refactor: split builder prompt into invariant core + named packs (byte-identical assembly) + opt-in data-qa profile

.gitattributes

@@ -1,2 +1,6 @@
 packages/core/migration/**/snapshot.json linguist-generated
 packages/core/src/database/migration.gen.ts linguist-generated
+# Prompt fragments are byte-identity-pinned (sha256 asserted in
+# packages/opencode/test/altimate/prompt-profiles.test.ts); force LF so
+# autocrlf checkouts cannot alter the assembled prompt bytes.
+packages/opencode/src/altimate/prompts/** text eol=lf

packages/opencode/src/agent/agent.ts

@@ -18,7 +18,10 @@ import PROMPT_EXPLORE from "./prompt/explore.txt"
 import PROMPT_SUMMARY from "./prompt/summary.txt"
 import PROMPT_TITLE from "./prompt/title.txt"
 // altimate_change start - import custom agent mode prompts
-import PROMPT_BUILDER from "../altimate/prompts/builder.txt"
+// PromptProfiles.PROMPT_BUILDER is assembled from core + pack fragments (byte-identical
+// to the former builder.txt — see profiles.ts and test/altimate/prompt-profiles.test.ts)
+import { PromptProfiles } from "../altimate/prompts/profiles"
+import { Flag } from "@/flag/flag"
 import PROMPT_ANALYST from "../altimate/prompts/analyst.txt"
 import PROMPT_REVIEWER from "../altimate/prompts/reviewer.txt"
 // altimate_change end
@@ -224,7 +227,7 @@ export const layer = Layer.effect(
           builder: {
             name: "builder",
             description: "Create and modify dbt models, SQL, and data pipelines. Full read/write access.",
-            prompt: PROMPT_BUILDER,
+            prompt: PromptProfiles.PROMPT_BUILDER,
             options: {},
             permission: Permission.merge(
               defaults,
@@ -315,6 +318,38 @@ export const layer = Layer.effect(
             mode: "primary",
             native: true,
           },
+          // Opt-in data-qa profile (workload-adaptive harness PR 1): the invariant
+          // core + skills catalogue + teammate training — omits the Pre-Execution
+          // Protocol (sql-guard) pack and the build-oriented packs (dbt-ops,
+          // dbt-verify, dbt-workflow, pitfalls, self-review, finish). Ships the
+          // same DEFAULT permission ruleset as builder; per-agent config
+          // overrides apply per agent, as for every agent. Registered ONLY on
+          // explicit opt-in: ALTIMATE_DATA_QA_PROFILE=1/true, or an
+          // `agent: {"data-qa": {...}}` entry in config (which then overlays the
+          // native profile via the standard merge below). Nothing selects it
+          // implicitly; the default agent stays builder.
+          ...(Flag.truthyEnv("ALTIMATE_DATA_QA_PROFILE") || cfg.agent?.["data-qa"] != null
+            ? {
+                "data-qa": {
+                  name: "data-qa",
+                  description:
+                    "Opt-in data Q&A profile: builder toolset with a slimmer prompt (no dbt build protocols).",
+                  prompt: PromptProfiles.PROMPT_DATA_QA,
+                  options: {},
+                  permission: Permission.merge(
+                    defaults,
+                    Permission.fromConfig({
+                      question: "allow",
+                      plan_enter: "allow",
+                      sql_execute_write: "ask",
+                    }),
+                    userWithSafety,
+                  ),
+                  mode: "primary",
+                  native: true,
+                } satisfies Info,
+              }
+            : {}),
           // reviewer agent: dbt PR review verdict engine
           reviewer: {
             name: "reviewer",

packages/opencode/src/altimate/prompts/builder/core-training.txt

@@ -0,0 +1,24 @@
+## Teammate Training
+
+You are a trainable AI teammate. Your team has taught you patterns, rules, glossary terms, and standards that appear in the "Teammate Training" section of your system prompt. This is institutional knowledge — treat it as authoritative.
+
+### Applying Training
+- **Before writing code**: Check if any learned patterns or standards apply to what you're building. Follow them.
+- **Attribution**: When your output is influenced by a learned entry, briefly note it (e.g., "Following your staging-model pattern, I used CTEs for renaming columns."). This helps the user see that training is working.
+- **Conflicts**: If two training entries contradict each other, flag the conflict to the user and ask which takes precedence.
+
+### Detecting Corrections
+When the user corrects your behavior — explicitly or implicitly — recognize it as a teachable moment:
+- Explicit: "We never use FLOAT", "Always prefix staging models with stg_", "ARR means Annual Recurring Revenue"
+- Implicit: User rewrites your SQL to follow a convention, or consistently changes the same thing across interactions
+
+When you detect a correction:
+1. Acknowledge it and apply it immediately
+2. Offer: "Want me to remember this as a rule for future sessions?"
+3. If yes, use `training_save` with the appropriate kind, a slug name, and concise content
+
+### Available Training Tools
+- training_save — Save a learned pattern, rule, glossary term, or standard
+- training_list — List all learned training entries with budget usage
+- training_remove — Remove outdated training entries
+