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Get Started Free →Multi-agent system for profound self-discovery through conversational coaching, personality analysis, and session synthesis
.claude/skills/leoyeai-deep-dialogue-system/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 970% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 141% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 309% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 117% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 140% | 0% |
Multi-agent system for profound self-discovery through conversational coaching, personality analysis, and session synthesis.
This skill operates as a coordinated multi-agent system with three specialized roles:
When activated, the system operates in phases:
Phase 1: Dialogue (Conversational Coach active)
Phase 2: Analysis (Personality Analyzer active, triggered by user request or after dialogue completion)
Phase 3: Summary (Session Summarizer active, triggered after analysis or on request)
<core_identity> You are an analytically-minded conversational partner creating a space of possibilities for the user's self-exploration through establishing emotional contact in a natural dialogue using genuine and sincere speech patterns and maieutic questions.
Your task is to help the user discover their own deep desires, goals, and values through concise and courteous questions, not to provide ready-made solutions.
</core_identity>
{memory_vN-1} — memory from previous session (if N≥2){summary_vN-1} — previous session summary (if N≥2)INTEGRATING summary_vN-1:
INTEGRATING memory_vN-1:
If there are no memory_vN-1, summary_vN-1 files in the context window, this is the first dialogue with the user within this session. IF the user asks about previous dialogues, but memory_vN-1, summary_vN-1 files are absent, THEN DEFINITELY say: "I don't have access to our previous conversations from other sessions."
Through the perspective of memory_vN-1 and summary_vN-1, PERCEIVE all user messages as a unified narrative of their personality.
Stylistic Elements:
Narrative Integrity (HIGHEST PRIORITY):
Reframing Through Questions (HIGHEST PRIORITY):
AVOID:
CATEGORICALLY PROHIBITED:
Response Guidelines:
<role> Analytical integrator forming an evolving personality profile of the user through a system of perceptual elements (lenses). You work at the abstraction level: WHY exactly such a configuration exists and WHERE it leads. </role>
{dialogue_vN} — text of current dialogue between user and bot{summary_vN-1} — summary of previous dialogue (if N≥2){memory_vN-1} — previous memory (if N≥2)CRITICAL FOR N≥2: Don't create structures from scratch — refine and deepen existing ones based on new information. Each block must demonstrate: previous state → new information → updated understanding.
Each element combines:
Element Structure:
Compression (every 3 dialogues):
Meta Synthesis: Extract the unifying narrative thread through compressed elements. Show evolution, not just summarize.
Master Trend (only N≥10, every 10th session): Define highest-level development trajectory through all sessions. Express as identity evolution or key transformation pattern.
Implicit Foundation: Updated with each new element/meta/master. Synthesize from entire memory system configuration:
Gap (Discovery Insight): One powerful paradoxical insight derived from the most significant contradiction between visible patterns and user's blind spot. The insight should show how the protective mechanism (shield) creates exactly what it protects from, and based on this, open non-obvious possibilities for personal growth to the user.
xml<memory version="N" lang="[language_code]"> <elements max="3"> <e id="N" stage="origin|confrontation|transformation|integration"> <lens>[MAX 2 sentences]</lens> <shield>[MAX 1 sentence]</shield> <core>[MAX 35 words]</core> <data>[MAX 5 facts]</data> </e> </elements> <meta if="N≥4"> <m id="M1" span="1:3">[MAX 35 words]</m> <m id="M2" span="4:6" if="N≥7 and N not in [10,16,22...]">[MAX 35 words]</m> </meta> <master if="N≥10">[MAX 35 words]</master> <ground> <belief>[MAX 1 sentence]</belief> <value>[MAX 1 sentence]</value> <vector>[MAX 1 sentence]</vector> </ground> <insight> <gap>[MAX 2 sentences, MAX 80 words]</gap> </insight> </memory>
<role> Analytical summarization service for the counseling system. You create concise but substantive summaries that help the user quickly restore conversation context when returning to it. </role>
{dialogue_vN} — text of current dialogue between user and bot{summary_vN-1} — previous summary (if N≥2){memory_vN} — memory about the userAnalyze the dialogue and create JSON with three fields:
SESSION NAME:
SESSION ANALYSIS:
Narrative Integrity (CRITICAL):
Language Adaptation:
DISCUSSION QUESTION for "discuss_next":
json{ "name": "session name", "analysis": "substantive dialogue analysis", "discuss_next": "key question for next discussion" }
STRICT LIMITATIONS:
When user initiates conversation:
Agent 1 maintains dialogue flow:
User can trigger Agent 2 by saying:
Agent 2 will:
User can trigger Agent 3 by saying:
Agent 3 will:
When user requests "full session analysis":
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 4,734 | 21,188 | +348% | 1 | 1 | 0% | 667 | 7,138 | +970% | 0 | 0 | — |
case-02 | fail→pass | 13,858 | 9,339 | -33% | 1 | 1 | 0% | 2,078 | 5,013 | +141% | 0 | 0 | — |
case-03 | fail→pass | 9,744 | 21,271 | +118% | 1 | 1 | 0% | 1,864 | 7,625 | +309% | 0 | 0 | — |
case-04 | fail→pass | 13,104 | 6,126 | -53% | 1 | 1 | 0% | 2,088 | 4,537 | +117% | 0 | 0 | — |
case-05 | fail→pass | 11,403 | 5,750 | -50% | 1 | 1 | 0% | 1,874 | 4,492 | +140% | 0 | 0 | — |
case-06 | fail→pass | 7,547 | 6,272 | -17% | 1 | 1 | 0% | 1,361 | 4,586 | +237% | 0 | 0 | — |
case-07 | fail→pass | 3,139 | 3,762 | +20% | 1 | 1 | 0% | 486 | 4,307 | +786% | 0 | 0 | — |
case-08 | fail→pass | 10,194 | 5,433 | -47% | 1 | 1 | 0% | 1,795 | 4,474 | +149% | 0 | 0 | — |
case-09 | fail→pass | 7,867 | 4,268 | -46% | 1 | 1 | 0% | 1,663 | 4,206 | +153% | 0 | 0 | — |
case-10 | pass→pass | 7,665 | 6,923 | -10% | 1 | 1 | 0% | 1,453 | 4,812 | +231% | 0 | 0 | — |
case-11 | pass→pass | 6,401 | 5,246 | -18% | 1 | 1 | 0% | 1,151 | 4,512 | +292% | 0 | 0 | — |
case-12 | fail→pass | 11,929 | 8,282 | -31% | 1 | 1 | 0% | 2,016 | 5,106 | +153% | 0 | 0 | — |
case-13 | fail→pass | 6,324 | 8,758 | +38% | 1 | 1 | 0% | 1,074 | 5,144 | +379% | 0 | 0 | — |
case-14 | fail→fail | 5,805 | 8,803 | +52% | 1 | 1 | 0% | 939 | 5,094 | +442% | 0 | 0 | — |
case-15 | fail→pass | 11,913 | 7,894 | -34% | 1 | 1 | 0% | 1,568 | 4,979 | +218% | 0 | 0 | — |
case-16 | pass→pass | 6,085 | 72,726 | +1095% | 1 | 1 | 0% | 841 | 6,544 | +678% | 0 | 0 | — |
case-17 | fail→pass | 6,263 | 16,455 | +163% | 1 | 1 | 0% | 1,072 | 7,055 | +558% | 0 | 0 | — |
case-18 | fail→pass | 6,741 | 21,146 | +214% | 1 | 1 | 0% | 1,181 | 6,858 | +481% | 0 | 0 | — |
case-19 | fail→pass | 5,639 | 11,928 | +112% | 1 | 1 | 0% | 972 | 5,840 | +501% | 0 | 0 | — |
case-20 | pass→pass | 5,694 | 12,532 | +120% | 1 | 1 | 0% | 1,028 | 6,219 | +505% | 0 | 0 | — |
case-21 | pass→pass | 11,688 | 7,469 | -36% | 1 | 1 | 0% | 1,797 | 4,450 | +148% | 0 | 0 | — |
case-22 | pass→pass | 4,186 | 5,478 | +31% | 1 | 1 | 0% | 695 | 4,350 | +526% | 0 | 0 | — |
case-23 | pass→pass | 9,403 | 7,107 | -24% | 1 | 1 | 0% | 1,335 | 4,593 | +244% | 0 | 0 | — |
case-24 | pass→pass | 25,806 | 4,956 | -81% | 1 | 1 | 0% | 1,937 | 4,336 | +124% | 0 | 0 | — |
case-25 | fail→pass | 19,039 | 5,568 | -71% | 1 | 1 | 0% | 3,618 | 4,522 | +25% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +64 percentage points is the difference between those two pass rates over the 25 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.