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sophia
Discover your philosophical tradition through behavioral dimension analysis and philosopher matching.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Discover your philosophical tradition through behavioral dimension analysis and philosopher matching.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | sophia |
| description | Discover your philosophical tradition through behavioral dimension analysis and philosopher matching. |
Discover your philosophical tradition through behavioral pattern analysis.
φιλο-σοφία = "love of wisdom" Your conversation patterns carry the fingerprint of a philosophical tradition.
Invoke this skill when:
Skip when:
| Phase | What | Mode |
|---|---|---|
| 1. Collect | Gather behavioral data from sessions | dimension-profiler agent |
| 2. Match | Map dimension profile to philosophers | AI analysis |
| 3. Present | Dual-layer result + protocol affinity | Gate interaction |
| 4. Report | Generate HTML profile card | Automated |
Same-session reuse: If dimension-profiler output is already available in this
conversation (from a prior /sophia or /curses run), skip Phase 1 entirely and
reuse that output. Both skills produce identical profiler results.
Two-step delegation: first run coverage-scanner for pre-aggregated data, then pass
the result to dimension-profiler for dimension scoring. This avoids duplicate file
reading and gives the profiler access to protocol usage counts.
Step 1: Run coverage-scanner agent (see agents/coverage-scanner.md):
Aggregate facets, session-meta, and slash command data across all sessions.
Return structured coverage data including protocol_counts, friction_summary,
session_type_distribution, tool_counts, and satisfaction_distribution.
Step 2: Pass coverage output to dimension-profiler agent (see agents/dimension-profiler.md):
Analyze this user's behavioral dimensions from their session data.
coverage_data: [paste coverage-scanner output here]
data_sources:
rules_dir: ~/.claude/rules/
claude_md: ~/.claude/CLAUDE.md
settings_json: ~/.claude/settings.json
data_context: session-enriched
Return the dimension profile table with scores, confidence, and raw signals.
When coverage_data is provided, omit sample_size — the profiler derives
dimensions from aggregate data and does not sample raw files.
If coverage-scanner returns no data (new user with no sessions), skip Step 1 and
run dimension-profiler with data_sources only (rules + CLAUDE.md). Note reduced
confidence in results.
Read references/philosophers.md for the full philosopher database.
| Data quality | Match confidence |
|---|---|
| 20+ facets sampled, all dimensions measured | High |
| 10-19 facets, most dimensions measured | Medium |
| <10 facets or rules-only analysis | Low — note limitations |
Present the match progressively — start with an accessible introduction, then build to detailed analysis. Users may not know the philosopher; the opening should stand on its own without assumed knowledge.
A warm, 2-3 sentence introduction that explains the match in plain language:
Your thinking patterns most resemble the tradition of [Philosopher Name]
([dates], [tradition in one phrase]).
[1-2 sentences: what this philosopher is known for, in terms a non-philosopher
would understand. Connect to the user's actual behavior — not abstract philosophy.]
Similarity: 0.XX | Runner-up: [Name] (0.XX)
Show dimensions with human-readable explanations (from dimension-profiler output) so users understand what each bar means:
──────────────────────────────────────
D1 How you approach problems: ████████░░ 78 (hypothesis-first)
D2 How much you check: █████████░ 85 (thorough)
D3 How you interact: ██████░░░░ 62 (moderate)
D4 How you govern work: █████████░ 91 (systematic)
D5 Where you focus: ████████░░ 78 (exploring unknowns)
D6 How you use AI: █████████░ 88 (distributed thinking)
──────────────────────────────────────
Protocol affinity: /[command] ([protocol name] — [Greek])
What would you like to explore?
1. **Deep dive** — Why this tradition fits your patterns, and what it reveals
2. **Compare** — Side-by-side with the runner-up
3. **Protocol** — Try the suggested protocol now
4. **Report** — Generate HTML profile card
If the user selects "Report", generate an HTML profile card.
Read the existing CSS design system from ~/.claude/usage-data/report.html or
use the design tokens from the cooperative's dashboard/report templates.
Sections:
~/.claude/usage-data/sophia-profile.html
Open in browser: open <filepath>Use CSS clip-path: polygon(...) with computed vertices for a hexagonal radar chart.
Each vertex position is calculated from the dimension score (0-100 mapped to center-to-edge).
Work-unit triage for GitHub issues. Groups raw issues, fuses each group with the AGENTS.md northstar in session, and composes /distill to hand off each routed work unit.
Review markdown or HTML artifacts before fixation (publish/commit/deposit/merge) via /inquire × /sublate × /gap × /contextualize through a channel-first browser preview loop. Markdown renders via marked; HTML serves directly through a Shadow DOM. Each round mode is `apply + scan` (apply queued comments now + scan to surface findings into the next round's sidepanel) or `apply` (apply only). User-invoked via /comment-review.
Scoped empirical validation utility. Decomposes a target↔surrogate equivalence claim into verifiable facets, bounds a user-synchronized test space, captures evidence inside it, and carries the uncovered complement forward. Use when an inference-uncertain proposition (does it behave / perform / transfer / hold value) needs evidence in a smaller stand-in space, and you want the claim scoped to the conditions actually tested rather than asserted absolutely.
Convergence-paced code/PR review-resolve loop via /review-loop. Drives a pluggable review source (codex | code-review), passing it the design intent already captured for the changed surface (relevant project rules + adjacent design comments + decisions constituted at the loop's own gates, with a mission-anchored severity steer) so intentional documented choices are pre-filtered upstream, verifies each finding against the codebase (/inquire) and work-flow (/contextualize), auto-applies Mechanical fixes (Extension) and gates Judgment fixes by shared disposition (Constitution), risk-screens applies (substrate risk → harness permission; epistemic risk → direct Constitution), then re-reviews until the source verdict converges to approve — recurring signals escalate to constituted design decisions rather than suppression, keeping the reviewer independent. At exit, converged or free, a decision constituted at its gates is offered for durable record on the home its reach implies (for a PR scope, the issues the PR clo
Deficit Recognition Probe — surface multiple deficit hypotheses for the user's current situation and route by user-constituted recognition (fit review, not protocol scoring).
Distill a session-tethered working context into a self-contained portable handoff a fresh zero-memory agent can execute from. Closes deictic, grounding, relevance, provenance, and compression gaps, then emits a prose channel plus a schema-versioned TaskStateBlock. Type: (ContextTethered, AI, DISTILL, WorkingContext) → PortableHandoff. Alias: Diylisis(διύλισις).