infrastructure-autoresearch
SolidSkill for deterministic AutoResearch readiness planning. Use when adding, validating, or documenting opt-in autoresearch.yaml controls, stage-gate readiness, evidence-grounded claims, artifact readiness reports, or AutoResearchClaw-inspired workflow checks in template projects.
Install
Quality Score: 81/100
Skill Content
Details
- Author
- docxology
- Repository
- docxology/template
- Created
- 11 months ago
- Last Updated
- today
- Language
- Python
- License
- Apache-2.0
Integrates with
Similar Skills
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autoresearch
Autonomous goal-directed iteration loop that continuously improves prompts, templates, configs, or code. Two evaluation modes — deterministic (eval.py with proxy heuristics) or AI judge (LLM rubric scoring). Uses four-way separation in both modes. Inspired by Karpathy's autoresearch.
autoresearch-plan
Runs a Karpathy-autoresearch-style comparative experiment loop over candidate technical approaches (API designs, algorithms, data-flow strategies) after a plan has been reviewed but before it's handed to an implementer. Picks a winning approach against an explicit metric and records the baseline, so downstream implementation isn't built on an unvalidated guess. Use this whenever the user wants to "test approaches before committing", "spike a few options", "compare designs experimentally", mentions autoresearch/Karpathy in a planning context, or after gstack's /autoplan when the plan contains more than one viable technical direction.
auto-research
Autonomous goal-driven orchestrator. Classifies a natural-language goal, routes it to the right etk/ctk skill, confirms the plan, and executes. Use when: user describes a goal not a method, the right skill is unclear, or you want the agent to pick the approach. Triggers on: auto-research, figure out, fix the, improve the, get coverage, design a, build the, make sure, optimize the, why isn't