How do I install the H Compare skill?

Run npx skillmds@latest add m0n0x41d/h-compare-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is H Compare safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with H Compare?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is H Compare free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published H Compare?

m0n0x41d (@m0n0x41d) published this skill. Their other Agent Skills are listed on their SkillMD profile.

H Compare by m0n0x41d — Agent Skill

Name: H Compare
Author: m0n0x41d

H Compare

Compare two or more existing candidates under an explicit characteristic space, parity basis, and predeclared selection policy. Return trade-offs and a non-dominated set; persistence is conditional and a binding choice requires a direct unambiguous operator request.

m0n0x41d Updated Sep 17, 2026

File contents

h-compare — Compare under parity

Retrieve current FPF source first. Inspect a known SourceID or UnitID with haft_query(action="fpf", mode="inspect", identifier="..."); otherwise use mode="concern" with the comparison question and inspect the direct pattern body. Query returns source candidates, not a selected governing pattern or verdict.

Treat a concern result's candidate_set as incomplete navigation. Before relying on one candidate, inspect its exact identifier and direct pattern body. Keep several candidates live or abstain when the basis is insufficient. Never query after comparison merely to manufacture support for a verdict already reached.

Declare dimensions, indicator roles, parity, missing-data policy, and selection policy before scoring. Apply the same evidence window and scale to all candidates per dimension. Show constraints, dominated candidates, the Pareto front, uncertainty, weakest links, and evidence that could change the result.

This n-candidate comparison is Haft local practice, not automatically an A.19.CPM actual application or an A.19.SelectorMechanism result. Claim exact FPF conformance only when the current use also preserves each required binary CPM application, pair coverage, token-to-producer trace, claim scope and selected context slices, predicate basis, reference plane, evaluation window, and separate eligibility/output bindings recovered from the direct source.

Inline candidates are sufficient; frame and explore are not prerequisites. Persist only for explicit save intent or a concrete operator-named or agent-inferred receiving use supplied by current Work. A durable typed comparison requires an exact U.EntityRef, bounded context, an already projected portfolio, and independently addressable option Haft.ProjectRecordRef values. Preserve returned record_reference values; never derive them from artifact IDs. Missing references make typed projection underdetermined while the legacy carrier remains durable.

Do not use the legacy selected_ref for an ordinary typed comparison. It is excluded from Haft.PortfolioComparison: a Pareto front is not a winner, ChoiceResult, recommendation, or binding decision.

---name: h-compare-2description: Compare two or more existing candidates under an explicit characteristic space, parity basis, and predeclared selection policy. Return trade-offs and a non-dominated set; persistence is conditional and a binding choice requires a direct unambiguous operator request.---# h-compare — Compare under parityRetrieve current FPF source first. Inspect a known SourceID or UnitID with`haft_query(action="fpf", mode="inspect", identifier="...")`; otherwise use`mode="concern"` with the comparison question and inspect the direct patternbody. Query returns source candidates, not a selected governing pattern orverdict.Treat a concern result's `candidate_set` as incomplete navigation. Beforerelying on one candidate, inspect its exact identifier and direct patternbody. Keep several candidates live or abstain when the basis is insufficient.Never query after comparison merely to manufacture support for a verdictalready reached.Declare dimensions, indicator roles, parity, missing-data policy, and selectionpolicy before scoring. Apply the same evidence window and scale to allcandidates per dimension. Show constraints, dominated candidates, the Paretofront, uncertainty, weakest links, and evidence that could change the result.This n-candidate comparison is Haft local practice, not automatically an`A.19.CPM` actual application or an `A.19.SelectorMechanism` result. Claim exactFPF conformance only when the current use also preserves each required binaryCPM application, pair coverage, token-to-producer trace, claim scope andselected context slices, predicate basis, reference plane, evaluation window,and separate eligibility/output bindings recovered from the direct source.Inline candidates are sufficient; frame and explore are not prerequisites.Persist only for explicit save intent or a concrete operator-named oragent-inferred receiving use supplied by current Work. A durabletyped comparison requires an exact `U.EntityRef`, bounded context, an alreadyprojected portfolio, and independently addressable option`Haft.ProjectRecordRef` values. Preserve returned `record_reference` values;never derive them from artifact IDs. Missing references make typed projectionunderdetermined while the legacy carrier remains durable.Do not use the legacy `selected_ref` for an ordinary typed comparison. It isexcluded from `Haft.PortfolioComparison`: a Pareto front is not a winner,ChoiceResult, recommendation, or binding decision.

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