القائمة
Deep research using ChatGPT + NotebookLM + domain knowledge — cross-model validation for high-quality answers and continuous improvement
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when a pull request needs review by a different AI model than the one that authored it, or when configuring cross-model code review pipelines in CI/CD.
Run Demerzel's autonomous driver — full cycle or individual phases across all repos
Capture meaningful session state (current cursor, in-flight work, live hypotheses, open questions, do-NOT-carry-forward, success criteria) to state/digests/latest.md so the next session — including one after auto-compaction — can re-enter without re-discovering context cold. Distinct from /learnings (which captures surprises). Validates against docs/contracts/digest-schema.json.
Autonomous research scheduler — selects the most under-researched department by coverage ratio, generates questions from completeness gaps, cross-model validates, auto-produces courses at T>=0.8, rate-limited to 6 cycles/day with kill switch
Personalized AI tutor — turns any topic into a customized, progress-tracked learning course. Use when the user wants to LEARN a concept, skill, language, tool, or domain. Creates a MISSION, curated RESOURCES, iterative lessons with self-checks, and learning-records that persist across sessions. NOT for interrogating the user about their own design (use brainstorming/IDSD for that).
Run one bounded supervised autonomous cycle on the Demerzel governance repo. Picks the smallest unchecked governance slice inside allow_edit, implements it, runs the oracle (`pwsh scripts/verify.ps1`), emits cycle evidence, and stops. Refuses to run unless dev-process-overseer reports loop-eligible and the preflight passes. Use when asked to "run one supervised loop", "advance governance autonomously", or "do one cycle and stop".
| name | seldon-research |
| description | Deep research using ChatGPT + NotebookLM + domain knowledge — cross-model validation for high-quality answers and continuous improvement |
Combines ChatGPT (second opinion), NotebookLM (institutional memory), and GA domain knowledge to produce high-quality, validated answers. Every research interaction compounds back into the governance framework.
/seldon research [question] — full research pipeline
/seldon research quick [question] — fast answer without cross-model validation
1. Classify question (governance/experiential/domain)
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2. Check NotebookLM for existing knowledge
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3. Query GA domain skills if music-related
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4. Cross-validate with ChatGPT (GPT-4o-mini)
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5. Assess confidence via tetravalent logic
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6. Deliver answer with full agentic trace
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7. Compound: log interaction, update evolution, detect patterns
Determine knowledge layer:
Query the most relevant notebook:
mcp__notebooklm__ask_question({
question: "[processed question]",
notebook_id: "[best match]" // compound-the-compounding, probabilistic-grammars, etc.
})
If the notebook has relevant context, incorporate it into the answer with citations.
For music theory questions, invoke GA skills conceptually:
These produce deterministic answers (confidence 1.0).
For non-trivial questions, ask GPT-4o-mini to validate:
mcp__openai-chat__openai_chat({
model: "gpt-4o-mini",
messages: [
{ role: "system", content: "You are a music theory expert. Verify this answer is accurate. Be concise." },
{ role: "user", content: "[answer to validate]" }
]
})
Rate the answer:
Include full agentic trace showing which sources were used, which models validated, and confidence level.
Every research interaction feeds back:
personas/seldon.persona.yaml, policies/streeling-policy.yaml, policies/scientific-objectivity-policy.yaml (confidence calibration)