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 TserenTserenov/FMT-exocortex-template --skill discovery-sessiongit clone --depth 1 https://github.com/TserenTserenov/FMT-exocortex-templateWrote 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/tserentserenov/fmt-exocortex-template/discovery-session)<a href="https://agentmods.dev/skills/tserentserenov/fmt-exocortex-template/discovery-session"><img src="https://agentmods.dev/badge/skills/tserentserenov/fmt-exocortex-template/discovery-session/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/tserentserenov/fmt-exocortex-template/discovery-session"><img src="https://agentmods.dev/badge/skills/tserentserenov/fmt-exocortex-template/discovery-session.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 | $0.00121 | $0.01986 |
| Opus 5 | $0.00060 | $0.00993 |
| Sonnet 5 | $0.00024 | $0.00397 |
| Haiku 4.5 | $0.00012 | $0.00199 |
Grade A, and why
discovery-session 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 5d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discovery Session — разговор-распаковка неудовлетворённостей
Носитель: R1 Стратег (DP.ROLE.012). Структура (единый источник): DP.METHOD.053 (метод извлечения НЭП). Обещание: DP.SC.030. Этот навык — одна из витрин метода (локальная, для редактора); серверный многоходовый канал — спин-офф, тот же источник.
Инвариант (вшит в метод)
- Экзоскелет, не автопилот. Усиливаешь мышление пилота сократическими вопросами, НЕ формулируешь неудовлетворённости за него. Без human gate артефакт не считается.
- Модельный порог. Метод объявляет
min_model_tier: opus(provisional). Этот навык запускать только на модели ≥ порога — discovery на слабой модели ломает когнитивную нагрузку (заваливает вопросами или сдаётся рано). - Граница. Заканчиваешь на «контексте приоритетов» (приоритеты + состояние +
системные уровни). Упаковку в РП-неделя с бюджетами НЕ делаешь — это Плановик
(DP.ROLE.066), отдельный разговор (
/strategy-sessionweekly flow).
Шаг 0. Готовность
Проверь модель сессии. Если ниже Opus — сообщи: «Разговор-распаковка требует сильной
модели (порог из метода). Переключись на Opus (/model) и запусти снова.» Стоп.
Если пилот передал сырьё аргументом — прими его как вход. Если нет — собери на Шаге 1.
Шаг 1. Сбор сырья (этап 1 метода)
Прими заметки + рефлексию. Источники: {{WORKSPACE_DIR}}/{{GOVERNANCE_REPO}}/inbox/
(мимолётные заметки), docs/Dissatisfactions.md (накопленное), свободный текст пилота.
Если сырья мало — открытые вопросы (не больше 2-3 за раз):
- «Что за последнюю неделю вызывало беспокойство, раздражение, тревогу?»
- «Где ожидание разошлось с реальностью?»
Шаг 2. Выделение проблем (этап 2 метода)
Для каждого беспокойства — сократически:
- «В чём здесь разрыв между желаемым и текущим?»
- «Из какой роли ты это видишь?» (одна ситуация = проблема для одной роли, возможность для другой)
Фиксируй проблему как ошибку предсказания + роль. Если ошибки нет — это не проблема, а возможность (не тащить в таблицу НЭП).
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.
- 5d ago Changed · +1 lines 51dc819c4feb
- 10d ago First seen · 146 lines · 121 tokens per session scan A 4e953e3cf0f9
discovery-session is a skill published in the GitHub repository TserenTserenov/FMT-exocortex-template (50 stars, last pushed yesterday), licensed MIT. It adds 121 tokens to every session and 1,986 once invoked, about $0.0006 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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Use BEFORE dispatching any subagent that needs to understand the dataset. Generates a high-density, PII-free data profile in Markdown so subagents receive structured context instead of raw data.
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Use whenever building features for time-series or any temporal dataset. Enforces strict temporal integrity: no future data in features, no post-event information, correct CV strategy.
verification-before-delivery
Use when any analysis, model, or report is claimed to be complete. Runs mandatory artifact integrity, statistical evidence, and reproducibility checks before any delivery.