Standardize builders: runtime memlog + uv script invocations (#98)

* Standardize builders on runtime-installed memlog

Reference {project-root}/_bmad/scripts/memlog.py (sourced from bmm core
src/scripts) instead of bundling a per-skill copy, in bmad-workflow-builder
and bmad-agent-builder. Remove the bundled memlog.py copies and the
workflow-builder memlog test; rewrite the working-state teaching so produced
skills reference the runtime CLI rather than copying it in.

* Standardize script invocations on uv run

Replace `python3 <script>` with `uv run <script>` across bmad-workflow-builder
and bmad-agent-builder: prompt-facing invocations, script usage strings, and the
emitted init-sanctum/wake template usage. Align workflow-builder's
script-standards.md to mandate `uv run` for all scripts, matching agent-builder.

Left as-is: `#!/usr/bin/env python3` shebangs (stdlib-script convention per
script-standards), "runs under a bare python3" capability notes, and
`python3 -m pytest` test-runner docstrings.

Author: bmadcode

skills/bmad-agent-builder/SKILL.md

@@ -27,15 +27,15 @@ The builder produces agents along one gradient surfaced as feature decisions, no
 
 ## On Activation
 
-1. **Resolve customization.** Run `python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent` and apply the resolved `{agent.*}` values throughout the session. On failure, read `{skill-root}/customize.toml` directly and use defaults. Then execute each entry in `{agent.activation_steps_prepend}` in order, and treat every entry in `{agent.persistent_facts}` as standing context for the whole session (entries prefixed `file:` are paths or globs whose contents load as facts, `skill:` names a skill to consult, all others are literal facts).
+1. **Resolve customization.** Run `uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent` and apply the resolved `{agent.*}` values throughout the session. On failure, read `{skill-root}/customize.toml` directly and use defaults. Then execute each entry in `{agent.activation_steps_prepend}` in order, and treat every entry in `{agent.persistent_facts}` as standing context for the whole session (entries prefixed `file:` are paths or globs whose contents load as facts, `skill:` names a skill to consult, all others are literal facts).
 
 2. **Detect intent.** If `--headless` or `-H` is present, set `{headless_mode}=true` for every sub-prompt; this makes the builder non-interactive and is not the Pulse Mode a built autonomous agent runs at its own runtime. Otherwise read the invocation for whether the user wants to Create, Edit, or Analyze, and which agent they mean.
 
 3. **Load config.** Read `{project-root}/_bmad/config.yaml` and `{project-root}/_bmad/config.user.yaml` (root and bmb section), falling back to `{project-root}/_bmad/bmb/config.yaml`. If none exist and `bmad-bmb-setup` is available, mention it. Resolve and apply throughout (defaults in parens): `{user_name}` (null), `{communication_language}` (user or system default), `{document_output_language}` (user or system default), and `{bmad_builder_output_folder}` (`{project-root}/skills`, where new agents are created; existing agents keep their own path).
 
 4. **Open the floor (interactive only).** Before any structured questions or routing, invite the user to share everything in mind: who the agent is, how it should make them feel, the core outcome, examples, half-formed ideas, paths to existing agents or artifacts. Adapt the invitation to what they already gave you, then one soft "anything else?" surfaces what they almost forgot. This dump replaces most downstream questioning, so let it run. Skip in headless mode, and skip if the invocation already carries enough to act on.
 
-5. **Resume detection.** Once a target agent is identified, glob `{target-agent-path}/.memlog.md`. If one exists, read it once in full to rebuild the prior session's state, then continue append-only through `scripts/memlog.py`. This `.memlog.md` is the builder's process log and is separate from the agent's sanctum. In headless mode, resume automatically.
+5. **Resume detection.** Once a target agent is identified, glob `{target-agent-path}/.memlog.md`. If one exists, read it once in full to rebuild the prior session's state, then continue append-only through `{project-root}/_bmad/scripts/memlog.py`. This `.memlog.md` is the builder's process log and is separate from the agent's sanctum. In headless mode, resume automatically.
 
 6. **Route to the intent.** Pick the path below from the resolved intent and load only that file. Once the intent is routed, execute each entry in `{agent.activation_steps_append}` in order before the loop begins.
 

skills/bmad-agent-builder/assets/SKILL-template-bootloader.md

@@ -55,7 +55,7 @@ The full discipline (what goes where, the two-tier flow from session log to MEMO
 {if-customizable}
 ### Resolve the Agent Block
 
-Run: `python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent`
+Run: `uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent`
 
 If the script fails, resolve the `agent` block yourself by reading these three files in base → team → user order and applying structural merge rules: `{skill-root}/customize.toml`, `{project-root}/_bmad/custom/{skill-name}.toml`, `{project-root}/_bmad/custom/{skill-name}.user.toml`. Scalars override, tables deep-merge, arrays of tables keyed by `code`/`id` replace matching entries and append new ones, all other arrays append.
 

skills/bmad-agent-builder/assets/SKILL-template.md

@@ -42,7 +42,7 @@ description: { skill-description } # [4-6 word summary]. [trigger phrases]
 {if-customizable}
 ### Step 1: Resolve the Agent Block
 
-Run: `python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent`
+Run: `uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent`
 
 If the script fails, resolve the `agent` block yourself by reading these three files in base → team → user order and applying structural merge rules: `{skill-root}/customize.toml`, `{project-root}/_bmad/custom/{skill-name}.toml`, `{project-root}/_bmad/custom/{skill-name}.user.toml`. Scalars override, tables deep-merge, arrays of tables keyed by `code`/`id` replace matching entries and append new ones, all other arrays append.
 

skills/bmad-agent-builder/assets/init-sanctum-template.py

@@ -17,7 +17,7 @@
 touches.
 
 Usage:
-    python3 init-sanctum.py <project-root> <skill-path>
+    uv run init-sanctum.py <project-root> <skill-path>
 
     project-root: The root of the project (where _bmad/ lives)
     skill-path:   Path to the skill directory (where SKILL.md, references/, assets/ live)
@@ -187,7 +187,7 @@ def substitute_vars(content: str, variables: dict) -> str:
 
 def main():
     if len(sys.argv) < 3:
-        print("Usage: python3 init-sanctum.py <project-root> <skill-path>")
+        print("Usage: uv run init-sanctum.py <project-root> <skill-path>")
         sys.exit(1)
 
     project_root = Path(sys.argv[1]).resolve()

skills/bmad-agent-builder/assets/wake-template.py

@@ -14,7 +14,7 @@
 This loads runtime memory only. It never reads or writes config or customize.toml.
 
 Usage:
-    python3 wake.py <project-root> [--pulse]
+    uv run wake.py <project-root> [--pulse]
 
     project-root: The root of the project (where _bmad/ lives)
 """

skills/bmad-agent-builder/references/build-process.md

@@ -25,7 +25,7 @@ The dump tells you what the user pictured; offer what they did not. Before draft
 
 ## Capture into the memlog throughout
 
-As decisions and directions land, write them to `{target-agent-path}/.memlog.md` through `scripts/memlog.py`: `init --path {target-agent-path}/.memlog.md` once when the target is named, then `append --path {target-agent-path}/.memlog.md --type <decision|direction|assumption|gap|note|event> --text "..."` as things happen. For a new agent, propose a kebab-case name when the user did not give one; renaming later is a logged decision, not a redo. This `.memlog.md` is the builder's process trace beside the built agent's SKILL.md, never the agent's sanctum — a memlog entry records a build decision, sanctum content is the agent's living runtime state, and neither ever holds the other's material. Capture as you go so the reasoning is caught while fresh, because the memlog is the resume source and the trail you walk with the user at handoff.
+As decisions and directions land, write them to `{target-agent-path}/.memlog.md` through `{project-root}/_bmad/scripts/memlog.py`: `init --path {target-agent-path}/.memlog.md` once when the target is named, then `append --path {target-agent-path}/.memlog.md --type <decision|direction|assumption|gap|note|event> --text "..."` as things happen. For a new agent, propose a kebab-case name when the user did not give one; renaming later is a logged decision, not a redo. This `.memlog.md` is the builder's process trace beside the built agent's SKILL.md, never the agent's sanctum — a memlog entry records a build decision, sanctum content is the agent's living runtime state, and neither ever holds the other's material. Capture as you go so the reasoning is caught while fresh, because the memlog is the resume source and the trail you walk with the user at handoff.
 
 ## Write the minimal outcome-driven version first
 
@@ -69,7 +69,7 @@ Confirm the agent passes its own leanness bar before handoff, because the builde
 
 Every agent shares one output tree. The archetype changes which parts are present and the SKILL.md weight, captured in the delta table below rather than three separate trees.
 
-Emit each file from its matching template in this builder's `assets/`, applying `references/template-substitution-rules.md` for tokens, conditionals, and template selection — deterministically, via `python3 scripts/process-template.py <template> -o <dest> --var key=value... --true <condition>...` (one `--var` per token, one `--true` per conditional that holds). The templates are the single source for every emitted file, including `assets/init-sanctum-template.py`, `assets/wake-template.py`, `assets/memory-guidance-template.md`, and the two First Breath templates. The files whose content you author rather than substitute have guidance — load each at the moment you author that file, not before: `references/mission-writing-guidance.md` for the species mission, `references/standing-order-guidance.md` for CREED standing orders, `references/first-breath-adaptation-guidance.md` for deriving the First Breath territories, and `references/sample-capability-authoring.md` for the emitted capability-authoring.md.
+Emit each file from its matching template in this builder's `assets/`, applying `references/template-substitution-rules.md` for tokens, conditionals, and template selection — deterministically, via `uv run scripts/process-template.py <template> -o <dest> --var key=value... --true <condition>...` (one `--var` per token, one `--true` per conditional that holds). The templates are the single source for every emitted file, including `assets/init-sanctum-template.py`, `assets/wake-template.py`, `assets/memory-guidance-template.md`, and the two First Breath templates. The files whose content you author rather than substitute have guidance — load each at the moment you author that file, not before: `references/mission-writing-guidance.md` for the species mission, `references/standing-order-guidance.md` for CREED standing orders, `references/first-breath-adaptation-guidance.md` for deriving the First Breath territories, and `references/sample-capability-authoring.md` for the emitted capability-authoring.md.
 
 ```
 {agent-name}/

skills/bmad-agent-builder/references/quality-analysis.md

@@ -28,9 +28,9 @@ Confirm the agent is resolvable at `{target-agent-path}` and that a `SKILL.md` i
 Run the pre-pass once, before any lens sees the agent, so every lens reads a compact classification and token picture instead of re-deriving it from raw text:
 
 ```bash
-python3 scripts/prepass.py {target-agent-path}
-python3 scripts/scan-path-standards.py {target-agent-path}
-python3 scripts/scan-scripts.py {target-agent-path}
+uv run scripts/prepass.py {target-agent-path}
+uv run scripts/scan-path-standards.py {target-agent-path}
+uv run scripts/scan-scripts.py {target-agent-path}
 ```
 
 The two lint scanners return deterministic findings as JSON; carry their entries straight into the merged findings list with ids `lint-<n>`, keeping their severities. They are facts, not judgment, so no lens re-derives them.
@@ -143,7 +143,7 @@ Rules:
 Write the island object to `{run-folder}/findings.json` and render:
 
 ```bash
-python3 scripts/render_report.py {run-folder}/findings.json --shell assets/report-shell.html -o {run-folder}/agent-analysis-report.html --md {run-folder}/agent-analysis-report.md
+uv run scripts/render_report.py {run-folder}/findings.json --shell assets/report-shell.html -o {run-folder}/agent-analysis-report.html --md {run-folder}/agent-analysis-report.md
 ```
 
 If the script refuses, fix `findings.json` and re-run; never hand-edit the HTML. Open the HTML report for the user — it is the deliverable of Analyze; do not replace it with a chat summary of the findings. The shell fails loud: a malformed island shows a visible banner, never a blank page, and an empty findings array renders an explicit no-findings panel, so a clean agent still produces a real report.
@@ -153,7 +153,7 @@ If the script refuses, fix `findings.json` and re-run; never hand-edit the HTML.
 Append one memlog event carrying the grade (init the memlog first if `{target-agent-path}/.memlog.md` does not exist):
 
 ```bash
-python3 scripts/memlog.py append --path {target-agent-path}/.memlog.md --type event --text "analyze: grade <grade>, <c> critical / <h> high / <m> medium / <l> low, report .analysis/<timestamp>/agent-analysis-report.html"
+uv run {project-root}/_bmad/scripts/memlog.py append --path {target-agent-path}/.memlog.md --type event --text "analyze: grade <grade>, <c> critical / <h> high / <m> medium / <l> low, report .analysis/<timestamp>/agent-analysis-report.html"
 ```
 
 ## Present

skills/bmad-agent-builder/references/template-substitution-rules.md

@@ -1,6 +1,6 @@
 # Template Substitution Rules
 
-The SKILL-template provides a minimal skeleton: frontmatter, overview, agent identity sections, memory, and the activation spine. The bootloader carries no standalone config-load step — `init-sanctum` bakes config into the sanctum, so wake.py loads it as part of the identity. Everything beyond the skeleton is crafted by the builder based on what was learned during discovery. Apply these rules deterministically via `python3 scripts/process-template.py <template> -o <dest> --var key=value... --true <condition>...` — one `--var` per token, one `--true` per conditional that holds. The script fails (exit 3) on any leftover `{if-...}` marker and reports remaining `{token}` placeholders as `tokens_remaining` for you to judge against the runtime-token set.
+The SKILL-template provides a minimal skeleton: frontmatter, overview, agent identity sections, memory, and the activation spine. The bootloader carries no standalone config-load step — `init-sanctum` bakes config into the sanctum, so wake.py loads it as part of the identity. Everything beyond the skeleton is crafted by the builder based on what was learned during discovery. Apply these rules deterministically via `uv run scripts/process-template.py <template> -o <dest> --var key=value... --true <condition>...` — one `--var` per token, one `--true` per conditional that holds. The script fails (exit 3) on any leftover `{if-...}` marker and reports remaining `{token}` placeholders as `tokens_remaining` for you to judge against the runtime-token set.
 
 ## Frontmatter