docs: nest Scorers, Self-improvement, and Benchmarks under Measure and improve (GROW-6131)

.agents/references/terminology.md

@@ -362,7 +362,7 @@ Docs match the screen; the fix belongs in the app.
   *Usage note:* Lowercase common noun. Scoped to a factory's definition; distinct from the general [cloud agent runner](/platform/runners/) reference, which covers the same concept for standalone cloud agents outside a factory.
 
 - **Scorer** — A configured LLM judge, scoped per factory to chosen agents and sampled at a set rate, that classifies completed runs against criteria you write, such as "did the agent run the tests before opening a PR?" A Scorer assigns a label (a classification with a score), not a freeform numeric grade. Feeds the **Dashboard** page's Scorer cards, benchmarks, and Self-improvement.
-  *Usage note:* Capitalize "Scorer"/"Scorers" when referring to the feature or a configured instance ("create a Scorer," "Scorer cards"); lowercase only for a generic instance count or file listing ("two scorers," alongside "skills" in an example tree). Say "classify," never "grade" — the docs draw this distinction deliberately. The unit a Scorer evaluates is a **run** (a single agent execution), not a "conversation" or "completed work." `measure-and-improve.mdx` is the canonical page for what a Scorer is and how to configure one; other pages link there rather than repeating the definition.
+  *Usage note:* Capitalize "Scorer"/"Scorers" when referring to the feature or a configured instance ("create a Scorer," "Scorer cards"); lowercase only for a generic instance count or file listing ("two scorers," alongside "skills" in an example tree). Say "classify," never "grade" — the docs draw this distinction deliberately. The unit a Scorer evaluates is a **run** (a single agent execution), not a "conversation" or "completed work." `measure-and-improve/scorers.mdx` is the canonical page for what a Scorer is and how to configure one; other pages link there rather than repeating the definition.
 
 - **AI sovereignty** — Warp Factories' positioning around customer ownership and control of inference, hosting, and data exhaust (agent conversations, evals, memories) for their factory.
 

src/content/docs/factories/factory-as-code.mdx

@@ -404,7 +404,7 @@ Optional. The GitHub or GitLab repositories and exact commits the task starts fr
 
 ## `scorers/<name>/scorer.md`
 
-Optional. Each file defines a scorer: an LLM judge that classifies a sample of an agent's finished runs against a rubric. The directory segment is only a stable filesystem slug — the required `name` field is the scorer's identity. The YAML frontmatter declares the classification contract, and the Markdown body after the closing `---` fence is the rubric. See [Configure Scorers](/factories/measure-and-improve/#configure-scorers) for how scores are used.
+Optional. Each file defines a scorer: an LLM judge that classifies a sample of an agent's finished runs against a rubric. The directory segment is only a stable filesystem slug — the required `name` field is the scorer's identity. The YAML frontmatter declares the classification contract, and the Markdown body after the closing `---` fence is the rubric. See [Configuring Scorers](/factories/measure-and-improve/scorers/) for how scores are used.
 
 ```markdown title="scorers/tests-run/scorer.md"
 ---

src/content/docs/factories/factory-dashboard.mdx

@@ -70,7 +70,7 @@ When an agent proposes a change to a Warp-managed definition, a spec review for
 
 ## Score and benchmark
 
-**Scorers** is where you create Scorers and read their results. **Self-improvement** lists the pull requests the self-improvement flow opens after analyzing runs your Scorers mark as failing, and **Benchmarks** compares model and runner configurations against a fixed set of tasks. See [Configure Scorers](/factories/measure-and-improve/#configure-scorers), [Configure and review Self-improvement](/factories/measure-and-improve/#configure-and-review-self-improvement), and [benchmarking factory agent configurations](/factories/benchmarks/) for what each one is and how to use it.
+**Scorers** is where you create Scorers and read their results. **Self-improvement** lists the pull requests the self-improvement flow opens after analyzing runs your Scorers mark as failing, and **Benchmarks** compares model and runner configurations against a fixed set of tasks. See [Configuring Scorers](/factories/measure-and-improve/scorers/), [Configuring and reviewing Self-improvement](/factories/measure-and-improve/self-improvement/), and [benchmarking factory agent configurations](/factories/benchmarks/) for what each one is and how to use it.
 
 ## Change factory settings
 
@@ -79,7 +79,7 @@ When an agent proposes a change to a Warp-managed definition, a spec review for
 * **Identity** - The factory's name, avatar, and [**Foreman name**](/factories/factory-as-code/#alias), the handle your team @-mentions.
 * **Repositories** - The repos the factory works in.
 * **Pull request authorship** - Whether pull requests are authored by the agent or the run creator (the definition's [`credentialStrategy`](/factories/factory-as-code/#credentialstrategy)).
-* **Analysis model** - The model [Self-improvement](/factories/measure-and-improve/#configure-and-review-self-improvement) uses to analyze failed runs.
+* **Analysis model** - The model [Self-improvement](/factories/measure-and-improve/self-improvement/) uses to analyze failed runs.
 * **Runners** - The compute the factory's runs execute on.
 * **Integrations** - The integrations this factory can access.
 * **Deletion** - Deletes the factory. This cannot be undone.

src/content/docs/factories/factory-skills.mdx

@@ -65,7 +65,7 @@ For worked examples, including a factory-wide skill and a per-agent skill togeth
 
 ## Skills and self-improvement
 
-A factory can propose changes to a skill. When [Self-improvement](/factories/measure-and-improve/#configure-and-review-self-improvement) is on for a Scorer and it flags a recurring failure, it can edit the responsible skill in a follow-up run, the same way it can edit application code. The change still arrives as a pull request for your team to review, whether that's through the factory dashboard or your Git host.
+A factory can propose changes to a skill. When [Self-improvement](/factories/measure-and-improve/self-improvement/) is on for a Scorer and it flags a recurring failure, it can edit the responsible skill in a follow-up run, the same way it can edit application code. The change still arrives as a pull request for your team to review, whether that's through the factory dashboard or your Git host.
 
 ## Related pages
 

src/content/docs/factories/how-factories-work.mdx

@@ -65,7 +65,7 @@ The first two are workflow policy, written into the foreman's instructions; edit
 
 ## How the factory improves itself
 
-Your factory is self-improving, and you define what "better" means. [Scorers](/factories/measure-and-improve/) classify completed runs against criteria you write, and [Self-improvement](/factories/measure-and-improve/#configure-and-review-self-improvement) groups the failures they flag into follow-up runs that propose fixes — to the application code or to the factory's own definition. Every proposal arrives as a change for your review; nothing is adopted on its own.
+Your factory is self-improving, and you define what "better" means. [Scorers](/factories/measure-and-improve/scorers/) classify completed runs against criteria you write, and [Self-improvement](/factories/measure-and-improve/self-improvement/) groups the failures they flag into follow-up runs that propose fixes — to the application code or to the factory's own definition. Every proposal arrives as a change for your review; nothing is adopted on its own.
 
 The factory's definition is open to the same loop. Anyone on the team, or an agent, can propose changes to its instructions, skills, models, or other [definition files](/factories/factory-as-code/), and definitions stored in GitHub go through pull request review and [configuration checks](/factories/factory-as-code/#pull-request-checks-for-github-backed-factories) before a change reaches the production branch.
 

src/content/docs/factories/index.mdx

@@ -52,7 +52,7 @@ Warp Factories is designed for engineering teams with repeatable work that exten
 * **Definitions as code** - [Version-controlled definition files](/factories/factory-as-code/) describe your repositories, agents, automations, runners, [skills](/factories/factory-skills/), and MCP servers, so factory changes get the same review, history, and rollback as code changes.
 * **Integrations and the Factory MCP** - Work flows in from [Slack](/factories/integrations/slack/), [GitHub](/factories/integrations/github/), [GitLab](/factories/integrations/gitlab/), [Linear](/factories/integrations/linear/), and [Jira](/factories/integrations/jira/), plus [custom webhooks](/factories/webhooks/), direct runs, and schedules. The [Factory MCP](/factories/factory-mcp/) connects coding agents and other MCP clients.
 * **Model and harness choice** - Each agent can use a different model and [supported harness](/platform/harnesses/), including the Warp Agent, Claude Code, and Codex.
-* **Measurement and self-improvement** - The [factory dashboard](/factories/factory-dashboard/) shows work-item status, runs, automations, costs, and benchmarks. [Scorers](/factories/measure-and-improve/) classify completed runs, [Benchmarks](/factories/benchmarks/) compare fixed tasks across configurations, and [Self-improvement](/factories/measure-and-improve/#configure-and-review-self-improvement) turns repeated failures into follow-up work the factory proposes for review.
+* **Measurement and self-improvement** - The [factory dashboard](/factories/factory-dashboard/) shows work-item status, runs, automations, costs, and benchmarks. [Scorers](/factories/measure-and-improve/scorers/) classify completed runs, [Benchmarks](/factories/benchmarks/) compare fixed tasks across configurations, and [Self-improvement](/factories/measure-and-improve/self-improvement/) turns repeated failures into follow-up work the factory proposes for review.
 * **Infrastructure control** - Run on Warp-hosted infrastructure, or self-host execution on an eligible Enterprise plan. Teams can also connect supported inference providers, scope secrets, and (if eligible) store transcripts, artifacts, and run attachments in their own S3 or GCS buckets. See [infrastructure and security](/factories/infrastructure-and-security/) for the available controls.
 
 ## How Warp Factories relates to other Warp products

src/content/docs/factories/measure-and-improve.mdx

@@ -1,8 +1,8 @@
 ---
 title: Measure and improve a factory
 description: >-
-  Measure factory activity and costs, evaluate completed runs, compare
-  agent configurations, and turn failures into follow-up work.
+  Measure factory activity, compare benchmarks, and use Scorers and
+  Self-improvement to turn repeated failures into follow-up work.
 sidebar:
   label: "Measure and improve"
 ---
@@ -42,36 +42,15 @@ Use the **Dashboard** page to pick which runs to investigate, not to conclude wh
 
 ## Configure Scorers
 
-A **Scorer** uses an LLM judge to classify completed runs against criteria you write — for example, "did the agent run the tests before opening a PR?" It assigns a label, not a numeric grade, so keep each Scorer focused on one question its failures can point back to. Create Scorers on the factory dashboard's **Scorers** page, which also holds each Scorer's results. For Scorers defined as files in a factory definition, see the [`scorers/<name>/scorer.md` syntax](/factories/factory-as-code/#scorersnamescorermd) and the two scorers in [`02-sdlc-issue-to-pr`](https://github.com/warpdotdev/warp-factory-examples/tree/main/examples/02-sdlc-issue-to-pr) in the [warp-factory-examples](https://github.com/warpdotdev/warp-factory-examples) repository.
-
-Configure these fields:
-
-* **Agent(s) to evaluate** - The agents this Scorer applies to. Select at least one.
-* **Judge instructions** - The criteria the judge checks for.
-* **Judge model** - The model that acts as the judge.
-* **Classifications** - The labels the judge can assign, each with a score.
-* **Pass threshold** - The score a run needs to pass.
-* **Sample rate** - The share of the selected agents' completed runs to evaluate.
-
-{/* VISUAL: The Scorer create/edit form (judge instructions, judge model, classifications, pass threshold, sample rate). */}
-
-While the sample rate is above 0, scoring happens automatically: shortly after a sampled run completes, the judge evaluates it and records a classification, a score, and its reasoning. To stop automatic scoring, set the sample rate to 0.
-
-You can also score any single run on demand, which is useful for testing new judge instructions before raising the sample rate. Scoring a run again replaces its previous result from that Scorer.
-
-Changing **Pass threshold** updates how past scores display as pass or fail; the recorded results don't change.
+A **Scorer** uses an LLM judge to classify completed runs against criteria you write — for example, "did the agent run the tests before opening a PR?" See [Configuring Scorers](/factories/measure-and-improve/scorers/) for its fields and how automatic and on-demand scoring work.
 
 ## Compare configurations with benchmarks
 
 A benchmark compares model and runner configurations for a single agent on the same fixed tasks. Use it to test a configuration change before you apply it to production. See [benchmarking factory agent configurations](/factories/benchmarks/) for the workflow.
 
 ## Configure and review Self-improvement
 
-{/* VISUAL: The Self-improvement pull request list, or a Benchmarks suite run -- this section is text-only today. */}
-
-Turn on **Self-improvement** for each Scorer whose failures you want investigated automatically. Self-improvement groups related failures and files follow-up tasks as ordinary agent runs. A follow-up run can propose changes to application code. It can also improve the factory itself: when you manage your factory as [definitions as code](/factories/factory-as-code/), its prompts, skills, and configuration are version-controlled files, so a follow-up run can open a pull request against the factory definition the same way it would against application code. Nothing is adopted without your review.
-
-The factory dashboard's **Self-improvement** page lists the pull requests these follow-up runs open. Each pull request includes a **Regressions addressed** section that links the failing runs and Scorer results behind it, so you can trace the change back to its evidence.
+**Self-improvement** turns a Scorer's repeated failures into follow-up pull requests, against application code or the factory's own definition. See [Configuring and reviewing Self-improvement](/factories/measure-and-improve/self-improvement/) for how to turn it on and review its pull requests.
 
 ## Run a practical improvement loop
 
@@ -96,6 +75,9 @@ flowchart LR
 5. **Review and adopt.** If the evidence supports the change, make it. Review Self-improvement pull requests with the same standards as human-authored ones.
 6. **Keep monitoring.** Leave the Scorer active and compare new results against your baseline. Revise the Scorer, or set its sample rate to 0, when its criteria no longer match what your team needs.
 
-## Next step
+## Related pages
 
-Record an adopted change in [factory definitions as code](/factories/factory-as-code/) so your team can review the factory configuration.
+* [Configuring Scorers](/factories/measure-and-improve/scorers/) - What a Scorer is, its fields, and how it runs.
+* [Configuring and reviewing Self-improvement](/factories/measure-and-improve/self-improvement/) - Turn on Self-improvement and review its pull requests.
+* [Benchmarking factory agent configurations](/factories/benchmarks/) - Compare model and runner configurations on the same tasks.
+* [Definitions as code](/factories/factory-as-code/) - Record an adopted change so your team can review the factory configuration.

src/content/docs/factories/measure-and-improve/scorers.mdx

@@ -0,0 +1,33 @@
+---
+title: Configuring Scorers
+description: >-
+  Configure a Scorer to have an LLM judge classify completed factory runs
+  against criteria you write.
+sidebar:
+  label: "Scorers"
+---
+
+A **Scorer** uses an LLM judge to classify completed runs against criteria you write — for example, "did the agent run the tests before opening a PR?" It assigns a label, not a numeric grade, so keep each Scorer focused on one question its failures can point back to. Create Scorers on the factory dashboard's **Scorers** page, which also holds each Scorer's results. For Scorers defined as files in a factory definition, see the [`scorers/<name>/scorer.md` syntax](/factories/factory-as-code/#scorersnamescorermd) and the two scorers in [`02-sdlc-issue-to-pr`](https://github.com/warpdotdev/warp-factory-examples/tree/main/examples/02-sdlc-issue-to-pr) in the [warp-factory-examples](https://github.com/warpdotdev/warp-factory-examples) repository.
+
+Configure these fields:
+
+* **Agent(s) to evaluate** - The agents this Scorer applies to. Select at least one.
+* **Judge instructions** - The criteria the judge checks for.
+* **Judge model** - The model that acts as the judge.
+* **Classifications** - The labels the judge can assign, each with a score.
+* **Pass threshold** - The score a run needs to pass.
+* **Sample rate** - The share of the selected agents' completed runs to evaluate.
+
+{/* VISUAL: The Scorer create/edit form (judge instructions, judge model, classifications, pass threshold, sample rate). */}
+
+While the sample rate is above 0, scoring happens automatically: shortly after a sampled run completes, the judge evaluates it and records a classification, a score, and its reasoning. To stop automatic scoring, set the sample rate to 0.
+
+You can also score any single run on demand, which is useful for testing new judge instructions before raising the sample rate. Scoring a run again replaces its previous result from that Scorer.
+
+Changing **Pass threshold** updates how past scores display as pass or fail; the recorded results don't change.
+
+## Related pages
+
+* [Measure and improve a factory](/factories/measure-and-improve/) - Read dashboard metrics and run a practical improvement loop.
+* [Configuring and reviewing Self-improvement](/factories/measure-and-improve/self-improvement/) - Turn repeated Scorer failures into follow-up pull requests.
+* [Benchmarking factory agent configurations](/factories/benchmarks/) - Compare model and runner configurations on the same tasks.

src/content/docs/factories/measure-and-improve/self-improvement.mdx

@@ -0,0 +1,20 @@
+---
+title: Configuring and reviewing Self-improvement
+description: >-
+  Turn on Self-improvement for a Scorer to turn its repeated failures into
+  follow-up pull requests you review.
+sidebar:
+  label: "Self-improvement"
+---
+
+{/* VISUAL: The Self-improvement pull request list, or a Benchmarks suite run -- this section is text-only today. */}
+
+Turn on **Self-improvement** for each Scorer whose failures you want investigated automatically. Self-improvement groups related failures and files follow-up tasks as ordinary agent runs. A follow-up run can propose changes to application code. It can also improve the factory itself: when you manage your factory as [definitions as code](/factories/factory-as-code/), its prompts, skills, and configuration are version-controlled files, so a follow-up run can open a pull request against the factory definition the same way it would against application code. Nothing is adopted without your review.
+
+The factory dashboard's **Self-improvement** page lists the pull requests these follow-up runs open. Each pull request includes a **Regressions addressed** section that links the failing runs and Scorer results behind it, so you can trace the change back to its evidence.
+
+## Related pages
+
+* [Measure and improve a factory](/factories/measure-and-improve/) - Read dashboard metrics and run a practical improvement loop.
+* [Configuring Scorers](/factories/measure-and-improve/scorers/) - Define the criteria whose failures Self-improvement investigates.
+* [Benchmarking factory agent configurations](/factories/benchmarks/) - Compare model and runner configurations on the same tasks.