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 serejaris/personal-corp-os --skill planninggit clone --depth 1 https://github.com/serejaris/personal-corp-osWrote 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/serejaris/personal-corp-os/planning)<a href="https://agentmods.dev/skills/serejaris/personal-corp-os/planning"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/planning/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/serejaris/personal-corp-os/planning"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00045 | $0.01331 |
| Opus 5 | $0.00023 | $0.00665 |
| Sonnet 5 | $0.00009 | $0.00266 |
| Haiku 4.5 | $0.00005 | $0.00133 |
Grade A, and why
planning 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning
Планирование смотрит вперёд и не выполняет запланированную работу.
Пререквизит
До начала найди принятый отчёт reports/ГГГГ-WNN-retro.html за предыдущую неделю.
Если ретро не завершено или не принято, остановись и предложи сначала запустить retro.
Граница
Планируй только текущий отдел. Не обходи штаб и соседние отделы. GitHub разрешён только как активный источник задач текущего отдела, указанный в tasks/README.md.
Источниками состояния остаются локальные записи задач или GitHub Issues согласно активному режиму. HTML-план — представление выбранных результатов и ссылок.
Этап 1. Собрать вход
Прочитай:
AGENTS.md,README.mdи блок "Активный режим" вtasks/README.md;- принятый отчёт ретро;
- все открытые задачи из активного источника;
- решения по незавершённым задачам;
- последнее принятое изменение правил.
Для планирования открыты только задачи со статусом новая, в работе, на приёмке или заблокирована. Задачи завершена и снята, а также закрытые GitHub Issues не являются обязательствами новой недели.
Не переноси открытую задачу автоматически. Используй решение из ретро. Если в режиме GitHub Issues остался хотя бы один локальный файл со статусом новая, в работе, на приёмке или заблокирована, остановись до устранения конфликта источников.
Этап 2. Выбрать результаты недели
Результат недели описывает проверяемое состояние, а не действие.
Плохо:
Поработать над рассылкой.
Хорошо:
Первое письмо отправлено выбранному сегменту, ссылка и результат проверки записаны в задаче.
Предложи варианты на основе фактов, затем задавай founder по одному вопросу для решений, которых нет в источниках.
Для каждого принятого результата зафиксируй:
- что должно стать правдой;
- как это проверить;
- к какой дате;
- какая запись задачи владеет состоянием;
- что сознательно не входит в неделю.
Для текущей Области зафиксируй отдельную цель: какое изменение должно стать заметно к концу недели. Возьми предложение из Area Goals прошлого ретро и попроси founder принять или изменить его.
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 · 123 lines · 45 tokens per session scan A 6949a2397027
planning is a skill published in the GitHub repository serejaris/personal-corp-os (225 stars, last pushed 15d ago), licensed MIT. It adds 45 tokens to every session and 1,331 once invoked, about $0.0002 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
concept-to-video
Turn concepts into animated explainer videos using Manim (Python) with MP4/GIF output, audio overlay, multi-scene composition. Triggers on: "create a video", "animate this", "make an explainer", "manim animation", "motion graphic". NOT for React video, use remotion-video.
manuscript-provenance
Computational provenance audit verifying every number, table, and figure in a manuscript derives from code, not manual entry. Triggers on: "check provenance", "verify reproducibility", "audit my pipeline", "are my numbers from code", "provenance audit". Companion to manuscript-review (prose audit).
manuscript-review
Pre-publication manuscript audit producing a section-level refactoring report with citation hygiene and submission-readiness checks. Triggers on: "review my paper", "check before submission", "is this ready to submit", "pre-pub checklist", "refactor my paper", "check my references", "does the abstract work".
figure-rhetoric
Evaluate whether figures and plots in a manuscript effectively communicate the claims they support. Audits chart-type fit, axis design, visual hierarchy, data density, caption interpretation, perceptual accuracy, and narrative arc across 8 dimensions. Triggers on: "do my figures work", "check my plots", "are my graphs…
humanize
Detects and removes AI-generated writing patterns while preserving meaning and facts. Triggers on: "humanize text", "make this sound human", "remove AI patterns", "rewrite to sound natural", "make this less AI", "de-slop this", "not sound like ChatGPT", "human pass".
notebooklm
Full NotebookLM API via notebooklm-py CLI: create notebooks, add sources, generate podcasts, videos, infographics, slides, quizzes, flashcards, mind maps. Triggers on: "notebooklm", "create a podcast", "audio overview", "generate flashcards", "generate infographic", "/notebooklm".