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How Cognee’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score29/100

Agent-ready 43.5 × 0.30 = 13.05

API quality 5.1 × 0.20 = 1.02

Openness 46.2 × 0.20 = 9.24

Built-in AI 28.9 × 0.15 = 4.33

Automation 10.3 × 0.15 = 1.55

(13.05 + 1.02 + 9.24 + 4.33 + 1.55) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 29.19 ÷ 1.00 = 29.2

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready43.5/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [probe] https://docs.cognee.ai/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.cognee.ai/llms.txt # Cognee Documentation > Cognee turns documents, code, and application data into persistent AI memory that agents and a”
  • [claimed-docs] https://docs.cognee.ai/getting-started/introduction.md“Give Cognee text, files, or URLs. It ingests, chunks, extracts entities, and builds the knowledge graph for you in one call.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Cognee MCP brings persistent AI memory to your workflow through the Model Context Protocol.”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“The cognee-cli command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“The `cognee-cli` command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file.”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Quick start with Docker (single command): docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“`cognee-cli demo` gives you a working example with **no API key and no embedding provider**, on a machine with no network access”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee locally using Docker for development, testing, and custom deployments.”
  • [probe] https://docs.cognee.ai/cognee-cli/overview“official CLI documented at https://docs.cognee.ai/cognee-cli/overview”

Plug MCP servers into this product so it can use their toolsweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Connect Cognee to MCP-compatible AI tools like Claude, Cursor, and Cline.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Cognee MCP brings persistent AI memory to your workflow through the Model Context Protocol.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“You access them through MCP-compatible AI assistants like Cursor, Claude Desktop, Continue, Cline, and Codex.”
  • [probe] https://docs.cognee.ai/cognee-mcp/mcp-overview“official MCP server documented at https://docs.cognee.ai/cognee-mcp/mcp-overview”

Connect an agent via an official MCP serverweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Connect Cognee to MCP-compatible AI tools like Claude, Cursor, and Cline.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Cognee MCP brings persistent AI memory to your workflow through the Model Context Protocol.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“You access them through MCP-compatible AI assistants like Cursor, Claude Desktop, Continue, Cline, and Codex.”
  • [probe] https://docs.cognee.ai/cognee-mcp/mcp-overview“official MCP server documented at https://docs.cognee.ai/cognee-mcp/mcp-overview”

Use an official CLIweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“The cognee-cli command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“The `cognee-cli` command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“`cognee-cli push` uploads a local dataset's knowledge graph to Cognee Cloud”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“`cognee-cli report` writes a Graph Insight Report describing what a dataset's graph contains”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“`cognee-cli demo` gives you a working example with **no API key and no embedding provider**, on a machine with no network access”
  • [probe] https://docs.cognee.ai/cognee-cli/overview“official CLI documented at https://docs.cognee.ai/cognee-cli/overview”

Drive the product through a documented public APIweight 3

3 (weight) × 7 (quality) × 1.0 (full) = 21.0 of 30 max

  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/remember.md“The `.remember` operation is the main ingestion entry point in Cognee v1.0. It stores information in memory with a single API call.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/recall.md“when you do not specify a search type, `recall()` classifies the query and picks the best retrieval strategy automatically”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“The `.forget` operation is the unified deletion command in Cognee v1.0.”
  • [claimed-docs] https://docs.cognee.ai/python-api.md“Store data as permanent graph memory or fast session memory in one call.”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee container\ndocker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee locally using Docker for development, testing, and custom deployments.”
  • [probe] https://docs.cognee.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.cognee.ai/openapi.json, https://docs.cognee.ai/swagger.json, https://docs.cognee.ai/api/openapi.json, https://docs.cognee.ai/.well-known/openapi.json)”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/overview.md“This system provides data isolation and access control through dataset-scoped permissions and per-dataset storage, enabling multiple users or organizations to use the same Cognee instance while keeping their data completely separate.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/overview.md“provides data isolation and access control through dataset-scoped permissions and per-dataset storage, enabling multiple users or organizations to use the same Cognee instance”

Build against official SDKsweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/remember.md“The `.remember` operation is the main ingestion entry point in Cognee v1.0. It stores information in memory with a single API call.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/recall.md“when you do not specify a search type, `recall()` classifies the query and picks the best retrieval strategy automatically”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/improve.md“The `.improve` operation enriches an existing Cognee graph after data has already been ingested.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“The `.forget` operation is the unified deletion command in Cognee v1.0.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“The cognee-cli command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file.”
  • [claimed-docs] https://docs.cognee.ai/python-api.md“Store data as permanent graph memory or fast session memory in one call.”
  • [probe] https://docs.cognee.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.cognee.ai/openapi.json, https://docs.cognee.ai/swagger.json, https://docs.cognee.ai/api/openapi.json, https://docs.cognee.ai/.well-known/openapi.json)”
  • [probe] https://docs.cognee.ai/cognee-cli/overview“official CLI documented at https://docs.cognee.ai/cognee-cli/overview”

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Agent-ready = 91.4 ÷ 210 × 100 = 43.5

API quality5.1/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee container\ndocker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee locally using Docker for development, testing, and custom deployments.”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Quick start with Docker (single command): docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [probe] https://docs.cognee.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.cognee.ai/openapi.json, https://docs.cognee.ai/swagger.json, https://docs.cognee.ai/api/openapi.json, https://docs.cognee.ai/.well-known/openapi.json)”

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://docs.cognee.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.cognee.ai/openapi.json, https://docs.cognee.ai/swagger.json, https://docs.cognee.ai/api/openapi.json, https://docs.cognee.ai/.well-known/openapi.json)”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee container\ndocker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee locally using Docker for development, testing, and custom deployments.”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Quick start with Docker (single command): docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”

Test against a sandbox environment without touching production dataweight 1

1 (weight) × 6 (quality) × 0.6 (partial) = 3.6 of 10 max

  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee container\ndocker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee locally using Docker for development, testing, and custom deployments.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“`cognee-cli demo` gives you a working example with **no API key and no embedding provider**, on a machine with no network access”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/multi-user-mode/permissions-system/overview.md“This system provides data isolation and access control through dataset-scoped permissions and per-dataset storage, enabling multiple users or organizations to use the same Cognee instance while keeping their data completely separate.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cloud/overview.md“you can also run the UI and all pipelines entirely on your own machine for free, with no account required, using `cognee.start_ui()`”

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://docs.cognee.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.cognee.ai/openapi.json, https://docs.cognee.ai/swagger.json, https://docs.cognee.ai/api/openapi.json, https://docs.cognee.ai/.well-known/openapi.json)”

API quality = 3.6 ÷ 70 × 100 = 5.1

Openness46.2/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/remember.md“The `.remember` operation is the main ingestion entry point in Cognee v1.0. It stores information in memory with a single API call.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“The `.forget` operation is the unified deletion command in Cognee v1.0.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/improve.md“The .improve operation enriches an existing Cognee graph after data has already been ingested.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cloud/overview.md“Upload data, explore knowledge graphs, run searches, and manage datasets through the browser.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cloud/overview.md“Cognee Cloud gives you the full Cognee platform ... you can also run the UI and all pipelines entirely on your own machine for free, with no account required, using `cognee.start_ui()`.”
  • [probe] https://docs.cognee.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.cognee.ai/openapi.json, https://docs.cognee.ai/swagger.json, https://docs.cognee.ai/api/openapi.json, https://docs.cognee.ai/.well-known/openapi.json)”
  • [probe] https://docs.cognee.ai/cognee-cli/overview“official CLI documented at https://docs.cognee.ai/cognee-cli/overview”

Export all of my data in open formats and leaveweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://www.cognee.ai/pricing“Cognee is open source. Run the full memory engine locally or on your own stack — free, forever.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cloud/overview.md“run the UI and all pipelines entirely on your own machine for free, with no account required, using cognee.start_ui()”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee locally using Docker for development, testing, and custom deployments.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“`cognee-cli report` writes a Graph Insight Report describing what a dataset's graph contains”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“Single data item deletion: remove one data item from a dataset. Dataset deletion: remove an entire dataset and its graph/vector data.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“Single data item deletion: remove one data item from a dataset. Dataset deletion: remove an entire dataset and its graph/vector data. Full cleanup: remove everything the current user can delete.”

Read the product's source under an open licenseweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

  • [claimed-docs] https://www.cognee.ai/pricing“Cognee is open source. Run the full memory engine locally or on your own stack — free, forever.”

Self-host the core productweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee container\ndocker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Run Cognee locally using Docker for development, testing, and custom deployments.”
  • [claimed-docs] https://docs.cognee.ai/api-reference/introduction.md“Quick start with Docker (single command): docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main”
  • [claimed-docs] https://www.cognee.ai/pricing“Cognee is open source. Run the full memory engine locally or on your own stack — free, forever.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cloud/overview.md“you can also run the UI and all pipelines entirely on your own machine for free, with no account required, using `cognee.start_ui()`”
  • [claimed-docs] https://docs.cognee.ai/cognee-cloud/overview.md“run the UI and all pipelines entirely on your own machine for free, with no account required, using cognee.start_ui()”
  • [community] https://hn.algolia.com/api/v1/items/48919162“Cognee's knowledge-graph design (ontology, LLM extraction, dedup) 'looks like such a heavy setup that adds a lot of friction and silos... feels like I just get my data trapped in their service, for not a ton of value' — at personal/small scale it's overkill; better suited for medium-to-large scale deployments.”

Openness = 46.2 ÷ 100 × 100 = 46.2

Built-in AI28.9/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“`cognee-cli report` writes a Graph Insight Report describing what a dataset's graph contains”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/improve.md“The `.improve` operation enriches an existing Cognee graph after data has already been ingested.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/improve.md“The .improve operation enriches an existing Cognee graph after data has already been ingested.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/improve.md“Use `improve()` after Remember when you want to enrich an existing graph further. Use it at the end of a chat or agent session to bridge short-term session memory into permanent memory.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/recall.md“when you do not specify a search type, `recall()` classifies the query and picks the best retrieval strategy automatically”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/recall.md“The `.recall` operation is the main retrieval entry point in Cognee v1.0. It searches memory using the best available source for the request.”

Set up automations that run autonomously in the backgroundweight 2

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

  • [claimed-docs] https://docs.cognee.ai/llms-integrations.md“Install Cognee as a Linear agent that answers @mentions and delegated issues from memory, and index the workspace's issues.”
  • [claimed-docs] https://docs.cognee.ai/llms-integrations.md“Connect a GitHub organization and index every covered repository into the code graph.”
  • [claimed-docs] https://www.cognee.ai“Connect Slack, GitHub, Linear to Cognee and help agents recall what your company knows.”
  • [claimed-docs] https://www.cognee.ai/pricing“Data source integrations: Slack, Notion, Linear, Google Drive”

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [claimed-docs] https://docs.cognee.ai/llms-integrations.md“Install Cognee as a Linear agent that answers @mentions and delegated issues from memory, and index the workspace's issues.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Connect Cognee to MCP-compatible AI tools like Claude, Cursor, and Cline.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Cognee MCP brings persistent AI memory to your workflow through the Model Context Protocol.”

Operate the product with natural-language commandsweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/recall.md“when you do not specify a search type, `recall()` classifies the query and picks the best retrieval strategy automatically”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“The cognee-cli command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file.”
  • [claimed-docs] https://docs.cognee.ai/cognee-cli/overview.md“The `cognee-cli` command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Connect Cognee to MCP-compatible AI tools like Claude, Cursor, and Cline.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“Cognee MCP brings persistent AI memory to your workflow through the Model Context Protocol.”
  • [claimed-docs] https://docs.cognee.ai/cognee-mcp/mcp-overview.md“You access them through MCP-compatible AI assistants like Cursor, Claude Desktop, Continue, Cline, and Codex.”
  • [probe] https://docs.cognee.ai/cognee-mcp/mcp-overview“official MCP server documented at https://docs.cognee.ai/cognee-mcp/mcp-overview”
  • [probe] https://docs.cognee.ai/cognee-cli/overview“official CLI documented at https://docs.cognee.ai/cognee-cli/overview”

Built-in AI = 26.0 ÷ 90 × 100 = 28.9

Automation10.3/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://docs.cognee.ai/getting-started/introduction.md“Give Cognee text, files, or URLs. It ingests, chunks, extracts entities, and builds the knowledge graph for you in one call.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“Single data item deletion: remove one data item from a dataset. Dataset deletion: remove an entire dataset and its graph/vector data.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“Single data item deletion: remove one data item from a dataset. Dataset deletion: remove an entire dataset and its graph/vector data. Full cleanup: remove everything the current user can delete.”
  • [claimed-docs] https://docs.cognee.ai/core-concepts/main-operations/forget.md“Remove a data item, an entire dataset, or all memory for the current user.”
  • [claimed-docs] https://docs.cognee.ai/llms-integrations.md“Connect a GitHub organization and index every covered repository into the code graph.”

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Schedule recurring jobs or workflowsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Version, review, and roll back my automationsweight 1

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Automation = 7.2 ÷ 70 × 100 = 10.3