> ## Documentation Index > Fetch the complete documentation index at: https://docs.getbased.health/llms.txt > Use this file to discover all available pages before exploring further. # Context assembly > How getbased assembles lab, profile, lens, and UI context for AI prompts. # Context Assembly How user-provided data becomes AI prompts. This is the core intelligence layer — every AI feature is only as good as the context it receives. ## The Big Picture ``` ┌─────────────────────────────────────────┐ │ 7 AI FEATURES │ │ │ │ Chat Focus Health PDF Persona │ │ Panel Card Dots Import Generator │ │ Per-Marker AI Correlation AI │ └────────────┬────────────────────────────┘ │ ▼ ┌──────────────────┐ │ callClaudeAPI() │ ← single entry point │ api.js │ └────────┬─────────┘ │ ┌──────────┬──────┴──────┬──────────┬──────────┐ ▼ ▼ ▼ ▼ ▼ OpenRouter Routstr PPQ Venice Local AI (OpenAI) (OpenAI) (OpenAI) (OpenAI) (OpenAI) via shared via shared via shared via shared via shared helper helper helper helper helper ``` Every AI call passes the same shape: `{ system, messages, maxTokens, onStream? }`. The caller assembles the prompt; the router just delivers it. ## Data Sources Everything the AI can know about the user comes from these sources: ``` ┌─────────────────────────────────────────────────────────────────────┐ │ USER DATA SOURCES │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ PROFILE CONTEXT CARDS (9) LAB DATA │ │ ├─ sex ├─ Health Goals ├─ entries[] │ │ ├─ DOB (→ age) ├─ Medical History ├─ dates[] │ │ └─ location/latitude ├─ Diet ├─ marker values│ │ ├─ Exercise ├─ ref ranges │ │ PERSONA ├─ Sleep & Rest ├─ optimal ranges│ │ ├─ personality ID ├─ Light & Circadian ├─ phase ranges │ │ ├─ promptText ├─ Stress ├─ custom markers│ │ └─ evidenceBased flag ├─ Love Life └─ flagged results│ │ └─ Environment │ │ CHAT │ │ ├─ user message OTHER │ │ └─ last 10 history msgs ├─ Interpretive Lens │ │ ├─ Context Notes │ │ MARKER SCHEMA ├─ Menstrual Cycle │ │ ├─ MARKER_SCHEMA ├─ Supplements │ │ └─ customMarkers ├─ User Notes │ │ └─ Genetics (SNPs, if imported) │ │ │ └─────────────────────────────────────────────────────────────────────┘ ``` ## buildLabContext() — The Central Serializer `buildLabContext()` in `lab-context.js` is the single function that converts all user data into a plain-text block. It is used by Chat and Health Dots. The Focus Card uses the lighter `buildFocusContext()` instead. ### Output Structure Sections are ordered by priority — the AI sees "what are you trying to solve?" first, then "what do the numbers say?", then medical/lifestyle context. This exploits primacy bias in LLMs. Each section is wrapped in `[section:name]...[/section:name]` tags for machine-parsable extraction (used by getbased-mcp, Hermes Agent, OpenClaw). Flagged results use `[critical]...[/critical]`. Lab sections include an `updated:date` attribute. ``` Lab data for current profile (sex: female, age: 34, unit system: SI, today: 2026-02-24, dates: Jan 15, Feb 20, Mar 10): [section:healthGoals] ←── 1. GOALS (what to optimize) ## Health Goals (Things to Solve) ### Major Priority - Optimize thyroid function ### Mild Priority - Improve sleep quality [/section:healthGoals] [section:interpretiveLens] ←── 2. LENS (analytical framework) ## Interpretive Lens Quantum biology, functional medicine paradigm [/section:interpretiveLens] [index] ←── category index for selective parsing Available sections: biochemistry, hormones, lipidPanel [/index] Note: status labels below use reference ranges. [section:biochemistry updated:2026-03-10] ←── 3. LAB VALUES (the data) ## Biochemistry - Glucose: Jan 15: 5.2, Feb 20: 5.0, Mar 10: 4.9 mmol/L (ref: 3.9–5.6, status: normal) - Creatinine: Jan 15: 72, Feb 20: 70 µmol/L (ref: 53–97, status: normal) [/section:biochemistry] [section:hormones updated:2026-02-20] ## Hormones - Estradiol: Jan 15: 180 [follicular, ref 77–921], Feb 20: 95 [luteal, ref 65–380] pmol/L ↑ phase-aware: each value shows its cycle phase + phase-specific ref range [/section:hormones] [critical] ←── 4. FLAGS (quick-scan summary) ## Flagged Results (Latest) - Ferritin: 12 µg/L (LOW, range: 15–200) [/critical] [section:userNotes] ←── 5. NOTES (temporal context) ## User Notes - Jan 10: Started new thyroid medication - Feb 15: Feeling much better energy-wise [/section:userNotes] [section:diagnoses] ←── 6. MEDICAL CONTEXT ## Medical History / Diagnoses - Hashimoto's (major, since 2020) ### Family history (heritable/environmental risk signal) - father: Heart Attack (MI), onset age 52 — survived, on statin since - mother: Type 2 Diabetes, onset age 45 - maternal grandmother: Breast Cancer, onset age 61 Notes: On levothyroxine 50mcg [/section:diagnoses] [section:supplements] ←── 7. SUPPLEMENTS ## Supplements & Medications - Vitamin D3 (5000 IU) [supplement]: Jan 1 → ongoing - Magnesium glycinate (400mg) [supplement]: Jan 15 → ongoing [/section:supplements] [section:menstrualCycle] ←── 8. CYCLE (female only) ## Menstrual Cycle Profile: 28-day cycle (5-day period), regular, moderate flow. Recent periods: Jan 3-Jan 7 (moderate) [Cramps, Fatigue], ... Blood draw cycle context: - Jan 15: Day 13 (follicular phase) - Feb 20: Day 18 (luteal phase) Next optimal blood draw window: Mar 3-5 [/section:menstrualCycle] [section:diet] ←── 9-15. LIFESTYLE CARDS ## Diet & Digestion Type: Mediterranean. Pattern: 3 meals. Breakfast (07:00): eggs, avocado Lunch (12:30): salad with chicken Dinner (19:00): salmon, vegetables [/section:diet] ## Exercise & Movement Frequency: 4x/week. Types: weights, yoga. Intensity: moderate. ## Sleep & Rest Duration: 7-8h. Quality: good. Room temp: 65°F. Environment: blackout curtains, grounding sheet. ## Light & Circadian Morning light: 15min. UV exposure: moderate. Latitude: 40-50°N. ## Stress Level: moderate. Sources: work, finances. ## Love Life & Sexual Health Status: partnered. Relationship quality: good. Libido: normal. ## Environment Water: filtered. EMF: wifi router. Home lighting: mixed. ## Context Change Timeline ←── 17. CHANGE HISTORY - Mar 1, 2026: Diet & Digestion — type: omnivore → carnivore - Feb 15, 2026: Stress — level: high → moderate ## Additional Context Notes ←── 18. FREETEXT NOTES Started cold plunges in January ``` ### Empty-Card Guards Cards with no meaningful content are omitted entirely (not sent as empty sections). Each card checks for actual data: * `diagnoses`: conditions array not empty OR note has text * `diet`: type set OR any meal content OR note * `exercise`: frequency set OR types array not empty OR note * `sleepRest`: duration set OR quality set OR issues not empty OR note * `stress`: level set OR sources not empty OR note * `loveLife`: status set OR libido set OR concerns not empty OR note * `environment`: setting set OR water set OR air not empty OR note ### Data Flow Diagram ``` importedData (localStorage) │ ├─── .healthGoals ────────────────────────────────┐ ← FIRST ├─── .interpretiveLens ───────────────────────────┤ │ │ └─── .entries ──┐ │ ▼ │ getActiveData() │ │ │ ├─── categories[].markers[] │ │ ├─ values[] (per date) │ │ ├─ refMin/refMax │ │ ├─ phaseRefRanges[] (female) │ │ └─ phaseLabels[] (female) │ │ │ └─── getAllFlaggedMarkers() ────────────┤ │ ├─── .notes ──────────────────────────────────────┤ ├─── .diagnoses ──────────────────────────────────┤ ├─── .supplements ────────────────────────────────┤ ← MEDICAL ├─── .menstrualCycle ─── + helper functions ──────┤ │ ├─ getCyclePhase() │ │ ├─ getBloodDrawPhases() │ │ ├─ getNextBestDrawDate() │ │ ├─ detectPerimenopausePattern() │ │ └─ detectCycleIronAlerts() │ ├─── .diet ───────────────────────────────────────┤ ├─── .exercise ───────────────────────────────────┤ ├─── .sleepRest ──────────────────────────────────┤ ← LIFESTYLE ├─── .lightCircadian ─── + getLatitudeFromLocation() ├─── .stress ─────────────────────────────────────┤ ├─── .loveLife ───────────────────────────────────┤ ├─── .environment ────────────────────────────────┤ ├─── .changeHistory ──────────────────────────────┤ └─── .contextNotes ───────────────────────────────┤ ← LAST │ ▼ buildLabContext() │ ▼ plain text block ``` ## Prompt Composition Per Feature ### 1. Chat Panel (`sendChatMessage`) The most complex composition — layers 4 components. Lab context is placed before personality so the AI processes data first, then adopts the persona: ``` ┌──────────────────────────────────────────────────────┐ │ SYSTEM PROMPT │ │ │ │ ┌────────────────────────────────────────────────┐ │ │ │ CHAT_SYSTEM_PROMPT (constants.js) │ │ │ │ Priority-tiered structure: │ │ │ │ │ │ │ │ ## Core Rules │ │ │ │ • NOT a doctor disclaimer │ │ │ │ • Reference specific values/dates │ │ │ │ • Format with markdown │ │ │ │ │ │ │ │ ## Priority Context (apply when present) │ │ │ │ • Health goals → prioritize by severity │ │ │ │ • Interpretive lens → frame through experts │ │ │ │ • Medical conditions → interpret accordingly │ │ │ │ • Supplements → correlate start/stop dates │ │ │ │ • Menstrual cycle → phase-aware hormones │ │ │ │ • Notes → medication changes, symptoms │ │ │ │ │ │ │ │ ## Lifestyle Context (apply when present) │ │ │ │ • Diet, exercise, sleep, light, stress, │ │ │ │ relationships, environment │ │ │ │ • Cross-cutting: cortisol/HPA axis note │ │ │ │ │ │ │ │ ## Style │ │ │ │ • Accessible, concise, redirect off-topic │ │ │ └────────────────────────────────────────────────┘ │ │ + │ │ ┌────────────────────────────────────────────────┐ │ │ │ "Current lab data:\n" │ │ │ │ buildLabContext() ← entire serialized context │ │ │ └────────────────────────────────────────────────┘ │ │ + │ │ ┌────────────────────────────────────────────────┐ │ │ │ PERSONALITY LAYER (after data) │ │ │ │ • Default: (nothing added) │ │ │ │ • Dr. House: personality.promptAddition │ │ │ │ • Custom: "Persona: {promptText}" │ │ │ │ + evidence-based disclaimer if opted in │ │ │ └────────────────────────────────────────────────┘ │ │ + │ ├──────────────────────────────────────────────────────┤ │ MESSAGES │ │ │ │ Last 10 chat history entries (role + content) │ │ + current user message │ │ │ ├──────────────────────────────────────────────────────┤ │ maxTokens: 4096 │ streaming: yes │ └──────────────────────────────────────────────────────┘ ``` ### 2. Focus Card (`loadFocusCard`) Uses the lightweight `buildFocusContext()` (\~200-400 tokens) instead of full `buildLabContext()` (\~2000-8000 tokens). Health-goals-aware system prompt: ``` ┌──────────────────────────────────────────────────────┐ │ SYSTEM (if health goals exist): │ │ "You are a blood work analyst. Respond with ONE │ │ sentence, max 40 words. If the patient has health │ │ goals listed, connect your finding to their most │ │ relevant goal. Name the single most actionable │ │ marker finding, its direction, and why it matters. │ │ No preamble, no disclaimer." │ │ │ │ SYSTEM (no health goals): │ │ "...Name the single most important marker finding, │ │ its direction (rising/falling/high/low), and │ │ briefly why it matters clinically..." │ ├──────────────────────────────────────────────────────┤ │ USER: buildFocusContext() │ │ • Profile: sex, age, today │ │ • Major health goals (if any) │ │ • Flagged markers (latest values + ranges) │ │ • Notable changes >20% from previous │ │ ~200-400 tokens total │ ├──────────────────────────────────────────────────────┤ │ maxTokens: 100 │ streaming: no │ ├──────────────────────────────────────────────────────┤ │ CACHE: fingerprint = hash(entries + sex + DOB + │ │ all 9 cards + lens + notes + cycle + supps) │ └──────────────────────────────────────────────────────┘ ``` ### 3. Context Card Health Dots (`loadContextHealthDots`) Requests structured JSON for only the stale (changed) cards. JSON.parse is wrapped in try-catch — on malformed AI responses, stale cards get gray dots while cached good data is preserved: ``` ┌──────────────────────────────────────────────────────┐ │ SYSTEM: "Based on this person's lab data and │ │ profile context, assess each profile area. │ │ Return ONLY valid JSON with these keys..." │ │ │ │ { "diet": {"dot":"...","tip":"..."}, │ │ "stress": {"dot":"...","tip":"..."} } ← only │ │ stale │ │ Dot: green/yellow/red/gray cards │ │ Tip: max 12 words, reference specific markers │ ├──────────────────────────────────────────────────────┤ │ USER: buildLabContext() │ ├──────────────────────────────────────────────────────┤ │ maxTokens: 500 │ streaming: no │ ├──────────────────────────────────────────────────────┤ │ CACHE: per-card fingerprint via getCardFingerprint() │ │ only stale cards re-fetched │ └──────────────────────────────────────────────────────┘ ``` ### 4. PDF Import (`parseLabPDFWithAI`) Completely different context — no user profile, just schema + raw PDF. Filename is included in the user message for multi-file disambiguation: ``` ┌──────────────────────────────────────────────────────┐ │ SYSTEM: "You are a lab report data extraction │ │ assistant..." │ │ │ │ + JSON.stringify(buildMarkerReference()) │ │ { "biochemistry.glucose": { name, unit, ref }, │ │ "hormones.testosterone": { ... }, │ │ ... all known + custom markers } │ │ │ │ + extraction rules (date format, value parsing, │ │ WBC differential, custom marker suggestions) │ │ WBC rule at position 3 (most error-prone) │ │ │ │ + expected JSON output schema │ ├──────────────────────────────────────────────────────┤ │ USER: "Extract all biomarker results from this │ │ lab report (file: report.pdf):\n\n" + pdfText │ ├──────────────────────────────────────────────────────┤ │ maxTokens: 8192 │ streaming: no │ └──────────────────────────────────────────────────────┘ ``` ### 5. Persona Generator (`generateCustomPersonality`) Standalone creative prompt — no lab data involved: ``` ┌──────────────────────────────────────────────────────┐ │ SYSTEM: "You are a persona designer for getbased. │ │ Create a thorough persona covering:" │ │ 1. Identity & Background │ │ 2. Communication Style │ │ 3. Medical & Health Philosophy │ │ 4. Analytical Approach │ │ 5. Lifestyle & Optimization Lens │ │ 6. Character & Personality │ │ 7. Signature Recommendations │ │ "400-500 words. No disclaimers." │ ├──────────────────────────────────────────────────────┤ │ USER: "Create a comprehensive persona for: {name}" │ ├──────────────────────────────────────────────────────┤ │ maxTokens: 2048 │ streaming: yes (into textarea) │ └──────────────────────────────────────────────────────┘ ``` ### 6. Per-Marker AI (`askAIAboutMarker`) Not a separate API call — builds a user message and injects it into the chat panel. Uses effective (phase-aware) reference ranges and includes trend direction when 2+ values exist: ``` askAIAboutMarker("hormones.estradiol") │ ▼ "Tell me about my Estradiol results. Values: Jan 15: 180 pmol/L (follicular phase, ref 77–921), Feb 20: 95 pmol/L (luteal phase, ref 65–380). Reference range: 77–921 pmol/L. Optimal range: 200–400. Current status: normal. Trend: down 47.2% from previous. Note: reference ranges shown are phase-specific. What does this mean and should I be concerned?" │ ▼ openChatPanel(prompt) → sendChatMessage() → full Chat composition ``` ## Caching Strategy Each AI feature has independent caching to avoid redundant API calls: | Feature | Cache Key | Fingerprint Inputs | Invalidation | | ----------- | ----------------------------------- | ------------------------------------------------------------ | -------------------------------- | | Focus Card | `labcharts-{profile}-focusCard` | entries + sex + DOB + 9 cards + lens + notes + cycle + supps | Any data change | | Health Dots | `labcharts-{profile}-contextHealth` | Per-card: lab data + card data + sex + DOB | Only changed cards re-fetched | | Chat | `labcharts-{profile}-chat-t_{id}` | N/A (conversation history) | Never invalidated, 50-thread cap | | PDF Import | N/A | N/A | No caching | | Persona | N/A | N/A | No caching (user saves manually) | ## Token Budget | Feature | maxTokens | Typical Context Size | | ----------------- | --------- | --------------------------------------- | | Chat | 4096 | \~2,000–8,000 tokens (scales with data) | | Focus Card | 100 | \~200–400 tokens (lightweight context) | | Health Dots | 500 | \~2,000–8,000 tokens (full context) | | PDF Import | 8192 | \~1,000–3,000 tokens (marker schema) | | Persona Generator | 2048 | \~400 tokens (fixed) | ## Key Design Decisions 1. **Two serializers**: `buildLabContext()` for full context (Chat, Health Dots) and `buildFocusContext()` for slim context (Focus Card, \~200-400 tokens). The focus card only needs flagged markers and notable changes for a 40-word response. 2. **Priority-ordered sections**: `buildLabContext()` outputs sections in priority order — goals and lens first, then lab values and flags, then medical context, then lifestyle cards. This exploits LLM primacy bias so the most important context gets the most attention. 3. **Data before persona**: In Chat, lab context is placed before the personality layer in the system prompt. This ensures the AI processes the medical data first, then adopts the persona style. 4. **Priority-tiered system prompt**: `CHAT_SYSTEM_PROMPT` uses a 4-tier structure (Core Rules → Priority Context → Lifestyle Context → Style) instead of a flat bullet list. Health goals and interpretive lens are at the top of Priority Context. 5. **Empty-card guards**: Cards with no meaningful content are completely omitted rather than sent as empty sections. Each card has a specific content check (not just truthiness). 6. **Phase-aware values**: For female profiles with active cycle data, estradiol, progesterone, LH, and FSH values include per-date phase labels and phase-specific reference ranges inline. Disabled for hormonal contraception and non-cycling statuses (postmenopause, pregnant, breastfeeding). 7. **Effective ranges**: `askAIAboutMarker()` uses effective (phase-aware) reference ranges, not the static schema ranges. Also includes trend direction with percentage change. 8. **Robust JSON parsing**: `loadContextHealthDots()` wraps the AI response JSON.parse in try-catch. On malformed responses, stale cards get gray dots while cached good data is preserved. ## Prompt Improvement Methodology ### Versioning Context assembly changes follow the scheme `YYYY-MM` (e.g., `2026-02`). Each version is documented in the changelog below. ### Evaluation Criteria When evaluating context assembly changes, assess these 5 dimensions: 1. **Completeness** — Does the AI receive all relevant user data? Are any fields missing from the context? 2. **Primacy positioning** — Are the most important sections (goals, lens, lab values) at the top where LLMs pay most attention? 3. **Signal-to-noise** — Are empty cards omitted? Is the context lean and relevant, or padded with empty sections? 4. **Specificity** — Do values include proper units, reference ranges, phase context, and trend direction? 5. **Token efficiency** — Is the context appropriately sized for each feature's needs? (Focus card: \~200-400 tokens, not \~8000) ### Testing Methodology Changes are verified through 4 layers: 1. **Source inspection** — `test-audit.js` assertions read source code and verify structural properties (function existence, string patterns, section ordering) 2. **DOM verification** — Browser tests check that rendered output includes expected elements 3. **Manual console check** — `window.buildLabContext()` in browser console to inspect actual output 4. **Cross-feature check** — Verify that Chat, Focus Card, and Health Dots all receive appropriate context ### Changelog Format Each entry documents: * Version identifier (YYYY-MM) * Changes made (grouped by category) * Files modified * Rationale for non-obvious decisions ## Context Assembly Changelog ### 2026-02 — Priority Reordering + Enriched Context **Enriched header** * Added age (computed from DOB), current date (ISO), and unit system label to `buildLabContext()` header * Consolidated 3 inline date formatters into single `fmtDate` helper **Section reordering** (primacy bias optimization) * Health Goals → Interpretive Lens → Lab Values → Flagged Results → User Notes → Marker Notes → Per-Value Notes → Medical History → Supplements → Menstrual Cycle → Diet → Exercise → Sleep → Light → Stress → Love Life → Environment → Context Notes * Rationale: AI sees "what to solve" first, then data, then medical context, then lifestyle **Empty-card guards** * All 7 lifestyle/medical cards check for actual content (not just object truthiness) * Prevents sending `## Diet\n` with no content when user has an empty diet card object **System prompt restructure** (`CHAT_SYSTEM_PROMPT`) * Restructured from flat 20-bullet list to 4 priority tiers (Core Rules → Priority Context → Lifestyle Context → Style) * Promoted health goals + interpretive lens to top of Priority Context * Consolidated 4 separate cortisol/HPA mentions into one cross-cutting note * Removed duplicate creatinine/urea from exercise section (already in diet) **Chat prompt order** * Moved personality layer after lab data (was before): `SYSTEM_PROMPT + lab data + personality + search` * Rationale: AI should process data first, then adopt persona style **Focus card optimization** (`buildFocusContext`) * New lightweight serializer: \~200-400 tokens vs \~2000-8000 from `buildLabContext()` * Includes: profile (sex, age, today), major health goals, flagged markers, notable changes >20% * Health-goals-aware system prompt: connects findings to goals when present **Per-marker AI improvements** (`askAIAboutMarker`) * Uses effective (phase-aware) reference ranges instead of static schema ranges * Adds trend direction with percentage change when 2+ values exist **PDF import fixes** (`parseLabPDFWithAI`) * Moved WBC differential rule from position 6 to position 3 (most error-prone extraction rule) * Added filename to user message for multi-file disambiguation **Robustness** * JSON.parse try-catch guard in `loadContextHealthDots()` — malformed AI responses degrade gracefully (gray dots on stale cards, cached good data preserved) **Files modified**: `js/chat.js`, `js/constants.js`, `js/views.js`, `js/context-cards.js`, `js/pdf-import.js`, `service-worker.js` (v50→v51), `test-audit.js` ### 2026-02b — Staleness Signals + Absent Field Awareness + Gate Broadening **Staleness signals** (`buildLabContext`) * **Global**: When most recent lab results are >90 days old, inserts explicit `NOTE:` line with date and approximate months since last test * **Per-category**: After each category's markers, if that category's latest data is >90 days old, appends `⚠ Last tested ~N months ago` line — catches stale categories even when other data is recent (e.g., old fatty acids alongside fresh CBC) * System prompt instructs AI to recommend retesting stale categories and discuss what similar/changed results would suggest **Focus card staleness** (`buildFocusContext`) * Added `last labs ` to compact header so focus card can caveat stale data **System prompt additions** (`CHAT_SYSTEM_PROMPT`) * Core Rules: instruction to note when data age affects analysis relevance * Lifestyle Context: two bullets teaching AI that missing fields = user didn't provide (not assumed default), and missing sections = user hasn't filled that area **Auto-gating with `hasCardContent()`** (v53) * Replaced 7 hand-written card gates with generic `hasCardContent(obj)` from `js/utils.js` * Returns `true` if any field has content: strings non-empty, arrays non-empty, `note` field trimmed * Cards using auto-gate: diagnoses, diet, exercise, sleep, stress, loveLife, environment * Light & Circadian keeps custom `lc || autoLat` gate (external latitude injection) * Eliminates bug class: new fields added to any card are automatically included in AI context without manual gate updates **Previously broadened empty-card gates** (v52, now superseded by auto-gating) * Diet: added `restrictions`, `pattern`, `snacks` to gate (user who only sets restrictions was silently dropped) * Exercise: added `intensity`, `dailyMovement` to gate * Sleep: added `schedule`, `roomTemp`, `environment`, `practices` to gate * Love Life: added `relationship`, `satisfaction`, `frequency`, `orgasm` to gate * Environment: added `climate`, `waterConcerns`, `emf`, `emfMitigation`, `homeLight`, `toxins`, `building` to gate **Files modified**: `js/utils.js`, `js/chat.js`, `js/constants.js`, `js/views.js`, `js/changelog.js`, `service-worker.js` (v52→v53), `test-audit.js`, `test-changelog.js` ### 2026-03 — Change History **Context card change tracking** (`buildLabContext`) * `importedData.changeHistory` populated by `recordChange()` in `context-cards.js` whenever a card save detects a field value change * New `## Context Change Timeline` section (17) appended to `buildLabContext()` output, showing human-readable diffs between consecutive snapshots per field * Enables temporal correlation: AI can connect a diet change on March 1 to a lab shift on March 15 **Files modified**: `js/state.js`, `js/profile.js`, `js/context-cards.js`, `js/cycle.js`, `js/chat.js`, `js/export.js`, `tests/test-change-history.js` ### 2026-03b — Section Tags **Machine-parsable section tags** (`buildLabContext`) * Every section wrapped in `[section:name]...[/section:name]` tags for programmatic extraction (used by getbased-mcp, Hermes Agent, OpenClaw) * Flagged results wrapped in `[critical]...[/critical]` * Lab category sections include `updated:date` attribute with last data date * New `[index]` block listing available lab category keys * No change to content within sections — tags are additive **Files modified**: `js/chat.js`, `tests/test-prelab.js` ## Source Files | File | Key Functions | | --------------------------- | ------------------------------------------------------------------------------- | | `js/utils.js` | `hasCardContent()` — generic empty-card gate for context assembly | | `js/constants.js` | `CHAT_SYSTEM_PROMPT` — priority-tiered system prompt | | `js/lab-context.js` | `buildLabContext()` — central serializer (full context) | | `js/chat-send.js` | `sendChatMessage()` — chat prompt composition | | `js/chat-marker-prompts.js` | `askAIAboutMarker()` — per-marker prompt (effective ranges + trend) | | `js/chat-personalities.js` | `generateCustomPersonality()` — persona generator prompt | | `js/views.js` | `buildFocusContext()` — lightweight focus card context (\~200-400 tokens) | | `js/views.js` | `loadFocusCard()` — focus card prompt (health-goals-aware) | | `js/context-cards.js` | `recordChange(field)` — timestamps context field snapshots into `changeHistory` | | `js/context-cards.js` | `loadContextHealthDots()` — health dots prompt (JSON.parse guarded) | | `js/pdf-import.js` | `parseLabPDFWithAI()` — PDF import prompt (filename included) | | `js/pdf-import.js` | `buildMarkerReference()` — marker schema serializer | | `js/api.js` | `callClaudeAPI()` — provider router | | `js/data.js` | `getActiveData()` — data pipeline (feeds buildLabContext) |