From ralphharness
This skill should be used when running an interactive interview before a spec phase, gathering requirements through dialogue, asking the user clarifying questions before delegating to a subagent, or when any Ralph phase command (research, requirements, design, tasks) needs adaptive brainstorming dialogue. Covers the 3-phase algorithm (Understand, Propose Approaches, Confirm and Store).
How this skill is triggered — by the user, by Claude, or both
Slash command
/ralphharness:interview-frameworkThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Adaptive brainstorming dialogue algorithm for all spec phases. Each phase command provides its own exploration territory (phase-specific areas to probe).
Adaptive brainstorming dialogue algorithm for all spec phases. Each phase command provides its own exploration territory (phase-specific areas to probe).
Each question must have 2-4 options (max 4). Keep the most relevant options, combine similar ones.
Every question asked via AskUserQuestion in Phase 1 leads with the recommended option (except when options are symmetric, in which case [Recommended] may be omitted):
AskUserQuestion:
question: "[Context-aware question referencing prior answers]. [One sentence rationale for the recommendation.]"
options:
- "[Recommended] [Option text -- the AI's suggested answer]"
- "[Alternative 1]"
- "[Alternative 2 if needed]"
- "Other"
Rules:
[Recommended] is a label prefix on the first option only.[Recommended] label rather than placing it arbitrarily.Example:
AskUserQuestion:
question: "Where should the spec live? You only have one specs directory configured, so the default is fine unless you want to reorganize."
options:
- "[Recommended] ./specs/ (default)"
- "Let me configure a different path"
- "Other"
Before asking any question, determine whether the answer is a codebase fact or a user decision:
Only ask what you cannot discover yourself.
After each response, check for early completion signals using token-based matching:
completionSignals = ["done", "proceed", "skip", "enough", "that's all", "continue", "next"]
tokens = tokenize(userResponse.lower()) # split on whitespace/punctuation
for signal in completionSignals:
if signal in tokens: # exact token match, not substring
-> SKIP remaining questions, move to PROPOSE APPROACHES
Read all available context (.progress.md, prior artifacts, goal text). Build a question tree from the exploration territory with dependency ordering. Traverse the tree: auto-resolve codebase facts via exploration, ask user only about decisions. Each question leads with [Recommended] answer. No fixed question caps. Exit when all nodes resolved or user signals completion.
See references/algorithm.md for full pseudocode.
Synthesize dialogue into 2-3 distinct approaches. Each includes: name, description, trade-offs. Lead with recommendation. Present via AskUserQuestion. Maximum 3 approaches (more causes decision fatigue). Trade-offs must be honest. No straw-man alternatives.
See references/algorithm.md for full pseudocode.
Brief recap to user of key decisions and chosen approach. If user corrects something, update before storing. Store in .progress.md under Context Accumulator pattern.
See references/algorithm.md for full pseudocode.
When user selects "Other": ask a context-specific follow-up (never generic "elaborate"). Reference what the user typed. Continue until clarity or 5 rounds. Do not increment askedCount for follow-ups.
See references/examples.md for example follow-up patterns.
After each interview, update .progress.md: read existing content, append new section under "## Interview Responses" with descriptive keys reflecting what was discussed. Include the chosen approach.
See references/examples.md for storage format.
references/algorithm.md -- Full 3-phase pseudocode (UNDERSTAND decision-tree, PROPOSE APPROACHES, CONFIRM & STORE)references/examples.md -- Example interview questions, "Other" response handling, context storage formatGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.
npx claudepluginhub informatico-madrid/ralph-harness --plugin ralphharness