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SkillsMP has collected 73 skills from Rycen7822/DeepScientist-hermes. Open a skill to review its source and details.
Showing 40 of 73 collected skills.
Use DeepScientist from Codex CLI through the native dsctl adapter for research semantics/provenance: quests, memory, artifacts, experiments, strict literature workflow, paper/resource operations, and formal ds_bash_exec evidence. Routine file/search/edit,…
Use when a quest needs one or more follow-up runs such as ablations, robustness checks, error analysis, or failure analysis after a main experiment.
Use when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.
Use when the quest needs an explicit go, stop, branch, reuse-baseline, write, finalize, reset, or user-decision transition with reasons and evidence.
Use when a quest is ready for a concrete implementation pass or a main experiment run tied to a selected idea and an accepted baseline.
Use when the quest is ready to consolidate final claims, limitations, recommendations, summary state, and graph exports before stopping or archiving.
Use when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before choosing the next anchor.
Use when an algorithm-first quest should manage candidate briefs, optimization frontier, branch promotion, or fusion-aware search instead of the paper-oriented default loop.
Use when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision response.
Run a skeptical evidence-grounded DeepScientist review pass for drafts, paper-like reports, claim scope, and follow-up routing.
Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work.
Use when a quest has enough evidence to draft or refine a paper, report, or research summary without inventing missing support.
Write DeepScientist-aware implementation, experiment-roadmap, code-only, and formal-command plans with TDD and durable quest outputs.
Use when a quest needs one or more follow-up runs such as ablations, robustness checks, error analysis, or failure analysis after a main experiment.
Use when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.
Use when a quest is ready for a concrete implementation pass or a main experiment run tied to a selected idea and an accepted baseline.
Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work.
Use when a quest has enough evidence to draft or refine a paper, report, or research summary without inventing missing support.
Use when a quest needs one or more follow-up runs such as ablations, robustness checks, error analysis, or failure analysis after a main experiment.
Use when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.
Use when the quest needs an explicit go, stop, branch, reuse-baseline, write, finalize, reset, or user-decision transition with reasons and evidence.
Use when a quest is ready for a concrete implementation pass or a main experiment run tied to a selected idea and an accepted baseline.
Use when the quest is ready to consolidate final claims, limitations, recommendations, summary state, and graph exports before stopping or archiving.
Use when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before choosing the next anchor.
Use when an algorithm-first quest should manage candidate briefs, optimization frontier, branch promotion, or fusion-aware search instead of the paper-oriented default loop.
Use when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision response.
Run a skeptical evidence-grounded DeepScientist review pass for drafts, paper-like reports, claim scope, and follow-up routing.
Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work.
Use when a quest has enough evidence to draft or refine a paper, report, or research summary without inventing missing support.
Write DeepScientist-aware implementation, experiment-roadmap, code-only, and formal-command plans with TDD and durable quest outputs.
Execute DeepScientist quest experiment command documents with durable ds_* logging, manifest validation, and cautious baseline-gate handling.
Verify paper reliability through the bundled DeepScientist verifier workflow before using papers as evidence.
Create durable DeepScientist quest handoff documents, AGENTS.md files, researcher packages, and verified sync artifacts.
Execute DeepScientist quest experiment command documents with durable ds_* logging, manifest validation, and cautious baseline-gate handling.
Verify paper reliability through the bundled DeepScientist verifier workflow before using papers as evidence.
Create durable DeepScientist quest handoff documents, AGENTS.md files, researcher packages, and verified sync artifacts.
Execute DeepScientist quest experiment command documents with durable ds_* logging, manifest validation, and cautious baseline-gate handling.
Verify paper reliability through the bundled DeepScientist verifier workflow before using papers as evidence.
Create durable DeepScientist quest handoff documents, AGENTS.md files, researcher packages, and verified sync artifacts.
Run a skeptical evidence-grounded DeepScientist review pass for drafts, paper-like reports, claim scope, and follow-up routing.