configs/hermes/default/skills/research/experiment-log-interview/SKILL.md
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.
npx skillsauth add poorrican/dotfiles experiment-log-interviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Runs the human-facing interview layer for building or updating a single experiment entry.
Use experiment-log-structure as the canonical definition of the artifact itself. This interview skill is intentionally thinner: it focuses on mode detection, questioning, challenge, and incremental writing discipline rather than re-specifying the full experiment-entry schema.
The output still becomes the standard experiment log artifact, but this skill's job is to get accurate, calibrated content into that artifact through a structured Socratic process.
Trigger on requests like: "log this experiment," "write up the results of X," "help me document a backtest," "pre-register this run," "I have results, help me interpret them," "update the entry for...", or whenever the user shares experimental results in a context where they are likely to want a durable record.
Do NOT use for:
experiment-log-structure.Always determine the lifecycle mode before deep questioning:
If the mode is unclear, ask: "Is this a new experiment you haven't run yet, results you're writing up, or a revision to an existing entry?"
Use experiment-log-structure as the source of truth for what each mode implies for the document contract.
After identifying the mode:
experiment-log-structure.references/template.md to a working file (default: experiments/<short-slug>.md unless the user specifies another path).Everything after this point edits the entry itself.
Read references/interview-guide.md once. It contains the per-section question bank and challenge protocol.
Interview discipline:
Mode handling:
Read references/exploration.md for exact prompts.
Three moments are worth the latency:
Condense sub-agent output into short bullets before it enters the document. Bring the most important finding back to the user as a question.
Before finishing:
Then present the completed file path and current lifecycle state.
Pre-registration remains sacred. The structure skill defines the freeze boundary; this interview skill must respect it.
Evidence is shown, not claimed. Push for the comparison behind every claim.
One question at a time. The interview is Socratic, not a survey.
Negative and null results are first-class. Document them with the same rigor.
Sub-agents return summaries, not transcripts. Keep the main thread centered on the user.
The structure lives in the companion skill. If you find yourself re-deriving the experiment-entry schema, reload experiment-log-structure and conform to it.
references/template.md — experiment-entry scaffold copied at the start.references/interview-guide.md — interview questions and challenge prompts.references/exploration.md — sub-agent / scoped-pass prompts.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.
testing
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.
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.