ase-meta-quotes

@${CLAUDE_SKILL_DIR}/../../meta/ase-control.md @${CLAUDE_SKILL_DIR}/../../meta/ase-skill.md @${CLAUDE_SKILL_DIR}/../../meta/ase-getopt.md

<purpose name="ase-meta-quotes"> Find Quotes on a Topic </purpose>

<expand name="getopt" arg1="ase-meta-quotes" arg2="--ground|-g --proximity|-p --count|-c=8"> $ARGUMENTS </expand>

<objective> *Find* quotes for the following topic keywords: <keywords><getopt-arguments/></keywords> </objective> <flow>

1. <step id="STEP 1: Sanity Check Usage">
   1. <if condition="<keywords/> is empty"> Only output the following <template/> and then immediately *STOP* processing the entire current skill: <template> ⧉ **ASE**: ✪ skill: **ase-meta-quotes**, ▶ ERROR: expected a `<topic-keywords>` argument </template> </if>

   2. Set <quotes></quotes> (set to empty).

   3. Determine the maximum total number of quotes to surface: set <count/> to <getopt-option-count/>; if <getopt-option-count/> is non-numeric or less than or equal to 0, use the default 8 instead.

   </step>
2. <step id="STEP 2: Harvest Quotes">

   1. Determine quotes -- sayings, aphorisms, maxims, proverbs, and citations -- which are about the topic <keywords/>, and store them in <quotes/>. Per quote, record its text, its author (a named person or organization, if any is known), its origin (a named work, standard, or document, if any is known), and <keywords/> as its source topic.

   2. <if condition="<getopt-option-ground/> is equal `true`">

      Additionally -- and never instead -- gather quotes from the Internet/Web by using the ase-meta-search skill in a sub-agent with the following tool call:

      Agent( description: "Query Web Search Service", subagent_type: "ase:ase-meta-search", prompt: "Search the Internet/Web and gather quotes about the following topic: <keywords/>", run_in_background: false )

      Merge the returned quotes into <quotes/>, deduplicating quotes which differ only in punctuation, capitalization, or attribution wording, and remember for every quote whether the search confirmed its exact wording and attribution.

      <if condition="the sub-agent returned no usable quotes"> Output the following <template/> and continue with the model knowledge only: <template> <ase-tpl-bullet-secondary/> **WARNING**: grounding found no usable quotes -- falling back to model knowledge. </template> </if> </if>
   </step>
3. <step id="STEP 3: Widen Topic via Proximity" condition="<getopt-option-proximity/> is equal `true`">

   1. Set <prompt><keywords/></prompt>.

   2. <if condition="<getopt-option-ground/> is equal `true`"> Set <prompt>GROUND <keywords/></prompt>, so the agent grounds its determination in Internet/Web facts instead of using model knowledge only. </if>

   3. Determine the conceptual neighborhood of <keywords/> by using the ase-meta-proximity agent in a sub-agent with the following tool call:

      Agent( description: "Determine Conceptual Proximity", subagent_type: "ase:ase-meta-proximity", prompt: "<prompt/>", run_in_background: false )

   4. <if condition="the sub-agent returned no usable neighborhood"> Output the following <template/>, *SKIP* the remaining sub-steps of this step, and continue with the quotes harvested in STEP 2 only: <template> <ase-tpl-bullet-secondary/> **WARNING**: proximity agent returned no usable result -- keeping the narrow topic only. </template> </if>

   5. Parse the returned labeled list and set <neighborhood/> to the values of its PARENT: line (the broader topic), of its four SIBLING: lines (the same-level topics), and of its four CHILD: lines (the narrower topics).

   6. Harvest quotes for each topic of <neighborhood/> exactly as in STEP 2 (Harvest Quotes), record the contributing neighborhood topic as the source topic of each of those quotes, and merge the results into <quotes/>.

   </step>
4. <step id="STEP 4: Classify and Render Quotes">

   1. Classify Quotes:

      Classify every quote of <quotes/> along two orthogonal axes:

      • ATTRIBUTION: A quote is ATTRIBUTED if a named author and/or a named origin is known for it, and ANONYMOUS otherwise.

      • LITERALNESS: A quote is LITERAL if its text contains at least one of the topic keywords of <keywords/> as a whole word -- matched case-insensitively and tolerating inflections (e.g. architect and architectural match the keyword architecture), but never as a mere substring (e.g. art does not match architecture). A quote is THEMATIC otherwise.

      Both axes span the four quadrants:

      • Q1 (ATTRIBUTED and LITERAL)
      • Q2 (ATTRIBUTED and THEMATIC)
      • Q3 (ANONYMOUS and LITERAL)
      • Q4 (ANONYMOUS and THEMATIC)

   2. Finally, reduce <quotes/> to at most <count/> quotes in total, distributed as evenly as possible across the four quadrants and preferring the most relevant and most well-known quote per quadrant.

   3. Render Quotes:

      Render every quote on its own ○-prefixed line, with the following suffixes appended in this order:

      • — *<author/>*, <origin/> in the two ATTRIBUTED quadrants, omitting whichever of <author/> and <origin/> is unknown.

      • [from proximity:<source-topic/>] if the quote was harvested for a neighborhood topic in STEP 3 rather than for <keywords/> itself.

      • *(unverified)* if the exact wording or the attribution of the quote could not be established with confidence -- but omit this marker for a quote whose wording and attribution the Internet/Web search of STEP 2.2 confirmed, including its re-application for a neighborhood topic in STEP 3.

      Render the single line ○ (none) for a quadrant without any quote. Output the result with the following <template/>:

      <template> <ase-tpl-head title="QUOTES"/>

      ● Q1 - ATTRIBUTED / LITERAL: ○ [...]

      ● Q2 - ATTRIBUTED / THEMATIC: ○ [...]

      ● Q3 - ANONYMOUS / LITERAL: ○ [...]

      ● Q4 - ANONYMOUS / THEMATIC: ○ [...]

      <ase-tpl-foot title="QUOTES"/> </template>
   </step>

</flow>