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 agentmods add skills/thinkoffapp/ide-agent-kit/agent-puzzlesnpx skills add ThinkOffApp/ide-agent-kit --skill agent-puzzlesgit clone --depth 1 https://github.com/ThinkOffApp/ide-agent-kitWhat 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 | $0.00053 | $0.01462 |
| Opus 5 | $0.00026 | $0.00731 |
| Sonnet 5 | $0.00011 | $0.00292 |
| Haiku 4.5 | $0.00005 | $0.00146 |
Grade A, and why
agent-puzzles 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 2d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 2d ago First seen · 206 lines · 53 tokens per session scan A 25afc9bb2a1b
agent-puzzles is a skill published in the GitHub repository ThinkOffApp/ide-agent-kit (43 stars, last pushed 2d ago), licensed AGPL-3.0. It adds 53 tokens to every session and 1,462 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.
Other skills, from other repositories
access-gevety-health-biomarker-data
Access your Gevety health data - biomarkers, healthspan scores, biological age, supplements, activities, daily actions, 90-day health protocol, and upcoming tests.
novelty-check
Use when checking novelty of a research idea or proposal. Extract core claims, do multi-source arxiv / Scholar / S2 search, grade each claim HIGH/MEDIUM/LOW. 基本就是 ARIS novelty-check skill, 但去掉了 cross-model verification 和 report schema.
acorn-theorem-prover-verifier
Verify and write proofs using the Acorn theorem prover for mathematical and cryptographic formalization. Use when working with Acorn proof files (.ac), verifying theorems, formalizing mathematical or cryptographic protocols, or writing proofs in the Acorn language. Triggers on:…
incident-fix
第2層。査読 RETRY の指摘に対し、追加調査・再実験・artifact 修正、または技術的根拠を示した反論で対応する。調査セッションを継続（resume）し、指摘ごとの対応表をコメント投稿する。scope 拡大はしない。.
sagemath
Run SageMath through an explicit override, a detected environment named sage, or a PATH-resolved command. Use for CTF and cryptography work involving asymmetric cryptography, finite fields, elliptic curves, lattices, polynomial algebra, modular arithmetic, discrete logs, small-root attacks, or PRNG cryptanalysis.
run-pipeline
Run the full data science pipeline: validate raw data, preprocess, engineer features, train model, and evaluate. Use this when you want to execute the end-to-end ML pipeline or re-run it after data or code changes.