Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json |
| Date | Not specified in source |
| Repository | Janady13/AI_Brain |
| Revision | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 |
| Branch | codex/build-online-github-ani-irqa7y |
| Path or archive member | daemon/datasets/cognitive_capabilities.json |
| Original SHA-256 | c1a787610bcb2e077189c4225268ea3433fa141737081a81330b7846e15163c2 |
| Git blob SHA | e3f4aa69d1f5b2fc8ea9961d2e9e8ec016793f6b |
| Reading-copy SHA-256 | c1a787610bcb2e077189c4225268ea3433fa141737081a81330b7846e15163c2 |
Every recorded occurrence
These 17 records contain identical original source bytes. Their paths and revisions remain part of the archive.
| Original record | Branch or archive | Revision / identifier | Original location |
|---|---|---|---|
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-0etkns | e250b19d446bdffd542c9eea31cead12e30800d6 | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/cognitive_capabilities.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
Recovered source transcript
Download complete source text ↓500 lines · 14.4 KB
[
{
"name": "Machine Learning Feedback Loop",
"pillar": "machine_learning",
"description": "Continuously trains predictive models on campaign performance to unlock autonomous optimization across spend, creative, and targeting.",
"signal_keywords": [
"machine learning",
"ml",
"model",
"predict",
"optimize",
"train",
"automation"
],
"prompt_strategies": [
"Instrument few-shot prompts that surface historical ROI, CTR, and funnel deltas so models learn from past experiments.",
"Request counterfactual simulations to evaluate how creative or budget shifts impact projected outcomes."
],
"frameworks": [
"Cache-Augmented Generation",
"Knowledge-Augmented Generation",
"Amazon SageMaker Pipelines"
],
"tooling": [
"TensorFlow extended training jobs",
"AWS SageMaker",
"Vertex AI pipelines"
],
"metrics": [
"model_accuracy",
"lift_over_control",
"lifetime_value_uplift"
],
"knowledge_assets": [
"historical_roi_curves",
"channel_response_distributions",
"experiment_metadata"
],
"play": "Deploy reinforcement learning loops that re-weight channel investment daily based on revenue lift."
},
{
"name": "Reasoning and Decision Engine",
"pillar": "reasoning",
"description": "Explains marketing decisions with transparent logic, connecting evidence to next actions and quantifying uncertainty.",
"signal_keywords": [
"reason",
"logic",
"explain",
"justify",
"analysis",
"decision",
"planning"
],
"prompt_strategies": [
"Ask for chain-of-thought rationales that cite benchmarks, enrichment signals, and statistical priors.",
"Request explicit trade-off matrices covering risk, reward, and resource intensity."
],
"frameworks": [
"Knowledge-Augmented Generation",
"Bayesian updating",
"Decision trees"
],
"tooling": [
"Haystack reasoning pipelines",
"Pandas profiling",
"Probabilistic programming notebooks"
],
"metrics": [
"confidence_score",
"expected_value_delta",
"risk_adjusted_roi"
],
"knowledge_assets": [
"statistical_reasoning_store",
"benchmark_catalog",
"risk_playbooks"
],
"play": "Combine statistical priors with enrichment evidence to produce reasoned executive decisions."
},
{
"name": "Problem-Solving Accelerator",
"pillar": "problem_solving",
"description": "Transforms ambiguous growth hurdles into solvable workstreams with hypotheses, experiments, and measurable checkpoints.",
"signal_keywords": [
"problem",
"challenge",
"solve",
"bottleneck",
"roadblock",
"fix",
"root cause"
],
"prompt_strategies": [
"Frame objectives as constraints and ask for experiment matrices that resolve each constraint.",
"Request prioritized issue trees that map blockers to data, automation, or partnership plays."
],
"frameworks": [
"Agent-based experimentation",
"Experiment Growth Grid",
"Automation sprinting"
],
"tooling": [
"EGG planner",
"Automation sprint scheduler",
"Linear issue trackers"
],
"metrics": [
"time_to_resolution",
"velocity_gain",
"conversion_uplift"
],
"knowledge_assets": [
"performance_memory",
"automation_sprint_templates",
"competitive_countermoves"
],
"play": "Activate coordinated EGG experiments with automation sprints to remove campaign bottlenecks."
},
{
"name": "Perception and Signal Fusion",
"pillar": "perception",
"description": "Aggregates signals from marketplaces, ads platforms, influencers, and finance data to perceive market reality in real time.",
"signal_keywords": [
"perceive",
"sense",
"monitor",
"listen",
"insight",
"market data",
"telemetry"
],
"prompt_strategies": [
"Ask for blended insights that correlate ads, affiliate, marketplace, and macro data.",
"Request anomaly detection summaries to highlight emerging threats or accelerators."
],
"frameworks": [
"Cache-Augmented Generation",
"Streaming analytics",
"Haystack multi-index"
],
"tooling": [
"CoinGecko telemetry",
"AWS and Amazon catalog feeds",
"Vector database monitors"
],
"metrics": [
"signal_latency_minutes",
"coverage_ratio",
"anomaly_detection_recall"
],
"knowledge_assets": [
"crypto_market_snapshots",
"marketplace_catalogs",
"google_ads_performance"
],
"play": "Fuse Google Ads, affiliate, marketplace, and stock telemetry into a real-time opportunity radar."
},
{
"name": "Language Intelligence Mesh",
"pillar": "language",
"description": "Uses advanced prompt engineering and linguistic analytics to translate briefs into persuasive multi-channel narratives.",
"signal_keywords": [
"language",
"copy",
"messaging",
"content",
"prompt",
"narrative",
"story"
],
"prompt_strategies": [
"Request tone, voice, and vocabulary matrices aligned to each audience segment.",
"Ask for multilingual or localization-ready prompt packs mapped to funnel stages."
],
"frameworks": [
"Prompt engineering",
"Knowledge-Augmented Generation",
"Prompt management orchestration"
],
"tooling": [
"Amazon Bedrock prompt orchestration",
"LlamaIndex prompt router",
"Copy experimentation dashboards"
],
"metrics": [
"message_resonance_score",
"conversion_copy_lift",
"multilingual_coverage"
],
"knowledge_assets": [
"diagnostic_briefs",
"influencer_scripts",
"executive_playbooks"
],
"play": "Deploy orchestrated prompt packs that adapt messaging across every channel surface."
},
{
"name": "Prompt Engineering Command Center",
"pillar": "prompt_engineering",
"description": "Curates reusable prompt assets, templates, and governance policies that embed domain expertise inside every generation.",
"signal_keywords": [
"prompt",
"instruction",
"template",
"guideline",
"playbook",
"prompt ops"
],
"prompt_strategies": [
"Ask for reusable prompt macros tied to specific processes (RAG, EGG, monetization).",
"Request guardrails and evaluation checklists for high-stakes generations."
],
"frameworks": [
"Prompt engineering",
"Prompt evaluation",
"Prompt ops"
],
"tooling": [
"Prompt registries",
"Amazon Bedrock prompt management",
"Internal prompt review workflows"
],
"metrics": [
"prompt_reuse_rate",
"quality_score",
"review_turnaround_time"
],
"knowledge_assets": [
"prompt_library",
"style_guides",
"prompt_test_results"
],
"play": "Operationalize prompt lifecycle management with governance and rapid iteration loops."
},
{
"name": "Cache-Augmented Generation Fabric",
"pillar": "cache_augmented_generation",
"description": "Pre-computes and caches domain answers, drastically reducing latency for repetitive marketing workflows.",
"signal_keywords": [
"cache",
"precompute",
"latency",
"speed",
"reuse",
"snippet"
],
"prompt_strategies": [
"Request cache warmers for high-volume campaign assets and ROI explanations.",
"Ask for cache invalidation policies triggered by new benchmarks or product launches."
],
"frameworks": [
"Cache-Augmented Generation",
"Vector database caching",
"Edge deployment"
],
"tooling": [
"Redis vector cache",
"Chroma or Pinecone",
"Lambda@Edge refresh jobs"
],
"metrics": [
"response_latency_ms",
"cache_hit_rate",
"update_cycle_time"
],
"knowledge_assets": [
"precomputed_campaign_briefs",
"statistical_snapshots",
"market_faqs"
],
"play": "Warm caches with executive-ready briefs and campaign assets before launches."
},
{
"name": "Knowledge-Augmented Generation Matrix",
"pillar": "knowledge_augmented_generation",
"description": "Connects curated knowledge bases, statistics, and market feeds to guarantee factual, explainable outputs.",
"signal_keywords": [
"knowledge",
"kb",
"reference",
"ground",
"source",
"citation"
],
"prompt_strategies": [
"Ask for cited answers referencing benchmarks, statistics, and enrichment payloads.",
"Request retrieval plans that merge Postgres, vector search, and APIs."
],
"frameworks": [
"Knowledge-Augmented Generation",
"Haystack",
"LlamaIndex"
],
"tooling": [
"Postgres knowledge stores",
"Weaviate or Pinecone",
"Haystack pipelines"
],
"metrics": [
"citation_coverage",
"fact_accuracy",
"retrieval_latency"
],
"knowledge_assets": [
"statistics_postgres",
"vector_memory",
"knowledge_bases"
],
"play": "Link statistical stores, vector memories, and documentation into a unified retrieval mesh."
},
{
"name": "Haystack Retrieval Orchestrator",
"pillar": "haystack",
"description": "Builds production-grade retrieval pipelines that join structured and unstructured marketing knowledge.",
"signal_keywords": [
"haystack",
"retrieval",
"document",
"index",
"pipeline"
],
"prompt_strategies": [
"Request Haystack pipeline blueprints keyed to campaign, finance, and experimentation data.",
"Ask for evaluation routines that monitor retrieval precision and recall."
],
"frameworks": [
"Haystack",
"Knowledge-Augmented Generation",
"Evaluation harnesses"
],
"tooling": [
"Haystack nodes",
"Elasticsearch",
"OpenSearch"
],
"metrics": [
"retrieval_precision",
"retrieval_recall",
"answer_consistency"
],
"knowledge_assets": [
"document_indices",
"faq_collections",
"market_reports"
],
"play": "Deploy Haystack pipelines that stitch together campaign docs, revenue ledgers, and benchmark data."
},
{
"name": "LlamaIndex Knowledge Router",
"pillar": "llamaindex",
"description": "Segments and routes marketing documents into semantic indices optimized for each process.",
"signal_keywords": [
"llamaindex",
"index",
"graph",
"router",
"chunk"
],
"prompt_strategies": [
"Ask for index graphs that split campaign, financial, and creative assets.",
"Request query plan templates that direct prompts to the right index."
],
"frameworks": [
"LlamaIndex",
"Knowledge-Augmented Generation",
"Vector orchestration"
],
"tooling": [
"LlamaIndex graph",
"Weaviate",
"Pinecone"
],
"metrics": [
"routing_accuracy",
"chunk_relevance",
"update_freshness"
],
"knowledge_assets": [
"index_graphs",
"prompt_routing_rules",
"document_embeddings"
],
"play": "Maintain specialized indices for diagnostics, monetization, and executive briefings."
},
{
"name": "Agent Automation Grid",
"pillar": "agent_apps",
"description": "Coordinates agent-based routines that execute repeatable marketing, finance, and enablement workstreams.",
"signal_keywords": [
"agent",
"autonomous",
"workflow",
"delegate",
"orchestrate",
"routine"
],
"prompt_strategies": [
"Ask for agent roles, tool access, and escalation rules aligned to each process output.",
"Request monitoring dashboards that track agent throughput and quality."
],
"frameworks": [
"Agent-based applications",
"Automation sprints",
"Scenario planning"
],
"tooling": [
"Celery task queues",
"Temporal workflows",
"Zapier automation"
],
"metrics": [
"tasks_completed_per_hour",
"quality_pass_rate",
"automation_coverage"
],
"knowledge_assets": [
"agent_playbooks",
"tool_integration_guides",
"monitoring_dashboards"
],
"play": "Deploy agent pods that own campaign launches, reporting, and monetization follow-up."
},
{
"name": "Vector Memory Infrastructure",
"pillar": "vector_databases",
"description": "Stores embeddings for campaigns, experiments, and knowledge so retrieval and similarity reasoning stay instantaneous.",
"signal_keywords": [
"vector",
"embedding",
"similarity",
"memory",
"semantic",
"search"
],
"prompt_strategies": [
"Ask for vector schema designs separating campaigns, experiments, and customers.",
"Request hybrid search strategies that mix dense and sparse signals."
],
"frameworks": [
"Vector databases",
"Cache-Augmented Generation",
"Knowledge-Augmented Generation"
],
"tooling": [
"Pinecone",
"Weaviate",
"Chroma"
],
"metrics": [
"query_latency_ms",
"embedding_coverage",
"relevance_score"
],
"knowledge_assets": [
"campaign_embeddings",
"statistical_vectors",
"customer_segments"
],
"play": "Deploy multi-namespace vector stores to ground every prompt in high-signal embeddings."
},
{
"name": "Prompt Management Control Tower",
"pillar": "prompt_management",
"description": "Uses Bedrock-class orchestration to version, test, and deploy prompts with governance across the ANI.",
"signal_keywords": [
"management",
"governance",
"bedrock",
"version",
"catalog",
"audit"
],
"prompt_strategies": [
"Request release processes for promoting prompts from staging to production.",
"Ask for A/B testing harnesses that evaluate prompt quality over time."
],
"frameworks": [
"Prompt management",
"Amazon Bedrock",
"Evaluation pipelines"
],
"tooling": [
"Amazon Bedrock",
"PromptOps dashboards",
"Continuous evaluation suites"
],
"metrics": [
"deployment_frequency",
"compliance_score",
"prompt_quality_index"
],
"knowledge_assets": [
"prompt_release_notes",
"approval_workflows",
"evaluation_reports"
],
"play": "Govern prompt lifecycles with automated approvals, evaluation gates, and rollback plans."
}
]