--- name: style-evolution-observer description: > Observe creative outputs and infer aesthetic preferences from user behavior — acceptance, revision patterns, explicit praise, and abandonment. Update the aesthetic-identity profile with dimensional shifts, confidence changes, and discovered dimensions. Activates after any creative output (artifact, design, visualization) to capture what was built and how the user responded. metadata: author: nirav version: "1.0" compatibility: Designed for Claude Code allowed-tools: Read Write Glob Grep --- # Style Evolution Observer — The Silent Curator Watch. Measure. Learn. Update. This observer tracks the user's evolving aesthetic by reading behavioral signals — not asking questions. The goal: after enough outputs, the aesthetic-identity profile should predict the user's preferences before they state them. ## When to Activate After any creative output that involves visual/aesthetic decisions: - Artifact creation (master-artificer) - Design direction (design-orchestrator) - Generative art - Dashboard or visualization styling - Any output where palette, composition, typography, or motion choices were made ## The Inference Protocol ### Signal Types (strongest → weakest) **Strong positive signals:** - User explicitly praises an aspect: "love this palette", "the motion is perfect" - Output is used without modification - User shares or references the output later **Moderate positive signals:** - User accepts the output with unrelated changes (content edits, not style edits) - User builds on the output in subsequent requests - No revision requested — silence after delivery **Moderate negative signals:** - User asks for specific style changes: "make it warmer", "too busy", "less playful" - User provides alternative references: "more like X instead" - User adjusts palette, density, or motion after delivery **Strong negative signals:** - User rejects the direction entirely: "start over", "not what I had in mind" - User abandons the output without using it - User overrides creative direction with very specific instructions (implies defaults were wrong) ### Mapping Signals to Dimensions Each signal maps to one or more dimensions: | Signal | Dimension(s) affected | Direction | |--------|----------------------|-----------| | "too busy" / "too much" | Density | → Sparse | | "too empty" / "needs more" | Density | → Rich | | "make it warmer" / "too cold" | Temperature | → Warm | | "more contrast" / "pops more" | Contrast | → Bold | | "too flashy" / "tone it down" | Contrast, Emotional Register | → Muted, → Serious | | "love the animation" | Motion Feel | reinforce current | | "too much movement" | Motion Feel | → Static | | Chose geometric shapes | Geometry | → Geometric | | Chose organic/natural forms | Geometry | → Organic | | Used monospace/grid layout | Precision | → Mechanical | | Used handwritten/textured feel | Precision | → Expressive | | Referenced retro/vintage | Temporal Register | → Retro | | Referenced futuristic/novel | Temporal Register | → Futuristic | This table is not exhaustive — the observer should map any aesthetic feedback to the most relevant dimension(s). If feedback doesn't fit existing dimensions, flag it as a candidate for dimension discovery. ## Update Protocol ### After Each Output 1. **Capture** — record the output's aesthetic position - What palette was used? (hex values, temperature, contrast level) - What composition approach? (density, symmetry, depth) - What motion? (amount, speed, easing) - What typography? (classification, weight, precision) - What mood? (emotional register, information stance) 2. **Observe** — read the user's response - Did they accept, modify, or reject? - What specific changes did they request? - What language did they use? (capture to mood vocabulary) - Did they reference any influences? 3. **Map** — translate observations to dimensional updates - For each affected dimension, calculate the signal strength and direction - Positive signals: move position toward the output's value, increase confidence - Negative signals: move position away from the output's value, increase confidence - No signal: slight confidence decay (recency weighting) 4. **Update** — write changes to the aesthetic-identity references - Update `dimension-registry.md` positions and confidence scores - Update `current-profile.md` narrative summary if any dimension crossed a threshold - Append to `evolution-log.md` if a meaningful change occurred - Update the Palette Library table in `current-profile.md` if a new palette was used - Add to mood vocabulary if new terms appeared - Add to influences if new references were cited ### Confidence Scoring ``` volume = log(n + 1) / log(21) # 0→0, 5→0.42, 12→0.67, 20→1.0 consistency = 1 - stdev(observations) / 0.5 # 1.0 if all identical, 0.0 if spread ≥0.5 recency = weighted avg where weight = 0.5^(age / 10) # half-life of 10 outputs confidence = min(1.0, volume * max(0, consistency) * recency) ``` - `n` = total observations for this dimension - `observations` = list of dimensional positions (0.0–1.0) from each output - `age` = number of outputs ago (0 = most recent) **Calibration targets** (assuming high consistency and recent data): - ~5 data points → ~0.4 confidence - ~12 data points → ~0.65 confidence - ~20 data points → ~0.9 confidence Low consistency (user oscillates) or stale data (no recent outputs engaging this dimension) will pull confidence down even with many data points. ### Drift Detection Compare the last 5 outputs against the last 20. If a dimension's recent average diverges from its historical average by >0.15: - **Gradual drift** — if the shift was incremental across outputs → update the profile, log as `drift` - **Sudden pivot** — if the shift appeared in 1-2 outputs → hold. Wait for confirmation before updating. If the new direction persists for 3+ outputs, log as `pivot` This prevents a single experimental output from overwriting an established profile. ## Dimension Discovery When the observer detects a consistent pattern that doesn't map to any existing dimension: 1. **Evidence threshold** — the pattern must appear in 3+ outputs 2. **Describe the axis** — identify the two poles based on observed variation 3. **Propose** — add to the Discovered section of `dimension-registry.md` with status "proposed" 4. **Validate** — if the proposed dimension accumulates 5+ data points and confidence > 0.3, promote to active **Discovery candidates to watch for:** - Composition structure (grid-based vs. freeform) - Edge treatment (sharp crops vs. bleeds/fades) - Information hierarchy approach (progressive disclosure vs. everything visible) - Texture use (clean/digital vs. gritty/tactile) - Scale relationships (uniform vs. extreme size contrast) - Narrative quality (does the design tell a story or present a state?) ## Tengan (天眼) Inspiration Analysis Mode A second activation mode. While the primary mode observes *outputs* and infers from *behavior*, Tengan analyzes *inputs* — inspiration images the user explicitly shares — and proposes dimension updates directly. **Name meaning:** 天眼 (tengan) — "heavenly eye" / "divine sight." A Buddhist concept of perceiving reality beyond surface appearance. Tengan sees the dimensional truth of an image across all 17 aesthetic axes. ### When to Activate When the user shares an image with an explicit intent signal: | Intent Signal | Examples | Action | |--------------|---------|--------| | **Absorb** | "absorb this", "add to my aesthetic", "Tengan, absorb this" | Full analysis → profile update at 0.7x weight | | **Reference** | "I like this", "inspired by", "reference for next project" | Full analysis → profile update at 0.4x weight | | **Analyze** | "what does Tengan see?", "analyze this", "map this to my dimensions" | Full analysis → report only, no profile update | | **Avoid** | "not this", "anti-reference", "the opposite of what I want" | Full analysis → negative profile update at -0.5x weight | ### Signal Strength Multipliers Inspiration signals carry less weight than output-based signals because liking something is not the same as wanting to create it. A user can admire a Vermeer without wanting their dashboards to look like Dutch Golden Age paintings. | Signal Source | Multiplier | Data Point Weight | Rationale | |---|:-:|:-:|---| | Creative output (primary mode) | 1.0x | 1.0 | The user made this and accepted it — strongest signal | | Inspiration "absorb" | 0.7x | 0.7 | Deliberate aesthetic expansion — strong but not output-equivalent | | Inspiration "reference" | 0.4x | 0.4 | Appreciation — may not want to replicate fully | | Inspiration "avoid" | -0.5x | 0.5 | Negative signal — push dimensions away | **Why not 1.0x for inspiration?** 20 Pinterest saves shouldn't outweigh 5 actual creative outputs. The multiplier prevents passive consumption from overwriting active creation. This matches how Pinterest (PinnerSage) and Pento weight implicit vs. explicit signals in their taste models. ### Inspiration Analysis Protocol When Tengan activates, analyze the image across **all 17 dimensions**: #### Phase 1 — Dimensional Map For each dimension, estimate the image's position (0.0–1.0): ``` TENGAN ANALYSIS — [Image description] Date: [date] Signal: [absorb / reference / analyze / avoid] ── SPATIAL ── Density: [0.XX] — [brief justification] Symmetry: [0.XX] — [brief justification] Depth: [0.XX] — [brief justification] ── CHROMATIC ── Temperature: [0.XX] — [brief justification] Chromatic Range: [0.XX] — [brief justification] Contrast: [0.XX] — [brief justification] ── FORM ── Geometry: [0.XX] — [brief justification] Precision: [0.XX] — [brief justification] ── TEMPORAL ── Motion Feel: [0.XX] — [brief justification] Temporal Register: [0.XX] — [brief justification] ── EMOTIONAL ── Emotional Register: [0.XX] — [brief justification] Information Stance: [0.XX] — [brief justification] ── PHOTOGRAPHIC ── Light Character: [0.XX] — [brief justification] Substrate/Grain: [0.XX] — [brief justification] Atmosphere/Mood: [0.XX] — [brief justification] ── DISCOVERED ── Light/Dark Pref: [0.XX] — [brief justification] Sublime Scale: [0.XX] — [brief justification] ``` #### Phase 2 — Confidence Modifiers Not every dimension is equally expressed in every image. Rate how clearly the image expresses each dimension: - **Strong expression** (1.0 modifier) — the image clearly demonstrates this dimension's position - **Moderate expression** (0.6 modifier) — the dimension is present but not the image's defining quality - **Weak expression** (0.3 modifier) — the dimension is barely relevant to this image - **Not applicable** (0.0 modifier) — skip this dimension for this image Apply: `effective_weight = signal_multiplier × confidence_modifier` #### Phase 3 — Delta from Current Profile Show the largest divergences between the image and the user's current profile: ``` LARGEST DIVERGENCES (Δ > 0.15): [Dimension]: current [X.XX] → image [Y.YY] (Δ [Z.ZZ]) [Dimension]: current [X.XX] → image [Y.YY] (Δ [Z.ZZ]) ... REINFORCEMENTS (Δ < 0.10, same direction as profile): [Dimension]: current [X.XX] ≈ image [Y.YY] — confirms existing position ... ``` #### Phase 4 — Proposed Updates If the signal is "absorb", "reference", or "avoid" (not "analyze"), propose profile updates: ``` PROPOSED UPDATES (signal: [type], multiplier: [X.Xx]): [Dimension]: [current] → [proposed] (conf: [current] → [proposed]) ... ``` Apply the confidence scoring formula from the Update Protocol section, treating inspiration data points as fractional: an "absorb" = 0.7 data points, a "reference" = 0.4 data points. ### Photography-Specific Checklist When the inspiration image is a photograph or photographic in nature, additionally analyze: 1. **Light** — Direction, quality, source. Natural vs. artificial. Time of day. Reference `photography-vocabulary.md` lighting table for dimensional mapping. 2. **Grain/Texture** — Film stock character, digital noise, processing artifacts. Reference `photography-vocabulary.md` substrate table. 3. **Atmosphere** — Weather, environmental conditions, haze, fog, smoke. Reference `photography-vocabulary.md` atmospheric conditions table. 4. **Color grading** — Natural vs. stylized. If graded: teal/orange, cool desaturation, warm amber, split-toned, cross-processed. Maps to Temperature + Contrast + Chromatic Range. 5. **Composition/Framing** — Focal length (wide/normal/tele), depth of field (deep/selective/shallow), framing pattern (negative space, centered, rule of thirds, fill). Reference `photography-vocabulary.md` composition tables. 6. **Genre** — Street, architectural, cinematic, editorial, other. Cross-reference with user's stated interests in the profile. Images in the user's interest genres carry full weight; images in anti-interest genres should be flagged but weighted at 0.0 unless the user explicitly signals otherwise. ### Evolution Log Entry Format Inspiration analyses use a distinct change type in `evolution-log.md`: ``` ## [Date] — inspiration ([absorb/reference/avoid]) **Trigger:** User shared [image description] with "[exact signal phrase]" **Type:** `inspiration` **Signal multiplier:** [0.7x / 0.4x / -0.5x] **Dimensions shifted:** - [Dimension]: [old] → [new] (confidence: [old] → [new]) - ... **Dimension discovered:** [if applicable] **Evidence:** - [Brief description of the image and what it signals] - Signal strength: [Strong/Moderate] — [rationale] **Notes:** [Any observations about how this fits or expands the existing profile] ``` ## What This Observer Does NOT Do - Ask the user questions (all inference is behavioral) - Override the user's explicit creative direction - Delete or downgrade dimensions (only decay confidence) - Make creative decisions (only updates the profile that other skills read) - Analyze images the user hasn't explicitly asked to analyze — Tengan inspiration analysis mode only activates on explicit user intent signals - Weight anti-interest genre images toward the profile (nature, fashion, portraits) unless the user explicitly overrides ## Scope Boundaries This observer covers **visual aesthetic evolution only**. Prose style tracking belongs to the writing domain's style-dna/style-analyzer system. If both visual and prose dimensions are relevant (e.g., a narrative visualization), each domain's observer tracks its own axes independently.