configs/hermes/default/skills/research/research-proposal-structure/SKILL.md
Use when an agent needs to produce, update, validate, or normalize a standardized research proposal artifact without running an interview. Defines the canonical structure, confidence-tag semantics, decision logic, and completion checks for proposal.md-style research plans.
npx skillsauth add poorrican/dotfiles research-proposal-structureInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill defines the artifact contract for a standardized research proposal. It is for agents that already have the necessary content and need to turn it into a durable, comparable, machine-readable markdown proposal — or check whether an existing proposal meets the standard.
Use this when the task is to write, clean up, standardize, audit, or update a proposal document. Unlike research-proposal-interview, this skill does not prescribe a Socratic questioning flow. It assumes the content is already available or is being generated by another process.
The governing principle is: specificity decays gracefully with distance from the present. The root question and success criteria stay fixed, immediate experiments are fully specified, and future work is represented as conditional branches instead of fake certainty.
Use when:
Do not use when:
research-proposal-interview.experiment-log-structure.Create or normalize a markdown proposal using references/template.md as the authoritative skeleton.
Default filename: proposal.md in the working directory unless the caller specifies another path.
Every proposal should be legible to:
[conditional] rather than pretending they are committed.Use these tags exactly and consistently:
[committed] — fully specified, ready to execute, owned.[planned] — intended next work, but not yet execution-ready.[conditional] — only makes sense under specific decision-gate outcomes.[speculative] — directional, aspirational, or long-horizon thinking.Downgrade tags when uncertain. Honest tagging is more valuable than impressive-sounding overcommitment.
When revising an existing proposal:
A proposal is ready when:
[planned] when it should be [conditional] or [speculative].references/template.md — canonical proposal skeleton.development
Implement multiple GitHub issues sequentially as stacked branches in separate worktrees, with an implementer sub-agent and an independent reviewer sub-agent per issue. Use when the user gives you two or more dependent issues and asks for them to be implemented in order, or says "stacked branches", "sequential issues", "issue chain", "do these in worktrees", or describes a parent epic with child issues that build on each other. Also reach for this whenever the user wants implementation and verification done by separate agents.
development
Conducts a structured Socratic interview to produce a comprehensive markdown research proposal that handles cascading uncertainty (fixed end-question, branching experiments). Use this skill whenever the user wants to write a research proposal, research plan, study design, experiment plan, thesis proposal, RFC, or "spec out" a research direction — even if they don't explicitly say "interview me." Trigger when the user says things like "help me plan this research", "I want to design experiments for X", "draft a proposal for...", "think through a research direction", or shares a half-formed research idea and asks for help structuring it. The skill interviews the user, challenges their priors with evidence requests and falsifiers, optionally uses sub-agents to explore prior art, and builds the proposal markdown incrementally so context stays clean and the document is always grounded.
testing
Use when an agent needs to produce, update, validate, or normalize a standardized experiment-log entry without running an interview. Defines the canonical structure, pre-registration rules, evidence/interpretation split, calibration tags, and append-only revision model for durable experiment records.
testing
Conducts a structured Socratic interview to produce or update a single experiment log entry — the durable record of what was run, what it showed, and what it means. Use this skill whenever the user wants to log an experiment, write up results, record a backtest, capture a finding, pre-register a run, document a study, or update an existing entry with new results or a revised interpretation. Trigger on phrases like "log this experiment," "write up the results of...", "I ran X, help me document it," "pre-register this," "update the entry for...", or when the user shares results and asks for help interpreting and recording them. The skill enforces the four-way separation between what happened, what it means, what it implies, and what comes next; challenges the user's interpretations with evidence requests and alternative explanations; and writes incrementally to keep context clean and the entry always grounded.