{"id":"69bbc464-6c69-4f9f-97ac-763b8b245d0c","shortId":"mpZct4","kind":"skill","title":"state-builder","tagline":"Aggregates all available reader outputs into the five-dimension di_deal_state snapshot using SCD Type 2 versioning. Runs daily after all readers complete. No direct CRM or transcript provider access — everything comes through DI tables. Writes new di_deal_state versions only when","description":"# State Builder — Deal State Projection\n\n> **Adapting this skill:** All CRM-specific values are wrapped in `{{PLACEHOLDER}}` markers.\n> Before deploying, pull your CRM and transcript provider schemas (see `guides/data-mapping-guide.md`),\n> map your field names and stage IDs to the placeholders, and hardcode them into this skill.\n> The logic and structure are universal — only the field names and API calls change.\n\n> **Starter note:** This skill references F08, the cadence-reader skill, and the supabase-reading-guide. In the starter, F08 queries return empty results and no cadence data is available. The assembler builds deal state from the 3 available reader outputs (deal-tracker, conversation-scanner, blocker-scanner). Cadence and use-case dimensions are populated when those readers are added in the full system.\n\nYou are the State Builder for {{COMPANY_NAME}}'s Deal Intelligence pipeline. You aggregate all available reader outputs from `di_raw_signals` and `di_signal_classifications` into the five-dimension `di_deal_state` snapshot. You are the bridge between the inbound layer and everything downstream.\n\n## Boundary\n\n**You answer one question:** Given all spotter outputs since the last assembly, what is the current five-dimension state of each deal?\n\n**You do NOT:**\n\n- Read CRM or transcript provider directly. Everything comes through di_* tables (reader outputs).\n- Interpret signals. You aggregate what the spotters classified. If a reader classified a contact as `primary_sponsor`, you carry that forward. You don't re-assess.\n- Detect patterns. You produce the data that pattern detection agents consume.\n- Score deals. You produce a factual snapshot.\n- Recommend actions. You are pure aggregation.\n- Delete or overwrite previous versions. SCD Type 2 — new version rows only.\n\n---\n\n## Framework Loading\n\nLoad each framework individually (single query per framework to avoid MCP response size limits):\n\n```sql\nSELECT content FROM di_frameworks WHERE framework_id = 'F08' AND status = 'active';\n```\n```sql\nSELECT content FROM di_frameworks WHERE framework_id = 'supabase-reading-guide' AND status = 'active';\n```\n\n| Framework | Use |\n|-----------|-----|\n| F08 — Velocity Benchmarks | **Use for naming only.** Stage names, benchmark field names. Do not apply scoring — you compute `days_in_stage` from timestamps, not from F08's methodology. |\n| SRG — Supabase Data Reading Guide | **Use for naming only.** SCD Type 2 rules, table structures, JSONB field shapes. Follow these as operational rules. |\n\n---\n\n## Supabase Connection\n\nProject: `{{DATABASE_PROJECT_ID}}` ({{DATABASE_NAME}})\nAll queries via `{{DATABASE_EXECUTE_SQL}}`.\n\n---\n\n## Core Principle: Version Only When Changed\n\nA new `di_deal_state` row is created ONLY when at least one dimension or top-level field has changed since the current version. If nothing changed, update `last_confirmed_at` on the current version.\n\n```sql\n-- Update last_confirmed_at when no changes detected\nUPDATE di_deal_state\nSET last_confirmed_at = NOW()\nWHERE deal_id = '[deal_id]' AND is_current = true;\n```\n\n---\n\n## Execution Sequence\n\n### Step 1: Load frameworks\n\n### Step 2: Identify deals to assemble\n\nFind all deals that have new spotter activity since the last assembly:\n\n```sql\nSELECT DISTINCT rs.deal_id\nFROM di_raw_signals rs\nLEFT JOIN di_deal_state ds ON ds.deal_id = rs.deal_id AND ds.is_current = true\nWHERE rs.captured_at > COALESCE(ds.valid_from, '1970-01-01'::timestamptz)\nAND rs.deal_id IS NOT NULL\nORDER BY rs.deal_id;\n```\n\nAlso include deals with `deal_created` signals that have no `di_deal_state` entry:\n\n```sql\nSELECT DISTINCT rs.deal_id\nFROM di_raw_signals rs\nWHERE rs.signal_type = 'deal_created'\nAND rs.deal_id NOT IN (SELECT DISTINCT deal_id FROM di_deal_state)\nAND rs.deal_id IS NOT NULL;\n```\n\n**Stage filter — skip pre-pipeline and terminal stages:** After identifying deals to assemble, exclude any deal whose current `deal_stage` (from the most recent `deal_snapshot` signal) is one of: `{{STAGE_FARMING}}` (Farming), `{{STAGE_PROSPECT}}` (Prospect), `{{STAGE_CLOSED_LOST}}`, `{{STAGE_DISQUALIFIED}}` (Disqualified), `{{STAGE_STALE}}` (Stale). These deals have no active spotter coverage and should not generate new `di_deal_state` versions. Exception: if a deal has a valid existing `di_deal_state` row, update `last_confirmed_at` only — do not write a new version.\n\n### Step 3: For each deal, read current state + new spotter outputs\n\n**Current state:**\n\n```sql\nSELECT * FROM di_deal_state\nWHERE deal_id = '[deal_id]' AND is_current = true;\n```\n\nIf no current state exists (new deal), this will return empty — start from scratch.\n\n**New raw signals since last version:**\n\n```sql\nSELECT rs.id, rs.signal_type, rs.deal_id, rs.contact_id, rs.raw_content, rs.observed_at, rs.captured_by, rs.confidence_tier\nFROM di_raw_signals rs\nWHERE rs.deal_id = '[deal_id]'\nAND rs.captured_at > COALESCE(\n  (SELECT valid_from FROM di_deal_state WHERE deal_id = '[deal_id]' AND is_current = true),\n  '1970-01-01'::timestamptz\n)\nORDER BY rs.observed_at;\n```\n\n**New classifications since last version:**\n\n```sql\nSELECT sc.id, sc.signal_id, sc.framework_id, sc.dimension, sc.classification, sc.confidence, sc.evidence_summary\nFROM di_signal_classifications sc\nJOIN di_raw_signals rs ON rs.id = sc.signal_id\nWHERE rs.deal_id = '[deal_id]'\nAND sc.classified_at > COALESCE(\n  (SELECT valid_from FROM di_deal_state WHERE deal_id = '[deal_id]' AND is_current = true),\n  '1970-01-01'::timestamptz\n);\n```\n\n### Step 4: Build each dimension\n\n#### 4a: cadence_state (from Cadence Reader output)\n\nRead the most recent cadence_snapshot signal:\n\n```sql\nSELECT rs.raw_content\nFROM di_raw_signals rs\nWHERE rs.deal_id = '[deal_id]'\nAND rs.signal_type = 'cadence_snapshot'\nAND rs.captured_by = 'cadence-reader'\nORDER BY rs.captured_at DESC\nLIMIT 1;\n```\n\nExtract into:\n\n```json\n{\n  \"last_engagement\": \"2026-04-15\",\n  \"days_since_engagement\": 5,\n  \"engagement_count_30d\": 6,\n  \"engagement_count_60d\": 11,\n  \"stage_benchmark_days\": null\n}\n```\n\n**Rules:**\n- `stage_benchmark_days` is null until F08 is calibrated. If F08 has a calibrated benchmark for this deal's stage, populate it. Otherwise null.\n\n#### 4b: titles_state (from Stakeholder Reader output — full system; from contact data in starter)\n\nGather all stakeholder classifications for this deal:\n\n```sql\nSELECT sc.classification, sc.confidence, sc.evidence_summary, sc.id AS classification_id, rs.contact_id\nFROM di_signal_classifications sc\nJOIN di_raw_signals rs ON rs.id = sc.signal_id\nWHERE rs.deal_id = '[deal_id]'\nAND sc.dimension = 'stakeholder_role'\nAND sc.framework_id = 'F03';\n```\n\nFor each contact, take the highest-confidence role classification. Build:\n\n```json\n{\n  \"contact_count\": 5,\n  \"multi_thread\": true,\n  \"contacts\": [\n    {\n      \"contact_id\": \"crm_contact_123\",\n      \"role_key\": \"primary_sponsor\",\n      \"confidence\": \"strong\",\n      \"evidence\": [\"evidence_summary_1\", \"evidence_summary_2\"],\n      \"first_appeared_version\": 1,\n      \"engagement_status\": \"active\"\n    }\n  ],\n  \"roles_present\": [\"primary_sponsor\", \"budget_authority\", \"technical_assessor\"]\n}\n```\n\n**Rules:**\n- `role_key` values MUST be lowercase: `primary_sponsor`, `budget_authority`, `technical_assessor`, `process_owner`, `internal_ally`, `opposing_stakeholder`, `cross_functional_advocate`\n- `roles_present` is the deduplicated list of roles from `contacts[]`\n- `multi_thread`: set to `true` if there is a `multi_thread_expansion` classification in `di_signal_classifications` for this deal (from the Stakeholder Reader — full system), OR if there is a `multi_threading_detected` raw signal for this deal. Query to check:\n  ```sql\n  SELECT COUNT(*) FROM di_signal_classifications sc\n  JOIN di_raw_signals rs ON rs.id = sc.signal_id\n  WHERE rs.deal_id = '[deal_id]'\n  AND sc.classification = 'multi_thread_expansion';\n  ```\n  If count > 0, `multi_thread = true`. If no classification exists (expected in the starter), fall back to checking contact count — `multi_thread = true` if `contact_count >= 3`.\n- `first_appeared_version`: if the contact existed in the previous version's titles_state, carry forward that version number. If new, use the new version number.\n- `engagement_status`: Use `last_engagement` from cadence_state assembled in Step 4a above to compute this. `active` (engagement in last 14 days), `cooling` (14-30 days), `cold` (30+ days). Default to `active` if no cadence data.\n- `evidence` array: take the 2-3 most recent, highest-confidence evidence_summary strings from classifications\n\n#### 4c: conversation_state (from Conversation Reader output)\n\nGather deal progression classifications:\n\n```sql\nSELECT sc.classification, sc.confidence, rs.observed_at\nFROM di_signal_classifications sc\nJOIN di_raw_signals rs ON rs.id = sc.signal_id\nWHERE rs.deal_id = '[deal_id]'\nAND sc.dimension = 'deal_progression'\nAND sc.framework_id = 'F02';\n```\n\nGroup by classification type, count, and find most recent:\n\n```json\n{\n  \"signals_present\": {\n    \"pain\": { \"count\": 3, \"most_recent\": \"2026-04-15\" },\n    \"commitment\": { \"count\": 1, \"most_recent\": \"2026-04-10\" }\n  },\n  \"language_posture\": \"evaluating\"\n}\n```\n\n**Rules:**\n- Omit signal types with zero count — do not list them as 0\n- `language_posture` is the most recent language posture classification from the Conversation Reader\n- Signal type names come from F02's taxonomy. Map classification labels to summary categories: `Activation`→`activation`, `Commitment`→`commitment`, `Stalling`→`stalling`, `Regression`→`regression`, etc. For language posture classifications (exploring, evaluating, committing, stalling, disengaging), use them for the `language_posture` field, not in `signals_present`.\n\n#### 4d: velocity_state (from Deal Properties Reader output)\n\nRead the most recent deal_snapshot and stage_change signals:\n\n```sql\nSELECT rs.raw_content, rs.observed_at\nFROM di_raw_signals rs\nWHERE rs.deal_id = '[deal_id]'\nAND rs.signal_type IN ('deal_snapshot', 'stage_change')\nAND rs.captured_by = 'deal-tracker'\nORDER BY rs.captured_at DESC;\n```\n\nCompute `days_in_stage`:\n1. Find the most recent `stage_change` signal for this deal (the `to_stage` tells you the current stage, `observed_at` tells you when it entered)\n2. If no `stage_change` signal exists (deal predates the pipeline), set `days_in_stage = null`. **Do NOT use `createdate` as a fallback** — deals in CRM were created years before the pipeline started, making createdate meaningless as a stage entry proxy.\n3. `days_in_stage = NOW() - stage_entry_timestamp` (only when a stage_change signal is available)\n\nCount regressions:\n\n```sql\nSELECT COUNT(*) AS regressions_total\nFROM di_raw_signals rs\nWHERE rs.deal_id = '[deal_id]'\nAND rs.signal_type = 'stage_change'\nAND rs.raw_content->>'direction' = 'regression';\n```\n\nBuild:\n\n```json\n{\n  \"days_in_stage\": 14,\n  \"stage_benchmark_days\": null,\n  \"close_date\": \"2026-06-30\",\n  \"deal_value\": 250000,\n  \"regressions_total\": 0\n}\n```\n\n**Rules:**\n- `stage_benchmark_days` is null until F08 is calibrated\n- `close_date` and `deal_value` come from the most recent deal_snapshot\n\n#### 4e: boost_friction_state (from Friction Reader output)\n\nGather friction and boost classifications, including the raw signal's contact_id and speaker text for attribution:\n\n```sql\nSELECT sc.id AS classification_id, sc.classification, sc.confidence, rs.observed_at,\n       rs.contact_id, rs.raw_content->>'speaker' AS speaker_name\nFROM di_signal_classifications sc\nJOIN di_raw_signals rs ON rs.id = sc.signal_id\nWHERE rs.deal_id = '[deal_id]'\nAND sc.dimension = 'friction_boost'\nAND sc.framework_id = 'F07';\n```\n\nThen load the stakeholder name→contact_id map for this deal:\n\n```sql\nSELECT DISTINCT rs.contact_id, rs.raw_content->>'name' AS contact_name\nFROM di_raw_signals rs\nWHERE rs.deal_id = '[deal_id]'\nAND rs.signal_type = 'stakeholder_signal'\nAND rs.contact_id IS NOT NULL;\n```\n\nFor each boost or friction entry, resolve `contact_ids` as follows:\n1. If the raw signal has a non-null `contact_id`, use it directly.\n2. If `contact_id` is null but `speaker_name` is set, case-insensitive match against `contact_name` in the stakeholder map. Use any matches found.\n3. If no match, `contact_ids` is an empty array `[]`.\n\nSeparate into friction and boost entries:\n\n```json\n{\n  \"friction\": [\n    {\n      \"type\": \"process_friction_security_review\",\n      \"classification_id\": \"uuid\",\n      \"observed_at\": \"2026-04-08\",\n      \"contact_ids\": []\n    }\n  ],\n  \"boosts\": [\n    {\n      \"type\": \"acceleration_stakeholder_change\",\n      \"classification_id\": \"uuid\",\n      \"observed_at\": \"2026-04-15\",\n      \"contact_ids\": [\"391601\"]\n    }\n  ]\n}\n```\n\n**Rules:**\n- Each entry MUST have `type`, `classification_id`, `observed_at`, and `contact_ids`. This is the canonical v2 format.\n- `contact_ids` is always an array — empty `[]` if attribution failed, never null or omitted.\n- Do NOT use the v1 format (`label` + `evidence` + `confidence` + `consequence`).\n- Friction entries are classifications where the type starts with `process_friction_`, `commercial_objection_`, `technical_concern_`, `political_friction_`, `timing_friction_`, `regulatory_friction_`, `competitive_mention_`.\n- Boost entries are classifications where the type starts with `acceleration_`, `unblocking_event`.\n\n### Step 5: Build top-level fields\n\n| Field | Source |\n|-------|--------|\n| `deal_id` | From the deal being assembled |\n| `deal_stage` | From the most recent `deal_snapshot` signal's `{{CRM_FIELD_DEAL_STAGE}}` field. **Store the raw CRM stage ID** (e.g., `{{STAGE_EVALUATING}}`), not the human label. |\n| `deal_value` | From the most recent `deal_snapshot` signal's `amount` field |\n| `close_date` | From the most recent `deal_snapshot` signal's `{{CRM_FIELD_CLOSE_DATE}}` field |\n| `org_type` | From the F05 `org_context` classification for this deal. **Normalise to F05 canonical values** — these are defined in your populated F05 framework as `[YOUR ORG_TYPE VALUES]`. If no F05 classification exists, carry forward from previous version. If no previous version, null. |\n| `use_case` | From the most recent F10 `use_case` classification. If none exists, null. **Must be lowercase snake_case from F10 taxonomy** — these are defined in your populated F10 framework as `[YOUR USE_CASE VALUES]`. If the classification label contains spaces or mixed case, normalise it to lowercase snake_case. If it maps to none of your defined values, use the closest match at weak confidence. |\n| `deal_synopsis` | **Two sentences, no more.** Sentence 1: \"[Company name] — [use case label] — [stage label] — [deal value].\" Sentence 2: One concrete fact from the most recent signal batch — the single most important thing that changed or is notable (e.g. \"Apr 23 kickoff meeting held with 3 stakeholders.\" or \"Close date pushed out 90 days.\" or \"No meetings in 45 days at SQL stage.\"). Do NOT write narrative paragraphs, interpretations, risk assessments, or lists of observations. The synopsis is a CRM headline, not an analysis. |\n| `version` | Previous version + 1, or 1 for new deals |\n| `is_current` | true |\n| `valid_from` | NOW() |\n| `valid_to` | null |\n| `trigger_type` | `scheduled` for cadence runs, `stage_transition` if a stage_change signal exists in the new batch, `significant_signal` for manual triggers |\n| `classification_ids` | UUID array of ALL classification IDs that informed this version — from all five dimensions |\n\n### Step 6: Diff against current version\n\nCompare the newly built dimensions and top-level fields against the current version. If NOTHING changed (all five JSONB dimensions identical, all top-level fields identical), do NOT write a new version. Just update `last_confirmed_at`.\n\nIf ANY change is detected, proceed to Step 7.\n\n### Step 7: Write new version (SCD Type 2)\n\n**Execute both statements as a single atomic transaction to prevent concurrent runs from leaving two `is_current = true` rows.**\n\n```sql\nBEGIN;\n\nUPDATE di_deal_state\nSET is_current = false,\n    valid_to = NOW()\nWHERE deal_id = '[deal_id]' AND is_current = true;\n\nINSERT INTO di_deal_state (\n  id, deal_id, version, valid_from, valid_to, last_confirmed_at,\n  trigger_type, trigger_signal_id, deal_stage, deal_value, close_date,\n  org_type, use_case, deal_synopsis,\n  titles_state, conversation_state, cadence_state, velocity_state, boost_friction_state,\n  classification_ids, is_current\n)\nVALUES (\n  gen_random_uuid(),\n  '[deal_id]',\n  [version],\n  NOW(),\n  NULL,\n  NOW(),\n  '[trigger_type]',\n  [trigger_signal_id or NULL],\n  '[raw CRM stage ID]',\n  [deal_value],\n  '[close_date]',\n  '[org_type]',\n  '[use_case]',\n  '[deal_synopsis]',\n  '[titles_state JSONB]'::jsonb,\n  '[conversation_state JSONB]'::jsonb,\n  '[cadence_state JSONB]'::jsonb,\n  '[velocity_state JSONB]'::jsonb,\n  '[boost_friction_state JSONB]'::jsonb,\n  ARRAY['uuid1', 'uuid2', ...]::uuid[],\n  true\n)\nRETURNING id;\n-- Capture the returned UUID as [new_deal_state_id] for use in the Step 8 traceability log.\n\nCOMMIT;\n```\n\n### Step 8: Write traceability log\n\n```sql\nINSERT INTO di_traceability_log (id, entity_type, entity_id, action, reasoning, frameworks_consulted, input_data, output_data, logged_at, logged_by)\nVALUES (\n  gen_random_uuid(),\n  'deal_state',\n  '[new_deal_state_id — the UUID returned by RETURNING id in Step 7]',\n  'state_versioned',\n  '[Summary: deal [synopsis], version N. Changed dimensions: [list]. Trigger: [trigger_type]. New signals since last version: M]',\n  ARRAY['F08', 'supabase-reading-guide'],\n  '{\"deal_id\": \"[deal_id]\", \"previous_version\": N-1, \"new_signals_count\": M, \"new_classifications_count\": C}'::jsonb,\n  '{\"new_version\": N, \"changed_dimensions\": [\"titles_state\", \"cadence_state\"], \"trigger\": \"[trigger_type]\"}'::jsonb,\n  NOW(),\n  'state-builder'\n);\n```\n\n---\n\n## Pipeline Stage ID Resolution\n\n> **You must fill this table with your CRM's stage IDs.** See `guides/data-mapping-guide.md` for how\n> to pull your CRM schema and map stages. The stage IDs below are placeholders.\n\n| Order | Stage Label | Stage ID Placeholder |\n|-------|-------------|---------------------|\n| 0 | Farming / Nurture | `{{STAGE_FARMING}}` |\n| 1 | Prospect | `{{STAGE_PROSPECT}}` |\n| 2 | MQL | `{{STAGE_MQL}}` |\n| 3 | SAL (Sales Accepted Lead) | `{{STAGE_SAL}}` |\n| 4 | SQL (Sales Qualified Lead) | `{{STAGE_SQL}}` |\n| 5 | Evaluating | `{{STAGE_EVALUATING}}` |\n| 6 | Technical + DD | `{{STAGE_TECHNICAL_DD}}` |\n| 7 | Contract Negotiation | `{{STAGE_CONTRACT_NEGOTIATION}}` |\n| 8 | Ready to Invoice | `{{STAGE_READY_TO_INVOICE}}` |\n| 9 | Invoiced | `{{STAGE_INVOICED}}` |\n| 10 | Paid | `{{STAGE_PAID}}` |\n| -1 | Closed Lost | `{{STAGE_CLOSED_LOST}}` |\n| -2 | Disqualified | `{{STAGE_DISQUALIFIED}}` |\n| -3 | Stale | `{{STAGE_STALE}}` |\n\n---\n\n## Noise the Agent Will Encounter\n\nThe Assembler does not read CRM or transcript provider directly — it aggregates spotter outputs. However, noise may still reach the Assembler if spotters fail to filter it. See `crm-data-reading-guide.md` → Multi-Pipeline and Cross-Pipeline Awareness for full context.\n\n| Noise Category | How it reaches the Assembler | What to do |\n|---------------|------------------------------|------------|\n| Partnership pipeline deals | Spotters should have filtered these. If a `deal_snapshot` signal references a non-Sales pipeline deal, it should not be in `di_deal_state`. | Do not assemble deals that are not in the Sales Pipeline. If `deal_stage` is unrecognised or the deal is not in `di_deal_state`, skip. |\n| Partner contacts on prospect deals | The Stakeholder Reader (full system) may have captured partner contacts with F03 role classifications. | Carry forward spotter classifications as-is. The Assembler does not re-assess. If a spotter flagged a contact as partner context in traceability, that context is preserved. |\n| Vendor/supplier records | Should not reach the Assembler — spotters filter these. | If encountered, ignore. |\n| Industry ecosystem contacts | Should not reach the Assembler. | If encountered, ignore. |\n| Internal calls and meetings | Should not reach the Assembler — spotters skip these. | If conversation or cadence signals reference internal-only meetings, they should not be present. No additional filtering needed. |\n\n---\n\n## Graceful Degradation\n\n- No spotter output for a dimension: carry forward from previous version. If no previous version, null for that dimension.\n- No Stakeholder Reader output (expected in starter): `titles_state` is built from contact data using the heuristic (contact_count >= 3 → multi_thread), or carried forward from previous version, or `{\"contact_count\": 0, \"multi_thread\": false, \"contacts\": [], \"roles_present\": []}`.\n- No Cadence Reader output: `cadence_state` is carried forward or all nulls.\n- No Conversation Reader output: `conversation_state` is carried forward or `{\"signals_present\": {}, \"language_posture\": null}`.\n- No Friction Reader output: `boost_friction_state` is carried forward or `{\"friction\": [], \"boosts\": []}`.\n- No Deal Properties Reader output: cannot determine deal_stage, deal_value, close_date. If previous version exists, carry forward. If not, skip this deal and log the gap.\n- Mixed confidence levels across classifications: take the highest-confidence classification per field.\n- Empty pipeline (no deals need assembly): report in traceability and exit.\n\n---\n\n## Output Summary\n\nAfter completing the run, report:\n\n1. **Deals assembled:** count\n2. **New versions written:** count (vs confirmed-only updates)\n3. **New deals detected:** count\n4. **Dimensions changed most frequently:** which of the 5 dimensions triggered the most new versions\n5. **Data gaps:** deals where one or more dimensions had no spotter 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topic:agent-skills · 15 github stars · SKILL.md body (21,575 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-05-18T19:06:27.344Z","embedding":null,"createdAt":"2026-05-14T07:05:44.130Z","updatedAt":"2026-05-18T19:06:27.344Z","lastSeenAt":"2026-05-18T19:06:27.344Z","tsv":"'-01':562,563,804,805,868,869 '-04':928,1351,1359,1838,1853 '-06':1614 '-08':1839 '-1':2565,2690 '-10':1360 '-15':929,1352,1854 '-2':2696 '-3':1278,2700 '-30':1261,1615 '0':1186,1376,1621,2631,2968 '1':509,921,1064,1071,1355,1490,1768,2124,2204,2206,2636,3074 '10':2686 '11':941 '123':1054 '14':1257,1260,1606 '1970':561,803,867 '2':21,320,411,513,1067,1277,1516,1783,2135,2319,2640,3078 '2026':927,1350,1358,1613,1837,1852 '23':2157 '250000':1618 '3':146,709,1210,1347,1557,1809,2162,2644,2956,3088 '30':1264 '30d':936 '391601':1857 '4':872,2651,3093 '45':2175 '4a':876,1248 '4b':971 '4c':1289 '4d':1433 '4e':1644 '5':933,1045,1937,2658,3101,3108 '6':937,2259,2662 '60d':940 '7':2311,2313,2532,2668 '8':2482,2487,2674 '9':2682 '90':2169 'acceler':1844,1933 'accept':2647 'access':35 'across':3046 'action':308,2502 'activ':353,369,525,673,1074,1253,1268,1404,1405 'ad':171 'adapt':54 'addit':2913 'advoc':1104 'agent':298,2706 'aggreg':4,189,265,312,2720 'alli':1099 'also':575 'alway':1880 'amount':1990 'analysi':2200 'answer':224 'api':105 'appear':1069,1212 'appli':386 'apr':2156 'array':1274,1818,1882,2245,2461,2552 'as-i':2836 'assembl':140,234,517,529,636,1245,1951,2710,2729,2755,2789,2840,2867,2881,2893,3061,3076 'assess':288,2187,2845 'assessor':1082,1095 'atom':2326 'attribut':1668,1885 'author':1080,1093 'avail':6,138,147,191,1572 'avoid':336 'awar':2745 'back':1199 'batch':2144,2236 'begin':2340 'benchmark':374,381,943,948,961,1608,1624 'blocker':157 'blocker-scann':156 'boost':1645,1655,1709,1759,1823,1842,1924,2402,2456,3006,3014 'boundari':222 'bridg':214 'budget':1079,1092 'build':141,873,1041,1601,1938 'builder':3,50,180,2591 'built':2267,2947 'c':2573 'cadenc':116,135,159,877,880,887,907,913,1243,1271,2223,2398,2448,2582,2900,2976,2979 'cadence-read':115,912 'calibr':955,960,1631 'call':106,2886 'cannot':3020 'canon':1874,2021 'captur':2468,2825 'carri':280,1225,2041,2832,2924,2960,2982,2994,3010,3032 'case':163,1795,2052,2059,2069,2084,2094,2100,2128,2391,2437 'case-insensit':1794 'categori':1403,2750 'chang':107,442,463,470,486,1449,1474,1496,1520,1569,1595,1846,2151,2230,2280,2305,2540,2578,3095 'check':1156,1201 'classif':201,812,831,988,1000,1007,1040,1127,1131,1163,1192,1288,1299,1309,1335,1385,1399,1416,1656,1673,1690,1832,1847,1864,1904,1927,2014,2039,2060,2088,2242,2248,2405,2571,2831,2835,3047,3053 'classifi':269,273 'close':661,1611,1632,1992,2004,2165,2386,2432,2691,2694,3026 'closest':2112 'coalesc':558,786,850 'cold':1263 'come':37,256,1393,1637 'commerci':1912 'commit':1353,1406,1407,1419,2485 'compani':182,2125 'compar':2264 'competit':1922 'complet':28,3070 'comput':389,1251,1486 'concern':1915 'concret':2137 'concurr':2330 'confid':1038,1059,1283,1899,2116,3044,3052 'confirm':473,482,494,699,2301,2375,3085 'confirmed-on':3084 'connect':424 'consequ':1900 'consult':2505 'consum':299 'contact':275,981,1033,1043,1049,1050,1053,1114,1202,1208,1216,1662,1719,1734,1764,1778,1785,1799,1813,1840,1855,1869,1877,2814,2827,2851,2876,2949,2954,2966,2972 'contain':2090 'content':343,356,766,893,1454,1598,1682,1731 'context':2013,2748,2854,2858 'contract':2669,2672 'convers':154,1290,1293,1388,2396,2444,2898,2988,2991 'conversation-scann':153 'cool':1259 'core':437 'count':935,939,1044,1159,1185,1203,1209,1337,1346,1354,1370,1573,1577,2568,2572,2955,2967,3077,3082,3092 'coverag':675 'creat':450,580,603,1543 'created':1535,1550 'crm':31,59,71,250,1052,1541,1962,1970,2002,2196,2427,2603,2614,2714 'crm-data-reading-guide.md':2737 'crm-specif':58 'cross':1102,2743 'cross-pipelin':2742 'current':238,466,477,504,553,641,714,719,734,738,801,865,1507,2211,2262,2276,2336,2347,2359,2408 'daili':24 'data':136,294,402,982,1272,2507,2509,2950,3109 'databas':426,429,434 'date':1612,1633,1993,2005,2166,2387,2433,3027 'day':390,930,944,949,1258,1262,1265,1487,1528,1558,1603,1609,1625,2170,2176 'dd':2664,2667 'deal':15,44,51,142,151,185,208,245,301,446,490,498,500,515,520,543,577,579,586,602,611,615,634,639,642,648,670,682,688,694,712,725,728,730,742,781,792,795,797,845,856,859,861,902,964,991,1021,1134,1153,1177,1297,1323,1327,1437,1445,1465,1471,1479,1500,1523,1539,1589,1616,1635,1642,1704,1724,1744,1945,1949,1952,1958,1964,1980,1986,1998,2017,2117,2132,2209,2343,2353,2355,2364,2367,2382,2384,2392,2413,2430,2438,2474,2518,2521,2536,2558,2560,2761,2769,2778,2785,2790,2799,2805,2810,2817,3016,3022,3024,3038,3059,3075,3090,3111 'deal-track':150,1478 'dedupl':1109 'default':1266 'defin':2025,2075,2108 'degrad':2917 'delet':313 'deploy':68 'desc':919,1485 'detect':289,297,487,1148,2307,3091 'determin':3021 'di':14,39,43,195,199,207,258,345,358,445,489,536,542,585,595,614,681,693,724,774,791,829,834,855,895,1005,1010,1129,1161,1166,1307,1312,1458,1582,1688,1693,1737,2342,2363,2494,2784,2809 'diff':2260 'dimens':13,164,206,241,456,875,2257,2268,2284,2541,2579,2923,2936,3094,3102,3116 'direct':30,254,1599,1782,2718 'disengag':1421 'disqualifi':664,665,2697,2699 'distinct':532,591,610,1727 'downstream':221 'ds':545 'ds.deal':547 'ds.is':552 'ds.valid':559 'e.g':1973,2155 'ecosystem':2875 'empti':131,746,1817,1883,3056 'encount':2708,2872,2883 'engag':926,932,934,938,1072,1237,1241,1254 'enter':1515 'entiti':2498,2500 'entri':588,1555,1563,1762,1824,1860,1902,1925 'etc':1412 'evalu':1363,1418,1975,2659,2661 'event':1935 'everyth':36,220,255 'evid':1061,1062,1065,1273,1284,1898 'except':685 'exclud':637 'execut':435,506,2320 'exist':692,740,1193,1217,1522,2040,2063,2232,3031 'exit':3066 'expans':1126,1183 'expect':1194,2941 'explor':1417 'extract':922 'f02':1332,1395 'f03':1030,2829 'f05':2011,2020,2029,2038 'f07':1713 'f08':113,128,350,372,397,953,957,1629,2553 'f10':2057,2071,2079 'fact':2138 'factual':305 'fail':1886,2732 'fall':1198 'fallback':1538 'fals':2348,2971 'farm':655,656,2632,2635 'field':80,102,382,416,461,1428,1942,1943,1963,1966,1991,2003,2006,2273,2290,3055 'fill':2598 'filter':624,2734,2765,2869,2914 'find':518,1339,1491 'first':1068,1211 'five':12,205,240,2256,2282 'five-dimens':11,204,239 'flag':2849 'follow':418,1767 'format':1876,1896 'forward':282,1226,2042,2833,2925,2961,2983,2995,3011,3033 'found':1808 'framework':325,329,334,346,348,359,361,370,511,2030,2080,2504 'frequent':3097 'friction':1646,1649,1653,1708,1761,1821,1826,1829,1901,1911,1917,1919,1921,2403,2457,3003,3007,3013 'full':174,978,1139,2747,2821 'function':1103 'gap':3042,3110 'gather':985,1296,1652 'gen':2410,2515 'generat':679 'given':227 'grace':2916 'group':1333 'guid':124,366,404,2557 'guides/data-mapping-guide.md':77,2608 'hardcod':89 'headlin':2197 'held':2160 'heurist':2953 'highest':1037,1282,3051 'highest-confid':1036,1281,3050 'howev':2723 'human':1978 'id':84,349,362,428,499,501,534,548,550,567,574,593,606,612,619,729,731,762,764,780,782,796,798,820,822,841,844,846,860,862,901,903,1001,1003,1017,1020,1022,1029,1051,1173,1176,1178,1319,1322,1324,1331,1464,1466,1588,1590,1663,1674,1680,1700,1703,1705,1712,1720,1729,1743,1745,1753,1765,1779,1786,1814,1833,1841,1848,1856,1865,1870,1878,1946,1972,2243,2249,2354,2356,2366,2368,2381,2406,2414,2423,2429,2467,2476,2497,2501,2523,2529,2559,2561,2594,2606,2621,2629 'ident':2285,2291 'identifi':514,633 'ignor':2873,2884 'import':2148 'inbound':217 'includ':576,1657 'individu':330 'industri':2874 'inform':2251 'input':2506,3120 'insensit':1796 'insert':2361,2492 'intellig':186 'intern':1098,2885,2904 'internal-on':2903 'interpret':262,2185 'invoic':2677,2681,2683,2685 'join':541,833,1009,1165,1311,1692 'json':924,1042,1342,1602,1825 'jsonb':415,2283,2442,2443,2446,2447,2450,2451,2454,2455,2459,2460,2574,2587 'key':1056,1085 'kickoff':2158 'label':1400,1897,1979,2089,2129,2131,2627 'languag':1361,1377,1383,1414,1426,2999 'last':233,472,481,493,528,698,754,814,925,1240,1256,2300,2374,2549 'layer':218 'lead':2648,2655 'least':454 'leav':2333 'left':540 'level':460,1941,2272,2289,3045 'limit':340,920 'list':1110,1373,2189,2542 'load':326,327,510,1715 'log':2484,2490,2496,2510,2512,3040 'logic':95 'lost':662,2692,2695 'lowercas':1089,2067,2098 'm':2551,2569 'make':1549 'manual':2240 'map':78,1398,1721,1804,2103,2617 'marker':66 'match':1797,1807,1812,2113 'may':2725,2823 'mcp':337 'meaningless':1551 'meet':2159,2173,2888,2906 'mention':1923 'methodolog':399 'mix':2093,3043 'mql':2641,2643 'multi':1046,1115,1124,1146,1181,1187,1204,2739,2957,2969 'multi-pipelin':2738 'must':1087,1861,2065,2597 'n':2539,2564,2577 'name':81,103,183,377,380,383,407,430,1392,1686,1718,1732,1735,1791,1800,2126 'narrat':2183 'need':2915,3060 'negoti':2670,2673 'never':1887 'new':42,321,444,523,680,706,716,741,750,811,1231,1234,2208,2235,2296,2315,2473,2520,2546,2566,2570,2575,3079,3089,3106 'newli':2266 'nois':2704,2724,2749 'non':1776,2775 'non-nul':1775 'non-sal':2774 'none':2062,2105 'normalis':2018,2095 'notabl':2154 'note':109 'noth':469,2279 'null':570,622,945,951,970,1531,1610,1627,1756,1777,1788,1888,2050,2064,2218,2417,2425,2933,2986,3001 'number':1229,1236 'nurtur':2633 'object':1913 'observ':1509,1835,1850,1866,2191 'omit':1365,1890 'one':225,455,652,2136,3113 'oper':421 'oppos':1100 'order':571,807,915,1481,2625 'org':2007,2012,2033,2388,2434 'otherwis':969 'output':8,149,193,230,261,718,882,977,1295,1440,1651,2508,2722,2920,2940,2978,2990,3005,3019,3067 'overwrit':315 'owner':1097 'paid':2687,2689 'pain':1345 'paragraph':2184 'partner':2813,2826,2853 'partnership':2759 'pattern':290,296 'per':333,3054 'pipelin':187,628,1526,1547,2592,2740,2744,2760,2777,2797,3057 'placehold':65,87,2624,2630 'polit':1916 'popul':166,967,2028,2078 'postur':1362,1378,1384,1415,1427,3000 'pre':627 'pre-pipelin':626 'predat':1524 'present':1076,1106,1344,1432,2911,2974,2998 'preserv':2860 'prevent':2329 'previous':316,1220,2044,2048,2202,2562,2927,2931,2963,3029 'primari':277,1057,1077,1090 'principl':438 'proceed':2308 'process':1096,1828,1910 'produc':292,303 'progress':1298,1328 'project':53,425,427 'properti':1438,3017 'prospect':658,659,2637,2639,2816 'provid':34,74,253,2717 'proxi':1556 'pull':69,2612 'pure':311 'push':2167 'qualifi':2654 'queri':129,332,432,1154 'question':226 'random':2411,2516 'raw':196,537,596,751,775,835,896,1011,1149,1167,1313,1459,1583,1659,1694,1738,1771,1969,2426 're':287,2844 're-assess':286,2843 'reach':2727,2753,2865,2879,2891 'read':123,249,365,403,713,883,1441,2556,2713 'reader':7,27,117,148,169,192,260,272,881,914,976,1138,1294,1389,1439,1650,2820,2939,2977,2989,3004,3018 'readi':2675,2679 'reason':2503 'recent':647,886,1280,1341,1349,1357,1382,1444,1494,1641,1957,1985,1997,2056,2142 'recommend':307 'record':2862 'refer':112,2772,2902 'regress':1410,1411,1574,1579,1600,1619 'regulatori':1920 'report':3062,3073 'resolut':2595 'resolv':1763 'respons':338 'result':132 'return':130,745,2466,2470,2526,2528 'review':1831 'risk':2186 'role':1026,1039,1055,1075,1084,1105,1112,2830,2973 'row':323,448,696,2338 'rs':539,598,777,837,898,1013,1169,1315,1461,1585,1696,1740 'rs.captured':556,769,784,910,917,1476,1483 'rs.confidence':771 'rs.contact':763,1002,1679,1728,1752 'rs.deal':533,549,566,573,592,605,618,761,779,843,900,1019,1175,1321,1463,1587,1702,1742 'rs.id':758,839,1015,1171,1317,1698 'rs.observed':767,809,1304,1455,1677 'rs.raw':765,892,1453,1597,1681,1730 'rs.signal':600,759,905,1468,1592,1747 'rule':412,422,946,1083,1364,1622,1858 'run':23,2224,2331,3072 'sal':2645,2650 'sale':2646,2653,2776,2796 'sc':832,1008,1164,1310,1691 'sc.classification':824,994,1180,1302,1675 'sc.classified':848 'sc.confidence':825,995,1303,1676 'sc.dimension':823,1024,1326,1707 'sc.evidence':826,996 'sc.framework':821,1028,1330,1711 'sc.id':818,998,1671 'sc.signal':819,840,1016,1172,1318,1699 'scanner':155,158 'scd':19,318,409,2317 'schedul':2221 'schema':75,2615 'score':300,387 'scratch':749 'secur':1830 'see':76,2607,2736 'select':342,355,531,590,609,722,757,787,817,851,891,993,1158,1301,1452,1576,1670,1726 'sentenc':2120,2123,2134 'separ':1819 'sequenc':507 'set':492,1117,1527,1793,2345 'shape':417 'signal':197,200,263,538,581,597,650,752,776,830,836,889,897,1006,1012,1130,1150,1162,1168,1308,1314,1343,1366,1390,1431,1450,1460,1497,1521,1570,1584,1660,1689,1695,1739,1750,1772,1960,1988,2000,2143,2231,2238,2380,2422,2547,2567,2771,2901,2997 'signific':2237 'sinc':231,464,526,753,813,931,2548 'singl':331,2146,2325 'size':339 'skill':56,93,111,118 'skill-state-builder' 'skip':625,2812,2895,3036 'snake':2068,2099 'snapshot':17,210,306,649,888,908,1446,1472,1643,1959,1987,1999,2770 'sourc':1944 'source-violetfleming47' 'space':2091 'speaker':1665,1683,1685,1790 'specif':60 'sponsor':278,1058,1078,1091 'spotter':229,268,524,674,717,2721,2731,2762,2834,2848,2868,2894,2919,3119 'sql':341,354,436,479,530,589,721,756,816,890,992,1157,1300,1451,1575,1669,1725,2178,2339,2491,2652,2657 'srg':400 'stage':83,379,392,623,631,643,654,657,660,663,666,942,947,966,1448,1473,1489,1495,1503,1508,1519,1530,1554,1560,1562,1568,1594,1605,1607,1623,1953,1965,1971,1974,2130,2179,2225,2229,2383,2428,2593,2605,2618,2620,2626,2628,2634,2638,2642,2649,2656,2660,2665,2671,2678,2684,2688,2693,2698,2702,2800,3023 'stakehold':975,987,1025,1101,1137,1717,1749,1803,1845,2163,2819,2938 'stale':667,668,2701,2703 'stall':1408,1409,1420 'start':747,1548,1908,1931 'starter':108,127,984,1197,2943 'state':2,16,45,49,52,143,179,209,242,447,491,544,587,616,683,695,715,720,726,739,793,857,878,973,1224,1244,1291,1435,1647,2344,2365,2395,2397,2399,2401,2404,2441,2445,2449,2453,2458,2475,2519,2522,2533,2581,2583,2590,2786,2811,2945,2980,2992,3008 'state-build':1,2589 'statement':2322 'status':352,368,1073,1238 'step':508,512,708,871,1247,1936,2258,2310,2312,2481,2486,2531 'still':2726 'store':1967 'string':1286 'strong':1060 'structur':97,414 'summari':827,997,1063,1066,1285,1402,2535,3068 'supabas':122,364,401,423,2555 'supabase-reading-guid':121,363,2554 'synopsi':2118,2193,2393,2439,2537 'system':175,979,1140,2822 'tabl':40,259,413,2600 'take':1034,1275,3048 'taxonomi':1397,2072 'technic':1081,1094,1914,2663,2666 'tell':1504,1511 'termin':630 'text':1666 'thing':2149 'thread':1047,1116,1125,1147,1182,1188,1205,2958,2970 'tier':772 'time':1918 'timestamp':394,1564 'timestamptz':564,806,870 'titl':972,1223,2394,2440,2580,2944 'top':459,1940,2271,2288 'top-level':458,1939,2270,2287 'topic-agent-skills' 'topic-agentic-ai' 'topic-agentic-workflow' 'topic-ai-agents' 'topic-ai-agents-framework' 'topic-b2b-sales-automation' 'topic-crm' 'topic-intelligence' 'topic-revops' 'topic-revops-automation' 'topic-sales-analysis' 'topic-sales-ops' 'total':1580,1620 'traceabl':2483,2489,2495,2856,3064 'tracker':152,1480 'transact':2327 'transcript':33,73,252,2716 'transit':2226 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