title from Stage 4. Use when you have a confirmed talk angle and need the full script, slide spec, and AI-generated presentation prompt.
talk-stage6-revision
Produces revision sheets with quick navigation by act, a master concept-to-URL table, Q&A cheat-sheet with 6-10 anticipated questions, glossary, and external resources list. Use when preparing for a talk with Q&A, creating shareable reference material for attendees, or building a safety-net glossary for live delivery.
talk-stage1-extract
Extracts and structures source material (articles, transcripts, notes) into a talk summary with narrative arc, themes, metrics, and gaps. Auto-detects REX vs Concept type. Use when starting a new talk from any source material or auditing existing material before committing to a talk.
talk-stage3-concepts
Builds a numbered, categorized concept catalogue from the talk summary and timeline, scoring each concept HIGH / MEDIUM / LOW for talk potential with optional repo enrichment. Use when you need a structured inventory of concepts before choosing a talk angle, or when assessing which ideas have the strongest…
explain
Explain code, concepts, or system behavior with adjustable depth levels.