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th3vib3coder/vibe-science/archive/vibe-science-v6.0-claude-code/SKILL.md

vibe-science

Scientific research engine v6.0 NEXUS — adversarial review (Reviewer 2), 32 quality gates, tree search, serendipity tracking, confounder harness, cross-session learning. Use for ANY scientific analysis, hypothesis testing, data validation, literature review, or task where correctness > speed.

Source repository stars
16
Declared platforms
0
Static risk flags
1
Last source update
2026-08-19
Source checked
2026-08-25

Decision brief

What it does: where it fits

Research engine: agentic tree search over hypotheses, adversarial review by separate sub-agent, 32 quality gates (8 schema-enforced), serendipity detection, hook-based enforcement, cross-session learning, temporal decay calibration. Infinite loops until discovery.

Best for

    Not for

    • Tasks that require unconfirmed production actions or broad system permissions.
    • Environments where the pinned source and install steps cannot be inspected.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/th3vib3coder/vibe-science --skill "archive/vibe-science-v6.0-claude-code"
    Safe inspection promptEditorial

    Inspect the Agent Skill "vibe-science" from https://github.com/th3vib3coder/vibe-science/blob/0b9f29a65578a30ee735e05e6c5bcb26ab693ba8/archive/vibe-science-v6.0-claude-code/SKILL.md at commit 0b9f29a65578a30ee735e05e6c5bcb26ab693ba8. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      PHASE 0: SCIENTIFIC BRAINSTORM (Before Everything)

      Not optional. Not skippable.

      UNDERSTAND — Domain, interests, constraints (ask user, one question at a time)LANDSCAPE — Rapid literature scan (last 3-5 years), field mapping, open debatesGAPS — Blue ocean hunting: cross-domain analogies, assumption reversal, scale shifting, contradiction hunting
    2. 02

      v5.0 FORCED Review Path

      SFI injection → BFP Phase 1 (blind) → Full review Phase 2 → V0 gate → R3/J0 gate → Schema validation → Normal gate evaluation.

      SFI injection → BFP Phase 1 (blind) → Full review Phase 2 → V0 gate → R3/J0 gate → Schema validation → Normal gate evaluation.
    3. 03

      5-STAGE EXPERIMENT MANAGER

      Full protocol: references/experiment-manager.md

      Full protocol: references/experiment-manager.md
    4. 04

      WHY THIS SKILL EXISTS

      AI agents in science optimize for completion, not truth. They find strong signals, construct narratives, never search for confounders, and declare "done" prematurely.

      SERENDIPITY DETECTS — the unexpected observation that starts the investigationPERSISTENCE FOLLOWS — 5, 10, 20+ cycles of testing, not one-and-doneREVIEWER 2 VALIDATES — systematic demolition before publication
    5. 05

      The Three Principles

      1. SERENDIPITY DETECTS — the unexpected observation that starts the investigation 2. PERSISTENCE FOLLOWS — 5, 10, 20+ cycles of testing, not one-and-done 3. REVIEWER 2 VALIDATES — systematic demolition before publication

      SERENDIPITY DETECTS — the unexpected observation that starts the investigationPERSISTENCE FOLLOWS — 5, 10, 20+ cycles of testing, not one-and-doneREVIEWER 2 VALIDATES — systematic demolition before publication

    Permission review

    Static risk signals and limitations

    Writes files

    medium · line 134

    The documentation asks the agent to create, modify, or delete local files.

    Create folder structure, populate STATE.md, PROGRESS.md, TREE-STATE.json, SPINE.md

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score96/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars16SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    th3vib3coder/vibe-science
    Skill path
    archive/vibe-science-v6.0-claude-code/SKILL.md
    Commit
    0b9f29a65578a30ee735e05e6c5bcb26ab693ba8
    License
    Apache-2.0
    Collected
    2026-08-25
    Default branch
    main
    View the original SKILL.md

    Vibe Science v6.0 NEXUS — Observe · Recall · Operate

    Research engine: agentic tree search over hypotheses, adversarial review by separate sub-agent, 32 quality gates (8 schema-enforced), serendipity detection, hook-based enforcement, cross-session learning, temporal decay calibration. Infinite loops until discovery.

    WHY THIS SKILL EXISTS

    AI agents in science optimize for completion, not truth. They find strong signals, construct narratives, never search for confounders, and declare "done" prematurely.

    Over 21 sprints of real research: the agent would have published a confounded claim (OR=2.30, p < 10^-100 — sign reversed by propensity matching), a physically impossible finding (effect direction contradicted by domain knowledge), a noise signal (Cohen's d = 0.07), and non-generalizable rankings. None were hallucinations — the data was real, the statistics correct. The agent never asked: "What if this is an artifact?"

    The solution is not more tools. It is a dispositional change: the system must contain an agent whose ONLY job is to destroy claims.

    Builder (Researcher)Destroyer (Reviewer 2)
    Optimizes forCompletion — shipping resultsSurvival — claims that withstand hostile review
    Default assumption"This result looks promising""This result is probably an artifact"
    Reaction to strong signalExcitement → narrative → paperSuspicion → search for confounders → demand controls
    Searches forSupporting evidencePrior art, contradictions, known artifacts
    Declares "done" whenResults look goodALL counter-verifications pass

    In Claude Code, R2 is a separate sub-agent launched via the Task tool with its own context window. It never sees the researcher's reasoning or excitement — only claims and evidence. This is native Blind-First Pass by architecture.

    The Three Principles

    1. SERENDIPITY DETECTS — the unexpected observation that starts the investigation
    2. PERSISTENCE FOLLOWS — 5, 10, 20+ cycles of testing, not one-and-done
    3. REVIEWER 2 VALIDATES — systematic demolition before publication

    Full exposition: references/constitution.md


    CONSTITUTION (12 Immutable Laws)

    LAW 1: DATA-FIRST — No thesis without evidence from data. NO DATA = NO GO. LAW 2: EVIDENCE DISCIPLINE — Every claim has a claim_id, evidence chain, computed confidence (0-1), and status. LAW 3: GATES BLOCK — 32 quality gates are hard stops. Fix first, re-gate, then continue. LAW 4: REVIEWER 2 IS CO-PILOT — R2 can VETO, REDIRECT, FORCE re-investigation. Non-negotiable. LAW 5: SERENDIPITY IS THE MISSION — Hunt for the unexpected at every cycle. Score >= 10 → QUEUE. >= 15 → INTERRUPT. LAW 6: ARTIFACTS OVER PROSE — If a step can produce a file, it MUST. LAW 7: FRESH CONTEXT RESILIENCE — Resumable from STATE.md + TREE-STATE.json + DB snapshots. All context lives in files and DB, never in chat history. LAW 8: EXPLORE BEFORE EXPLOIT — Min 3 draft nodes before promotion. Exploration ratio >= 20%. LAW 9: CONFOUNDER HARNESS — Every quantitative claim: raw → conditioned → matched. Sign change = ARTIFACT. Collapse >50% = CONFOUNDED. Survives = ROBUST. NO HARNESS = NO CLAIM. LAW 10: CRYSTALLIZE OR LOSE — Every result written to file. Context window is a buffer, not memory. LAW 11: LISTEN TO THE USER — When the user corrects direction, follow immediately. No arguing, no continuing on previous path. Three ignored corrections = session failure. LAW 12: INSTINCT — Learned patterns from past sessions inform current behavior. Instincts are weighted suggestions (confidence 0.3-0.9) that decay with time (-0.02/week) and can be overridden by contradicting evidence. An instinct below 0.2 confidence is archived.

    Full text + role constraints: references/constitution.md


    v6.0 INNOVATIONS (over v5.5)

    InnovationWhatReference
    Hook-Based Enforcement7 hooks (SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop, PreCompact, SubagentStop) enforce laws mechanicallyreferences/hook-system.md
    Cross-Session LearningPattern extraction at session end: gate failure clusters, repeated actions, claim lifecycle patternsreferences/pattern-extraction.md
    Instinct ModelECC-inspired learned behaviors with confidence decay. Observed patterns auto-promote after 3 confirmations.references/instinct-model.md
    Temporal Decay R2 CalibrationR2 weakness tracking with exponential decay (weight = e^(-0.02 * weeks)). Recent reviews weigh more.references/r2-calibration.md
    PreCompact Context ResilienceHook snapshots active research state to DB before context compactionreferences/context-resilience.md
    Agent Handoff ProtocolFormal Context/Findings/Files/Questions/Recommendations documents for agent-to-agent transfersreferences/handoff-protocol.md
    Progressive Context BuildingSessionStart injects ~700 tokens: state, alerts, R2 calibration, patterns, pending seedsreferences/hook-system.md
    DB-Backed Research SpineDual storage: SPINE.md file + spine_entries DB table. Embedding queue for semantic recall.references/research-spine.md
    Claude Code Multi-AgentTask tool delegation with native BFP. Model tiers: opus/sonnet/haiku per role.references/multi-agent-config.md

    Retained from v5.5

    InnovationWhatReference
    Data Quality Gates (DQ1-DQ4)4 gates at pipeline phases: post-extraction, post-training, post-calibration, post-findingreferences/dq-gates.md
    R2 INLINE Mode7-point checklist per finding at formulation time (does not replace FORCED)references/reviewer2-ensemble.md
    Research SpineMandatory structured logbook entry every CRYSTALLIZE. Not optional, not retroactive.references/research-spine.md
    Single Source of Truth (SSOT)All numbers originate from structured data files. No manual transcription.references/ssot.md
    Silent ObserverParallel sub-agent scanning for orphans, desync, drift, naming issuesreferences/silent-observer.md
    Data Dictionary Gate (DD0)Document every dataset column before using it. Column names lie.references/data-dictionary.md
    Design Compliance Gate (DC0)Execution must match research design. Deviations documented.references/design-compliance.md
    Literature Pre-Check (L-1)Prior art search BEFORE committing to any direction.references/literature-precheck.md
    Enforcement ScriptsPython scripts for deterministic gate checks (non-bypassable)scripts/

    MULTI-AGENT ARCHITECTURE

    RoleModelReasoningPurposeWhen to Spawn
    Researcherclaude-opus-4-6highBuild, explore, execute OTAE cyclesMain agent (always active)
    R2-DEEPclaude-opus-4-6highFORCED/BATCH/BRAINSTORM reviews. Separate context = native BFP.Major finding, stage transition, confidence explosion
    R2-INLINEclaude-sonnet-4-6medium7-point checklist per finding. Fast, lightweight.Every finding formulation
    OBSERVERclaude-haiku-4-5lowRead-only scans: orphans, desync, drift, namingEvery 5 cycles or on demand
    EXPLORERclaude-sonnet-4-6mediumParallel tree branches, literature searchWhen branching exploration needed
    R3-JUDGEclaude-opus-4-6highMeta-review of R2's reports (6-dimension rubric)J0 gate
    INSTINCT-SCANNERclaude-haiku-4-5lowScan for recurring patterns across sessionsSession end (stop hook)

    R2-DEEP as sub-agent (via Task tool) means it has NO access to the researcher's reasoning. It sees ONLY claims and evidence. This is architecturally superior to same-agent role-play.

    Full config: references/multi-agent-config.md · Role definitions: AGENTS.md


    SESSION INITIALIZATION

    Banner

    VIBE SCIENCE v6.0 NEXUS — Observe · Recall · Operate
    HOOKS → SFI → BFP → R2 ENSEMBLE → V0/J0 → GATES (32 total, 8 schema-enforced)
    SERENDIPITY RADAR · RESEARCH SPINE · OBSERVER · DQ1-DQ4
    PATTERNS · INSTINCTS · TEMPORAL DECAY · HANDOFF PROTOCOL
    Detect · Persist · Demolish · Discover · Learn
    

    Hook-Based Context Injection

    At session start, the SessionStart hook automatically provides:

    1. [STATE] — Last session summary (actions, claims created/killed)
    2. [ALERTS] — Unresolved observer alerts
    3. [R2 CALIBRATION] — Temporal decay hints about R2's historical weaknesses
    4. [PATTERNS] — Cross-session learned patterns with confidence scores
    5. [PENDING SEEDS] — Serendipity seeds from prior sessions awaiting triage

    This context is injected into the agent's system prompt (~700 tokens). No manual loading required.

    If .vibe-science/ exists → RESUME

    1. Read STATE.md, TREE-STATE.json, last 20 lines of PROGRESS.md
    2. Read CLAIM-LEDGER.md frontmatter, SPINE.md last entry
    3. Check pending: R2 demands, gate failures, debug nodes, Observer alerts
    4. Check injected context: patterns, instincts, R2 calibration hints
    5. Resume from "Next Action" in STATE.md
    6. Announce: "Resuming RQ-XXX, cycle N, stage S. Tree: X nodes (Y good). Next: [Z]."

    If .vibe-science/ does NOT exist → INITIALIZE

    1. → Phase 0: SCIENTIFIC BRAINSTORM (mandatory)
    2. Gate B0 must PASS before any OTAE cycle
    3. Create folder structure, populate STATE.md, PROGRESS.md, TREE-STATE.json, SPINE.md

    Post-Compaction Recovery

    If the context was compacted (auto or manual), the PreCompact hook saved a snapshot to DB:

    • Active claims, pending seeds, spine entry count, STATE.md content
    • Recovery: SessionStart loads last snapshot → agent has enough context to continue

    Full protocol: references/context-resilience.md


    PHASE 0: SCIENTIFIC BRAINSTORM (Before Everything)

    Not optional. Not skippable.

    1. UNDERSTAND — Domain, interests, constraints (ask user, one question at a time)
    2. LANDSCAPE — Rapid literature scan (last 3-5 years), field mapping, open debates
    3. GAPS — Blue ocean hunting: cross-domain analogies, assumption reversal, scale shifting, contradiction hunting
    4. DATA — Reality check: does data exist? Score DATA_AVAILABLE (0-1). LAW 1: NO DATA = NO GO
    5. HYPOTHESES — Generate 3-5 testable, falsifiable hypotheses with null hypotheses and predictions
    6. TRIAGE — Score: impact x feasibility x novelty x data readiness x serendipity potential (/25)
    7. R2 REVIEW — Reviewer 2 challenges direction (BLOCKING: must WEAK_ACCEPT)
    8. COMMIT — Lock RQ.md with: question, hypothesis, predictions, success/kill conditions

    Gate B0: 3+ gaps with evidence, data confirmed (>= 0.5), falsifiable hypothesis, R2 WEAK_ACCEPT, user approved.

    Full protocol: references/brainstorm-engine.md


    OTAE-TREE LOOP

    OBSERVE → THINK → ACT → EVALUATE → CHECKPOINT → CRYSTALLIZE → loop
    

    Each cycle: ONE meaningful action. Each tree node = one OTAE cycle.

    PhaseActionsv5.5 Insertionsv6.0 Hooks
    OBSERVERead STATE.md + TREE-STATE.json. Check pending gates, R2 demands, debug nodes.Check Observer alerts. Check SPINE.md last entry.SessionStart injects context: state, alerts, R2 calibration, patterns, seeds.
    THINKSelect next node or action. Plan: search, analyze, extract, compute, experiment.[DD0] If new data: document all columns before use. [L-1] If new direction: literature pre-check.Check instincts: any learned patterns relevant to current plan?
    ACTExecute planned action. Produce artifacts. Debug if buggy (max 3, then prune).[DQ1] After extraction. [DQ2] After training. [DQ3] After calibration.PostToolUse auto-logs spine entries, runs observer checks.
    EVALUATEExtract claims → CLAIM-LEDGER. Score confidence. Parse metrics. Detect serendipity.[DQ4] Every finding: numbers match source. [R2 INLINE] 7-point checklist per finding.Check instincts for relevant patterns. Update pattern confidence.
    CHECKPOINTStage gate (S1-S5). R2 co-pilot (FORCED/BATCH/SHADOW). Serendipity radar. Stop conditions.[DC0] At stage transitions: design compliance check.R2 calibration hints inform review priorities.
    CRYSTALLIZEUpdate STATE.md, TREE-STATE.json, PROGRESS.md, CLAIM-LEDGER.md.[SPINE] Mandatory structured entry. [SSOT] Run sync_check.py.Stop hook generates narrative, exports STATE.md, extracts patterns.

    v5.0 FORCED Review Path

    SFI injection → BFP Phase 1 (blind) → Full review Phase 2 → V0 gate → R3/J0 gate → Schema validation → Normal gate evaluation.

    Tree Structure

    Tree modes: LINEAR (literature), BRANCHING (experiments), HYBRID (both). Tree search selects next node by confidence + metrics. Each node = one OTAE cycle.

    Full protocol: references/loop-otae.md · Tree search: references/tree-search.md


    HOOKS ENFORCEMENT

    7 hooks enforce the laws mechanically. They run as Node.js scripts triggered by Claude Code events.

    HookEventWhat It DoesLaws Enforced
    SessionStartSession beginsOpens DB, creates session, builds progressive context (~700 tokens), loads R2 calibration + patterns + seedsLAW 7 (resilience), LAW 12 (instinct)
    UserPromptSubmitBefore each promptIdentifies agent role, logs prompt hash, performs semantic recall via vector searchLAW 10 (crystallize), LAW 7 (resilience)
    PostToolUseAfter every toolGate enforcement (DQ4, CLAIM-LEDGER prerequisites, L-1), permission checks, auto-logging spine entries, observer checksLAW 3 (gates), LAW 6 (artifacts), LAW 10 (crystallize)
    StopSession endingNarrative summary, blocks stop if unreviewed claims exist, exports STATE.md, extracts patternsLAW 4 (R2 co-pilot), LAW 7 (resilience), LAW 12 (instinct)
    PreCompactBefore compactionSnapshots active claims, pending seeds, spine count, STATE.md to DBLAW 7 (resilience), LAW 10 (crystallize)
    PreToolUseBefore Write/Edit toolBlocks CLAIM-LEDGER modifications without confounder_status field (regex matcher)LAW 9 (confounder harness)
    SubagentStopSubagent finishesChecks killed claims have serendipity seeds (Salvagente Rule)LAW 4 (R2 co-pilot), LAW 5 (serendipity)

    All hooks degrade gracefully if the DB is unavailable. They never hard-crash.

    Full protocol: references/hook-system.md


    CROSS-SESSION LEARNING

    Pattern Extraction (at session end)

    The Stop hook extracts recurring patterns from cross-session data:

    1. GATE_FAILURE_CLUSTER — Same gate failing across 2+ sessions → pattern (e.g., "DQ1 fails when zero-variance columns present")
    2. REPEATED_ACTION — Same action+input appearing across 2+ sessions → pattern (e.g., "Same bug fix applied 3 times")
    3. CLAIM_LIFECYCLE — Claims killed for same reason across sessions → pattern (e.g., "Confounders kill first quantitative claim every session")

    Patterns are stored in the research_patterns DB table with confidence scores. At session start, active patterns are surfaced in the [PATTERNS] context block.

    Full protocol: references/pattern-extraction.md

    Instinct Model (learned behaviors)

    Inspired by the ECC instinct system. Atomic behavior patterns with confidence:

    • Observation (0.3): Pattern noticed once
    • Pattern (0.5): Observed 3+ times
    • Instinct (0.7): Confirmed by evidence
    • Strong Instinct (0.9): Never contradicted

    Decay: -0.02/week (exponential). Instincts below 0.2 are archived.

    Scope: project (this RQ) or global (all RQs).

    Full protocol: references/instinct-model.md

    R2 Calibration with Temporal Decay

    R2's historical weaknesses are tracked with exponential temporal decay:

    weight = exp(-0.02 * ageWeeks)
    

    A weakness from 50 weeks ago contributes only ~37% of its original weight. This prevents stale calibration data from persisting indefinitely.

    SessionStart injects calibration hints like: "R2 historically weak on 'batch_effect_check' (decay-weighted score: 2.3). High priority."

    Full protocol: references/r2-calibration.md


    AGENT HANDOFF PROTOCOL

    When transferring work between agents (R2 returning verdict, Explorer reporting branch, stage transitions), use formal handoff documents:

    ## HANDOFF: [Source Agent] → [Target Agent]
    ### Context
    What was being done, which RQ, which stage, which cycle.
    ### Findings
    Key results, claims affected, metrics.
    ### Files Modified
    File paths with line ranges.
    ### Open Questions
    Unresolved issues requiring attention.
    ### Recommendations
    Suggested next steps.
    

    This prevents context loss during agent transitions and satisfies LAW 7 (resilience) + LAW 10 (crystallize).

    Full protocol: references/handoff-protocol.md


    5-STAGE EXPERIMENT MANAGER

    StageNameGoalMax IterGate
    1Preliminary InvestigationFirst working experiment or initial scan20S1: >= 1 good node
    2Hyperparameter TuningOptimize best approach12S2: metric improved, 2+ configs
    3Research AgendaExplore creative variants12S3: all sub-experiments attempted
    4Ablation & ValidationValidate each component + multi-seed18S4: all ablated, contributions quantified
    5Synthesis & ReviewFinal R2 ensemble + conclusion5S5: R2 ACCEPT + D2 PASS + all VERIFIED

    Full protocol: references/experiment-manager.md


    REVIEWER 2 CO-PILOT

    4 domain-agnostic reviewers: R2-Methods, R2-Stats, R2-Domain, R2-Engineering.

    7 activation modes:

    ModeTriggerBlocking?Sub-Agent?
    BRAINSTORMPhase 0 completionYES — must WEAK_ACCEPTR2-DEEP
    FORCEDMajor finding, stage transition, pivot, confidence explosion (>0.30/2cyc)YESR2-DEEP (SFI+BFP+V0+J0)
    BATCH3 minor findings accumulatedYESR2-DEEP
    SHADOWEvery 3 cycles automaticallyNO — can ESCALATE to FORCEDR2-DEEP
    VETOR2 spots fatal flawYES — cannot be overridden except by humanR2-DEEP
    REDIRECTR2 identifies better directionSoft — user choosesR2-DEEP
    INLINEEvery finding at formulation timeNO — advisory, but loggedR2-INLINE (sonnet)

    R2 INLINE 7-Point Checklist (v5.5+)

    For every finding, before recording in CLAIM-LEDGER:

    1. Numbers match source data? (SSOT)
    2. Sample size adequate and reported?
    3. Alternative explanations considered?
    4. Prior art checked? (not rediscovering known result)
    5. Confounder risk identified? (even if full harness not yet run)
    6. Reproducible? (seed, parameters, data path documented)
    7. Terminology consistent across documents?

    R2 Behavioral Requirements

    • ASSUME every claim is wrong
    • SEARCH for prior art, contradictions, artifacts
    • DEMAND confounder harness for every quantitative claim (LAW 9)
    • REFUSE premature closure — minimum 3 falsification attempts per major claim
    • ESCALATE, never soften — each pass MORE demanding
    • SALVAGENTE: When killing a claim, R2 MUST produce a serendipity seed
    • CALIBRATE (v6.0): Check temporal decay hints from SessionStart context. Prioritize historically weak areas.

    Full ensemble protocol: references/reviewer2-ensemble.md


    SERENDIPITY RADAR

    Three-part process: DETECTION → PERSISTENCE → VALIDATION.

    Detection (every EVALUATE): 5 scans — anomalies, cross-branch patterns, contradictions, assumption drift, unexpected metrics.

    Response: Score >= 10 → QUEUE. Score >= 15 → INTERRUPT (create serendipity node). Unaddressed flag after 5 cycles → ESCALATED.

    Salvagente (v5.0): When R2 kills a claim (INSUFFICIENT/CONFOUNDED/PREMATURE), R2 MUST produce a serendipity seed (schema-validated).

    Cross-Session Survival (v6.0): Pending seeds are stored in DB and loaded at session start. Seeds that survive across sessions get priority triage.

    Full protocol: references/serendipity-engine.md


    GATES (32 Total)

    CategoryGatesCountSchema-Enforced
    PipelineG0-G67
    LiteratureL-1, L0-L24L0 (source-validity), L2 (review-completeness)
    DecisionD0-D23D1 (claim-promotion), D2 (rq-conclusion)
    TreeT0-T34
    BrainstormB01B0 (brainstorm-quality)
    StageS1-S55S4 (stage4-exit), S5 (stage5-exit)
    Data QualityDQ1-DQ44
    Data DictionaryDD01
    Design ComplianceDC01
    VigilanceV01V0 (vigilance-check)
    JudgeJ01
    Total328 schema-enforced

    Key Gate Summaries

    • G0: Input sanity — data exists, format correct, no corruption
    • G1: Schema compliance — data schema matches expectation
    • DQ1: Post-extraction — no zero-variance, no leakage, cross-checks match
    • DQ2: Post-training — outperforms baseline, no single-feature dominance, stable folds
    • DQ3: Post-calibration — plausible range, not suspiciously perfect, adequate sample
    • DQ4: Post-finding — numbers match source JSON, sample size reported, alternatives listed
    • DD0: Data dictionary — all columns documented before use
    • DC0: Design compliance — execution matches research design
    • L-1: Literature pre-check — prior art searched before committing direction
    • V0: Vigilance — SFI faults caught (RMS >= 0.80, FAR <= 0.10)
    • J0: Judge — R3 meta-review score >= 12/18, no dimension = 0

    Full gate definitions: references/gates-complete.md DQ gate protocol: references/dq-gates.md


    ENFORCEMENT SCRIPTS

    Python scripts for deterministic checks. Exit code 0 = PASS, non-zero = FAIL. Non-bypassable.

    ScriptPurposeCLI Example
    dq_gate.pyDQ1-DQ4 data quality checkspython scripts/dq_gate.py --gate DQ1 --data data.json
    sync_check.pySSOT: numbers in markdown match JSON sourcepython scripts/sync_check.py --json results.json --md FINDINGS.md
    tree_health.pyT3 gate: exploration ratio, good/total ratiopython scripts/tree_health.py --tree TREE-STATE.json
    gate_check.pyGeneric gate: validate artifact against JSON Schemapython scripts/gate_check.py --gate B0 --artifact out.json --schema schemas/brainstorm-quality.schema.json
    spine_entry.pyCreate/validate Research Spine entriespython scripts/spine_entry.py --spine SPINE.md --type DATA_LOAD --action "Loaded dataset"
    observer.pyObserver checks: orphans, desync, drift, namingpython scripts/observer.py --project .vibe-science/

    All scripts: Python 3.8+, stdlib only (no external dependencies). Domain-configurable via --config domain-config.yaml.

    Script Output Format (all scripts return JSON to stdout)

    dq_gate.py — returns {"gate": "DQ1", "status": "PASS"|"FAIL", "checks": [{"check": "zero_variance", "passed": true, "detail": "OK", "flagged": []}]}. Each gate runs 4-5 named checks. Thresholds configurable via --config.

    sync_check.py — returns {"status": "PASS"|"FAIL", "total_numbers_in_markdown": 12, "matched": 12, "mismatched": 0, "tolerance": 0.001, "mismatches": [...]}. Skips dates, claim IDs, gate names. Divides percentages by 100.

    tree_health.py — returns {"gate": "T3", "status": "PASS"|"FAIL", "checks": [...]}. Checks: good_ratio (>=0.20), exploration_ratio (>=0.20), no_stale_branches (5+ non-improving = stale), branch_diversity (>=2 branches, skipped in LINEAR mode).

    gate_check.py — returns {"gate": "B0", "status": "PASS"|"FAIL", "schema_file": "...", "artifact_file": "...", "errors": [...], "error_count": 0}. Lightweight validator (no jsonschema lib).

    spine_entry.py — returns {"status": "PASS"|"FAIL", "type": "DATA_LOAD", "action": "...", "entry": "### ..."}. Creates SPINE.md if missing. Use --validate-only to check without writing.

    observer.py — returns {"status": "OK"|"WARN"|"HALT", "total_alerts": 0, "halt_count": 0, "warn_count": 0, "info_count": 0, "alerts": [...]}. Exit 1 only on HALT or missing project dir.


    FOLDER STRUCTURE

    .vibe-science/
    ├── STATE.md                    # Current state (max 100 lines, rewritten each cycle)
    ├── PROGRESS.md                 # Append-only log
    ├── CLAIM-LEDGER.md             # All claims with evidence + confidence
    ├── SPINE.md                    # Research Spine (structured logbook)
    ├── ASSUMPTION-REGISTER.md      # All assumptions with risk
    ├── SERENDIPITY.md              # Unexpected discovery log
    ├── TREE-STATE.json             # Full tree serialization
    ├── KNOWLEDGE/                  # Cross-RQ accumulated knowledge
    └── RQ-001-[slug]/              # Per Research Question
        ├── RQ.md                   # Question, hypothesis, criteria, kill conditions
        ├── 00-brainstorm/          # Phase 0 outputs
        ├── 01-discovery/           # Literature phase
        ├── 02-analysis/            # Analysis phase
        ├── 03-data/                # Data extraction + validation
        ├── 04-validation/          # Numerical validation
        ├── 05-reviewer2/           # R2 reviews
        ├── 06-runs/                # Run bundles
        ├── 07-audit/               # Decision log + snapshots
        ├── 08-tree/                # Tree search artifacts
        └── 09-writeup/             # Paper drafting
    

    STOP CONDITIONS (checked every cycle)

    1. SUCCESS — All criteria satisfied + all findings R2-approved → Stage 5 → Final R2 → EXIT
    2. NEGATIVE RESULT — Hypothesis disproven or data unavailable → EXIT with documented negative
    3. SERENDIPITY PIVOT — Score >= 15 → triage → create new RQ or queue
    4. DIMINISHING RETURNS — cycles > 15 AND new_finding_rate < 1/3 → WARN → 3 targeted cycles or pivot
    5. DEAD END — All avenues exhausted → EXIT with what was learned
    6. TREE COLLAPSE — T3 fails AND no pending debug → R2 emergency review → pivot or conclude

    RESOURCE ROUTING TABLE

    Load ONLY when needed. Never load all at once.

    ResourcePathWhen to Load
    Constitutionreferences/constitution.mdFull law text needed
    Brainstorm Enginereferences/brainstorm-engine.mdPhase 0
    OTAE Loopreferences/loop-otae.mdFirst cycle or complex routing
    Tree Searchreferences/tree-search.mdTHINK-experiment / tree init
    Experiment Managerreferences/experiment-manager.mdStage transitions
    Auto-Experimentreferences/auto-experiment.mdACT-experiment
    Evidence Enginereferences/evidence-engine.mdEVALUATE phase
    R2 Ensemblereferences/reviewer2-ensemble.mdCHECKPOINT-r2
    Search Protocolreferences/search-protocol.mdACT-search
    Serendipity Enginereferences/serendipity-engine.mdTHINK-brainstorm / CHECKPOINT
    Knowledge Basereferences/knowledge-base.mdSession init / RQ conclusion
    Data Extractionreferences/data-extraction.mdACT-extract
    Writeup Enginereferences/writeup-engine.mdStage 5
    Auditreferences/audit-reproducibility.mdRun manifests
    All Gatesreferences/gates-complete.mdEVALUATE phase
    DQ Gatesreferences/dq-gates.mdDQ1-DQ4 checks
    Data Dictionaryreferences/data-dictionary.mdDD0 — new data
    Design Compliancereferences/design-compliance.mdDC0 — stage transitions
    Literature Pre-Checkreferences/literature-precheck.mdL-1 — new directions
    Research Spinereferences/research-spine.mdCRYSTALLIZE
    SSOT Protocolreferences/ssot.mdCRYSTALLIZE
    Silent Observerreferences/silent-observer.mdObserver checks
    Multi-Agent Configreferences/multi-agent-config.mdSession init
    SFI Protocolreferences/seeded-fault-injection.mdFORCED R2 reviews
    Judge Agentreferences/judge-agent.mdJ0 gate
    BFP Protocolreferences/blind-first-pass.mdFORCED R2 reviews
    Schema Validationreferences/schema-validation.mdGate validation
    Circuit Breakerreferences/circuit-breaker.mdR2 deadlocks
    Hook Systemreferences/hook-system.mdUnderstanding enforcement
    Pattern Extractionreferences/pattern-extraction.mdSession end / pattern review
    R2 Calibrationreferences/r2-calibration.mdR2 review priorities
    Handoff Protocolreferences/handoff-protocol.mdAgent transitions
    Instinct Modelreferences/instinct-model.mdPattern management
    Context Resiliencereferences/context-resilience.mdRecovery after compaction
    Node Schemaassets/node-schema.mdTree mode init
    Stage Promptsassets/stage-prompts.mdStage-specific generation
    Metric Parserassets/metric-parser.mdACT-experiment
    Templatesassets/templates.mdCRYSTALLIZE / session init / handoffs
    Domain Configassets/domain-config-example.yamlDomain-specific thresholds
    Schemasassets/schemas/*.schema.jsonGate validation

    DEVIATION RULES

    SituationAction
    Search query typoAUTO-FIX silently, log
    Missing database in searchADD database, log, continue
    Minor findingACCUMULATE — batch review at 3
    Major findingGATE — stop → verification → R2 FORCED
    Serendipity observationLOG+TRIAGE → serendipity-engine
    Cross-branch patternSERENDIPITY — score → if >= 15: INTERRUPT — create node
    Dead end on current pathPIVOT — document → try alternative → escalate if none
    No data availableSTOP — LAW 1: NO DATA = NO GO
    Confidence explosion (>0.30/2cyc)FORCED R2 — possible confirmation bias
    Node buggy 3 timesPRUNE — mark pruned, select next
    Tree health T3 failsEMERGENCY — R2 review → strategy revision
    Stage gate failsBLOCK — fix, re-gate, advance
    User corrects directionOBEY — LAW 11: follow immediately, no argument
    Architectural change neededASK HUMAN — strategic decisions need human input
    Cross-session pattern detectedINSTINCT — check confidence → if >= 0.5: apply; if < 0.5: investigate
    Context compactedRECOVER — load PreCompact snapshot from DB, resume from STATE.md

    Frequently asked questions

    What to verify before installation and use

    What does the vibe-science source document cover?

    Research engine: agentic tree search over hypotheses, adversarial review by separate sub-agent, 32 quality gates (8 schema-enforced), serendipity detection, hook-based enforcement, cross-session learning, temporal decay calibration. Infinite loops until discovery.

    How do I install vibe-science?

    The source record exposes this install command: npx skills add https://github.com/th3vib3coder/vibe-science --skill "archive/vibe-science-v6.0-claude-code". Inspect the command and pinned source before running it.

    Which permission-related actions were detected?

    Static rules flagged write-files in the source; the page lists the matching lines and excerpts.

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