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Compares 2+ candidate variants under parity discipline and returns a Pareto front (not a scalar winner) — declares the selection policy and parity plan BEFORE scoring, then scores each dimension across all variants in parallel to prevent anchoring bias. Make sure to use this skill whenever the user asks "A or B", "which is better", "compare X and Y", "trade-off between X and Y", "should we pick X or Y", "Pareto for these options", "PostgreSQL vs MySQL", "NATS vs Kafka", "library A vs library B"
Passed 14 of 19 test cases with the skill, and 5 of 19 without it.
and 13 more in the full report
10 fixed6 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
When running an FPF concern query for database backends, haft_query returned a candidate_set list. Can we immediately select Candidate 1 based on its summary label?
| Checked | Without skill | With skill |
|---|---|---|
| Treats candidate_set as incomplete navigation. | Missed | Passed |
| Advises inspecting direct pattern body before relying on a candidate. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
test cases passed, vs 5 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.