autonomous-llm-research

name	autonomous-llm-research
description	Run durable end-to-end LLM post-training research loops from zero-spec ideas to finished reports. Use when the goal is autonomous literature review, hypothesis selection, experiment planning, Tinker training, evaluation, checkpointing, and resumable long-running research work.
version	1.0.0
author	Hermes Research Agent
license	MIT
metadata	{"hermes":{"tags":["research","llm","post-training","autonomy","tinker","evaluation"]}}

Autonomous LLM Research

Use this skill as the default operating procedure for Hermes Research Agent.

Core rules

1. Detect the request type first:

• zero_spec: the user gives a broad or vague goal. Start with literature and idea generation.
• partial_spec: the user gives some constraints. Fill the missing dataset, eval, and training details.
• full_spec: the user gives an executable recipe. Execute it unless there is a contradiction or an approval gate.

2. Be state-first:

• Before substantive work, create or resume a project with research_loop or research_state.
• Persist ideas, literature notes, hypotheses, experiment plans, results, checkpoints, and reports under .hermes-research/.
• Treat chat history as ephemeral. Treat on-disk state as canonical.

3. For zero_spec, the sequence is mandatory:

• Search literature and identify concrete gaps.
• Generate 3 candidate ideas.
• Score them on novelty, feasibility, cost, and evaluation clarity.
• Choose one and record why.
• Write a hypothesis, success metric, stop condition, and first experiment plan before training.

4. Never launch training without:

• a written hypothesis
• a written success metric
• a written stop condition
• a persisted experiment plan

5. Use the management layer after each major step:

• Use research_manager(action="triage_literature", ...) to rank papers and identify gap candidates.
• Use research_manager(action="assess_dataset", ...) before trusting newly generated or curated datasets.
• Use research_manager(action="rank_runs", ...) after evaluations to detect regressions versus baseline.
• Use research_manager(action="plan_next_step", ...) before deciding the next experiment.
• Use research_manager(action="write_research_memo", ...) whenever a loop chunk meaningfully changes the project state.

6. Long-running work must be resumable:

• Use research_loop(action="checkpoint_loop", ...) before context or time limits are likely.
• Use research_loop(action="schedule_continuation", ...) or rely on the checkpoint helper to resume in a fresh session.
• When Tinker runs are active, use research_loop(action="monitor_run", ...) so the project can return after hours of work.

When to load other skills

• Load research-idea-generation when the project starts from little or no specification.
• Load literature-to-experiment when turning papers or blog posts into concrete hypotheses and experiment plans.
• Load tinker when preparing or launching post-training runs.
• Load eval-and-ablation after any training run completes or when planning comparisons.
• Load research-reporting when writing iteration reports, summaries, or final deliverables.

Default output expectations

Every completed loop or chunk should leave behind:

• updated project state in .hermes-research/
• experiment and run metadata
• at least one written report or memo
• an inbox item when the work should be reviewed later