majiayu000 @${CLAUDE_SKILL_DIR}/../../meta/ase-control.md @${CLAUDE_SKILL_DIR}/../../meta/ase-skill.md @${CLAUDE_SKILL_DIR}/../../meta/ase-getopt.md
- Output the determined reason with just the following <template/>
and do not output anything else:
<template>
<ase-tpl-bullet-secondary/> **REASON**: *<reason/>*
</template>
</step>
If fewer than two alternatives could be derived (N<2), output the following and stop the entire flow immediately without executing any further steps:
For each alternative (K=1-N), decide whether you have all necessary information at hand to give it the proper, unique, short, and concise name . If you are unsure, use the
ase-meta-searchskill (at most one invocation per alternative, drawing from the skill-wide shared budget of at most 8ase-meta-searchinvocations in total across STEP 2 and STEP 3 combined) to find out more and adjust the name .If still unsure after the shared budget is exhausted, pick the best-guess concise name and proceed. Do not output anything.
For each alternative (K=1-N), decide which one of USP (Unique Selling Point -- what makes it unique), Crux (what you should notice), or Gotcha (what you should not stumble over) is its single most distinguishing perspective, and remember this as an (K=1-N) formatted like
<type/>: <hint/>where is one ofUSP,Crux, orGotchaand is a 1-6 word hint. Do not output anything.For the set of alternatives, decide what the 1-6 word long name of the class of alternatives is. Do not output anything.
For each alternative (K=1-N), decide whether it is a genuine member of . If any is not a member (i.e. the alternatives are not mutually comparable within a single class), collect the subset of non-member alternatives into the space-separated list (the for which the membership decision was negative), output the following and stop the entire flow immediately without executing any further steps:
Output the determined, individual alternatives as a Markdown table with just the following and do not output anything else:
⚑ Alternative ⚖ Hint ⚑ [...alternatives K=2-(N-1) for N>2...] ⚑
For each criterion (L=1-M), decide on its from the value set { 4.00, 2.00, 1.00, 0.50, 0.25 } (from most important, over normal, to less important). Do not output anything.
Ensure the final number of criteria is always within the range of minimum 8 and maximum 12: if fewer than 8 criteria were requested, use the set of alternatives to decide on additional criteria which potentially allow best to triage the alternatives, take the into account, and use the
ase-meta-searchskill (drawing from the skill-wide shared budget of at most 8ase-meta-searchinvocations in total across STEP 2 and STEP 3 combined) to find out about the potentially still missing criteria and assign their .If still under 8 criteria after the shared budget is exhausted, fill the remaining slots from existing knowledge without further searches; if more than 12 criteria were requested, drop the criteria with the smallest until 12 remain. Do not output anything.
To prevent a single high-weight criterion from dominating the weighted sum (the weight set is geometric ×2 while the evaluation Likert scale is linear), assign weight 4.00 to at least one and at most two criteria, and weight 2.00 to at least two and at most three criteria. Symmetrically, to prevent a long tail of negligible-weight criteria, assign weight 0.50 to at most two criteria, and weight 0.25 to at most one criterion. Do not output anything.
Then, calculate the ratings (K=1-N) for all alternatives in a single call by invoking the
ase_decision_matrix(matrix: [ [ <weight-1/>, <eval-1-1/>, ..., <eval-N-1/> ], ..., [ <weight-M/>, <eval-1-M/>, ..., <eval-N-M/> ] ])tool of theaseMCP server. The tool returns an array of N numerical values, where the K-th entry is the product-sum of all weights (L=1-M) and the evaluation (L=1-M) for alternative . Retain the raw, unrounded for use in STEP 5, but round to 2 decimal places for display only when emitting it in the table below. Do not output anything.Output the resulting Weighted Decision Matrix as a Markdown table with just the following and do not output anything else. When emitting the table, render one column per alternative (K=1-N).
⦿ Criteria ⚖ Weight ⚑ [...alternatives 2-(N-1)...] ⚑ [...evals 2-1..(N-1)-1...] [...criteria L=2-(M-1) for M>2...] [...evals 2-M..(N-1)-M...] RATING [...ratings 2-(N-1)...]
The second best alternative (X=1-N, X != K) is the alternative whose raw, unrounded is the second largest rating value across all alternatives. Do not output anything.
If multiple alternatives share the second-largest raw rating, pick any one of them as ; the resulting and are unaffected by the choice, so the downstream output is deterministic. Do not output anything.
Determine rating distance between and from their raw, unrounded ratings by calculating: = - . Do not output anything.
Determine rating distance percentage between and from their raw, unrounded ratings as follows. If is exactly zero, skip the division entirely (it is undefined against a zero anchor) and classify directly by instead of routing a raw rating magnitude through the ratio thresholds below: if is also exactly zero, both best and second best rate zero, which is a genuine tie, so set = 0 to fall into the MULTIPLE BEST branch below; otherwise > 0 means the second best rates strictly negative (since is the maximum), so there is a clear positive gap to a worse runner-up and is an unambiguous winner, hence set = 1 (a sentinel exceeding all small-distance thresholds) to fall through to the plain BEST ALTERNATIVE branch. Do not output anything.
Otherwise ( is non-zero), calculate: = / abs(). Do not output anything.
By construction, is the maximum rating across all alternatives, so >= 0 always holds; using abs() keeps sign-stable across all rating regimes. Note that when itself is negative, the denominator anchors to a poor best rating and small gaps can appear large; the all-negative regime is surfaced as a dedicated warning branch below. Do not output anything.
If is less than zero (i.e. all alternatives rate negatively and the "best" is merely the least-bad), stop the flow after you output just the following and do not output anything else. This all-negative check is evaluated before the small-distance checks below, so that an all-negative alternative set with a small winning gap surfaces the all-negative warning rather than being masked by the multiple-best or small-distance branches:
Otherwise, if is less than 0.01 (i.e. is effectively zero relative to abs()), stop the flow after you output just the following and do not output anything else:
Otherwise, if is less than 0.10, stop the flow after you output just the following and do not output anything else:
Otherwise ( is greater than or equal to 0.10), output just the following and do not output anything else: