h-explore

Generates 3-5 distinct candidate approaches with weakest links for a current engineering question.

1.4k|102|Updated Dec 10, 2025
One-click install
npx skills add https://github.com/m0n0x41d/haft --skill h-explore
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: h-explore
Source: https://github.com/m0n0x41d/haft/tree/main/internal/cli/skill/h-explore
Command: npx skills add https://github.com/m0n0x41d/haft --skill h-explore

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

When tackling an engineering problem, teams often fixate on the first plausible approach and stop searching. This Skill forces divergent thinking by generating 3-5 genuinely distinct candidate approaches for a current question, keeping each option's weakest link and evidence gap visible so premature convergence is avoided.

Core Features & Use Cases

  • Divergent option generation: Produces candidates that differ in kind, not degree, each with a title, mechanism, expected benefit, weakest link, and evidence gap.
  • FPF-grounded retrieval: Queries the Haft MCP server for First Principles Framework patterns and project memory to ground exploration in the current entity of concern.
  • Gated persistence: Returns candidates conversationally by default, and only persists a SolutionPortfolio with per-candidate Notes when an explicit save intent or reliance-bearing receiving use exists.
  • Use Case: While deciding how to structure a new caching layer, ask for alternatives and receive several architecturally distinct options, each annotated with its main risk, instead of a single recommendation.

Quick Start

Ask the agent to use h-explore to generate distinct candidate approaches for your current design question, keeping each option's weakest link visible.

Frequently Asked Questions about h-explore

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate alternative approaches for a design problem?

Invoke h-explore with your current question, cue, or problem reference. It produces 3-5 candidates that differ in kind rather than degree, each with a mechanism, expected benefit, weakest link, and evidence gap, returned conversationally by default.

When should I use option generation instead of comparison or diagnosis?

Use h-explore when the current need is generating options, not diagnosing a failure or comparing already available alternatives. Comparison, decision, planning, and implementation are separate later steps, not automatic follow-ups.

Does h-explore save the generated candidates automatically?

No. Candidates stay conversational unless there is explicit save intent or a concrete reliance-bearing receiving use. Only then does it persist one Note per candidate and a SolutionPortfolio through the Haft MCP tools.

What happens if project memory is unavailable during exploration?

Unavailable project basis, known absence, or explicit abstention is non-blocking. The exploration continues without inventing profiles, entities, or artifacts, and no typed memory is persisted merely because a read failed.

What are the limitations of candidate generation with h-explore?

Candidate generation is not a recommendation, choice, authorization, or work plan. It preserves uncertainty and stops once the option field is adequate; it does not select, rank for decision, or implement any variant.