| name | ase-meta-evaluate |
| argument-hint | <request> |
| description | Evaluate alternatives through a weighted multi-criteria decision matrix. Use when the user calls for the *evaluation* of *alternatives*, wants to *compare* things, or asks what the best is from a list of choices.
|
| user-invocable | true |
| disable-model-invocation | false |
| effort | high |
@${CLAUDE_SKILL_DIR}/../../meta/ase-control.md
@${CLAUDE_SKILL_DIR}/../../meta/ase-skill.md
Evaluate Alternatives
Evaluate Alternatives
Your role is an experienced, *expert-level assistant*,
specialized in *evaluating alternatives*.
*Evaluate* *alternatives* through a weighted
multi-*criteria* decision matrix.
1.
- From the $ARGUMENTS, try to derive the overall
reason for the evaluation. If no such reason can be
derived, assume generic comparison.
- Output the determined reason with just the following <template/>
and do not output anything else:
<template>
⚪ **REASON**: *<reason/>*
</template>
</step>
2.
- From the $ARGUMENTS derive the two or more
alternatives (K=1-N) the user wants to be
evaluated. Do not output anything.
- If fewer than two alternatives could be derived (N<2), output the
following <template/> and *stop the entire flow* immediately without
executing any further steps:
<template>
🟠 **ERROR: INSUFFICIENT ALTERNATIVES**: at least two are required for a comparison!
</template>
- For each alternative <alternative-K/> (K=1-N), decide whether
you have all necessary information at hand to give it the proper,
unique, short, and *concise name* <alternative-K/>. If you are
unsure, use the `ase-meta-search` skill (at most one invocation per
alternative, drawing from the *skill-wide shared budget* of at
most 8 `ase-meta-search` invocations in total across STEP 2 and STEP 3
combined) to find out more and adjust the name <alternative-K/>.
If still unsure after the shared budget is exhausted, pick the
best-guess concise name and proceed. Do not output anything.
- For each alternative <alternative-K/> (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 <info-K/> (K=1-N) formatted like `<type/>: <hint/>` where
<type/> is one of `USP`, `Crux`, or `Gotcha` and <hint/> 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* <class-of-alternatives/> is.
Do not output anything.
- For each alternative <alternative-K/> (K=1-N), decide whether
it is a genuine member of <class-of-alternatives/>. If any
<alternative-K/> is *not* a member (i.e. the alternatives are not
mutually comparable within a single class), let <alternative-J/>
(J=1-P, P<=N) be the subset of non-member alternatives, output the
following <template/> and *stop the entire flow* immediately without
executing any further steps:
<template>
🟠 **ERROR: INCOMPARABLE ALTERNATIVES**: *<class-of-alternatives/>*
<for items="<alternative-J/> [...]">
⚑ **<item/>** (*member of a different class*)
</for>
</template>
- Output the determined, individual alternatives as a Markdown
*table* with just the following <template/> and do not output
anything else:
<template>
🔵 **ALTERNATIVES**: *<class-of-alternatives/>*
| ⚑ *Alternative* | ⚖ *Hint* |
| :--------------------- | :-------- |
| ⚑ **<alternative-1/>** | <info-1/> |
[...alternatives K=2-(N-1) for N>2...]
| ⚑ **<alternative-N/>** | <info-N/> |
</template>
</step>
3.
- From the $ARGUMENTS, try to derive the criteria
(L=1-M) for the evaluation. Do not output anything.
- For each criteria <criteria-L/> (L=1-M), decide on its <weight-L/>
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 less 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
<reason/> into account, and use the `ase-meta-search` skill (drawing from
the *skill-wide shared budget* of at most 8 `ase-meta-search` invocations
in total across STEP 2 and STEP 3 combined) to find out about the
potentially still missing criteria and assign their <weight-L/>.
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 <weight-L/> 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.
</step>
4.
- For each alternative (K=1-N) and each criteria
(L=1-M), decide on the evaluation , which
means how good the alternative meets the criteria on a Likert-scale
from { -2, -1, 0, +1, +2 } (from worst, over neutral, to best). Do
not output anything.
- Then, calculate the ratings <rating-K/> (K=1-N) for all
alternatives in a single call by invoking the `decision_matrix(matrix:
[ [ <weight-1/>, <eval-1-1/>, ..., <eval-1-N/> ], ..., [ <weight-M/>,
<eval-M-1/>, ..., <eval-M-N/> ] ])` tool of the `ase` MCP service.
The tool returns an array of N numerical values, where the K-th
entry is the product-sum of all weights <weight-L/> (L=1-M) and
the evaluation <eval-K-L/> (L=1-M) for alternative <alternative-K/>.
Retain the *raw, unrounded* <rating-K/> for use in STEP 5, but
round <rating-K/> 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 <template/> and do not output
anything else. When emitting the table, render *one column per
alternative* <alternative-K/> (K=1-N).
<template>
🔵 **EVALUATION**: *Weighted Multi-Criteria Decision Matrix*
| ⦿ *Criteria* | ⚖ *Weight* | ⚑ **<alternative-1/>** | [...alternatives 2-(N-1)...] | ⚑ **<alternative-N/>** |
| :------------ | ----------: | ---------------------: | ---------------------------: | ---------------------: |
| <criteria-1/> | <weight-1/> | <eval-1-1/> | [...evals 1-2..1-(N-1)...] | <eval-1-N/> |
[...criteria L=2-(M-1) for M>2...]
| <criteria-M/> | <weight-M/> | <eval-M-1/> | [...evals M-2..M-(N-1)...] | <eval-M-N/> |
| **RATING** | | **<rating-1/>** | [...ratings 2-(N-1)...] | **<rating-N/>** |
</template>
</step>
5.
- The best alternative (K=1-N) is the alternative
whose raw, unrounded (i.e. the product-sum from STEP
4, before the display-only rounding) is the maximum rating value
across all alternatives. Do not output anything.
- The second best alternative <alternative-X/> (X=1-N, X != K) is
the alternative whose *raw, unrounded* <rating-X/> 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 <alternative-X/>; the resulting <distance/> and
<percentage/> are unaffected by the choice, so the downstream output
is deterministic. Do not output anything.
- Determine rating distance <distance/> between <alternative-K/> and
<alternative-X/> from their *raw, unrounded* ratings by calculating:
<distance/> = <rating-K/> - <rating-X/>. Do not output anything.
- Determine rating distance percentage <percentage/> between
<alternative-K/> and <alternative-X/> from their *raw,
unrounded* ratings by calculating: <percentage/> = <distance/> /
abs(<rating-K/>). Do not output anything.
- If <rating-K/> is exactly zero, skip the division entirely
and treat <percentage/> as if it were equal to <distance/>
(so a true zero tie with <distance/> = 0 falls into the
*MULTIPLE BEST* branch below, and a non-zero gap with zero
best falls into the *small distance* branch below).
Do not output anything.
- By construction, <rating-K/> is the maximum rating across
all alternatives, so <distance/> >= 0 always holds; using
abs(<rating-K/>) keeps <percentage/> sign-stable across all rating
regimes. Note that when <rating-K/> 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 <percentage/> is less than 0.01 (i.e. <distance/> is
effectively zero relative to abs(<rating-K/>)), stop the flow after
you output just the following <template/> and do not output anything
else:
<template>
🟠 **ERROR**: ✘ *MULTIPLE BEST ALTERNATIVES FOUND*,
⚠ *Please give some hints on the criteria to ensure a single best alternative!*
</template>
- Otherwise, if <percentage/> is less than 0.10, stop the flow after
you output just the following <template/> and do not output anything
else:
<template>
🟠 **BEST ALTERNATIVE**: ⚑ **<alternative-K/>**
⚠ *ATTENTION: small distance to second best alternative!*
</template>
- Otherwise, if <rating-K/> 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 <template/> and do not
output anything else:
<template>
🟠 **BEST ALTERNATIVE**: ⚑ **<alternative-K/>**
⚠ *ATTENTION: all alternatives rate negatively; this is the least-bad choice, not a strong winner!*
</template>
- Otherwise (<percentage/> is greater than or equal 0.10), output
just the following <template/> and do not output anything else:
<template>
🟠 **BEST ALTERNATIVE**: ⚑ **<alternative-K/>**
</template>
</step>
