fix(prompts): tell agents about Serena (repo-intelligence MCP) for code exploration

We have .serena/ configured (cache, memories, project.local.yml) but no
prompt mentioned Serena anywhere. Agents weren't using it for symbol
lookup or cross-file architecture mapping; they fell straight to rg/find.

Added a one-sentence Serena hint to the code-exploration step in:
- research-slice.md
- research-milestone.md
- plan-slice.md
- plan-milestone.md
- guided-research-slice.md

Phrased generically ("If a repo-intelligence MCP (e.g. Serena) is
configured...") so it degrades cleanly when Serena isn't set up.

Pattern based on bunker commit 4ba746888 but written fresh against our
post-rename prompt structure rather than cherry-picked.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/resources/extensions/sf/prompts/guided-research-slice.md

@@ -1,4 +1,4 @@
-Research slice {{sliceId}} ("{{sliceTitle}}") of milestone {{milestoneId}}. Read `.sf/DECISIONS.md` if it exists — respect existing decisions, don't contradict them. Read `.sf/REQUIREMENTS.md` if it exists — identify which Active requirements this slice owns or supports and target research toward risks, unknowns, and constraints that could affect delivery of those requirements. {{skillActivation}} Explore the relevant code — use `rg`/`find` for targeted reads, or `scout` if the area is broad or unfamiliar. Check libraries DeepWiki-first: `ask_question` / `read_wiki_structure` / `read_wiki_contents` for any GitHub-hosted library; fall back to `resolve_library` / `get_library_docs` (Context7, capped at 1000 req/month free) for npm/pypi/crates packages DeepWiki doesn't have. Skip both for libraries already used in this codebase. Use the **Research** output template below. Call `sf_summary_save` with `milestone_id: {{milestoneId}}`, `slice_id: {{sliceId}}`, `artifact_type: "RESEARCH"`, and the research content — the tool writes the file to disk and persists to DB.
+Research slice {{sliceId}} ("{{sliceTitle}}") of milestone {{milestoneId}}. Read `.sf/DECISIONS.md` if it exists — respect existing decisions, don't contradict them. Read `.sf/REQUIREMENTS.md` if it exists — identify which Active requirements this slice owns or supports and target research toward risks, unknowns, and constraints that could affect delivery of those requirements. {{skillActivation}} If a repo-intelligence MCP (e.g. Serena) is configured, prefer it for symbol lookup, references, and cross-file architecture mapping. For direct text inspection use `rg`/`find` for targeted reads, or `scout` if the area is broad or unfamiliar. Check libraries DeepWiki-first: `ask_question` / `read_wiki_structure` / `read_wiki_contents` for any GitHub-hosted library; fall back to `resolve_library` / `get_library_docs` (Context7, capped at 1000 req/month free) for npm/pypi/crates packages DeepWiki doesn't have. Skip both for libraries already used in this codebase. Use the **Research** output template below. Call `sf_summary_save` with `milestone_id: {{milestoneId}}`, `slice_id: {{sliceId}}`, `artifact_type: "RESEARCH"`, and the research content — the tool writes the file to disk and persists to DB.
 
 **You are the scout.** A planner agent reads your output in a fresh context to decompose this slice into tasks. Write for the planner — surface key files, where the work divides naturally, what to build first, and how to verify. If the research doc is vague, the planner re-explores code you already read. If it's precise, the planner decomposes immediately.
 

src/resources/extensions/sf/prompts/plan-milestone.md

@@ -20,7 +20,7 @@ After you finish, each slice goes through its own plan → execute cycle. Slice
 
 Before decomposing, build your understanding:
 
-1. **Codebase exploration.** For small/familiar codebases, use `rg`, `find`, and targeted reads. For large or unfamiliar codebases, use `scout` to build a broad map efficiently before diving in.
+1. **Codebase exploration.** If a repo-intelligence MCP (e.g. Serena) is configured, prefer it for symbol lookup, references, and cross-file architecture mapping. For small/familiar codebases, use `rg`, `find`, and targeted reads. For large or unfamiliar codebases, use `scout` to build a broad map efficiently before diving in.
 2. **Library docs — DeepWiki first.** Use `ask_question` / `read_wiki_structure` / `read_wiki_contents` (DeepWiki) as the default for any GitHub-hosted library. Fall back to `resolve_library` / `get_library_docs` (Context7) for npm/pypi/crates packages DeepWiki doesn't have. Context7 free tier is capped at 1000 req/month — spend those on cases DeepWiki can't cover. Skip both for libraries already used in this codebase.
 3. **Skill Discovery ({{skillDiscoveryMode}}):**{{skillDiscoveryInstructions}}
 4. **Requirements analysis.** If `.sf/REQUIREMENTS.md` exists, research against it. Identify which Active requirements are table stakes, likely omissions, overbuilt risks, or domain-standard behaviors.

src/resources/extensions/sf/prompts/plan-slice.md

@@ -26,7 +26,7 @@ Check prior slice summaries (inlined above as dependency summaries, if present).
 
 ### Explore Slice Scope
 
-Read the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.
+Read the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. If a repo-intelligence MCP (e.g. Serena) is configured, prefer it for symbol lookup, references, and cross-file architecture mapping. Use `rg`, `find`, and targeted reads for direct text inspection.
 
 ### Source Files
 

src/resources/extensions/sf/prompts/research-milestone.md

@@ -31,7 +31,7 @@ A milestone adding a small feature to an established codebase needs targeted res
 Then research the codebase and relevant technologies. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.
 1. {{skillActivation}}
 2. **Skill Discovery ({{skillDiscoveryMode}}):**{{skillDiscoveryInstructions}}
-3. Explore relevant code. For small/familiar codebases, use `rg`, `find`, and targeted reads. For large or unfamiliar codebases, use `scout` to build a broad map efficiently before diving in.
+3. Explore relevant code. If a repo-intelligence MCP (e.g. Serena) is configured, prefer it for symbol lookup, references, and cross-file architecture mapping. For small/familiar codebases, use `rg`, `find`, and targeted reads. For large or unfamiliar codebases, use `scout` to build a broad map efficiently before diving in.
 4. **Documentation lookup — prefer DeepWiki first.** Use `ask_question` / `read_wiki_structure` / `read_wiki_contents` (DeepWiki) as the default for any GitHub-hosted library or framework — AI-indexed, no free-tier cap. Fall back to `resolve_library` → `get_library_docs` (Context7) for npm/pypi/crates packages DeepWiki doesn't have. **Context7 free tier is capped at 1000 requests/month — spend those on cases DeepWiki can't cover.** Skip both for libraries already used in this codebase.
 5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — try DeepWiki → Context7 → web search in that order. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.
 6. Use the **Research** output template from the inlined context above — include only sections that have real content

src/resources/extensions/sf/prompts/research-slice.md

@@ -44,7 +44,7 @@ Research what this slice needs. Narrate key findings and surprises as you go —
 0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.
 1. {{skillActivation}} Reference specific rules from loaded skills in your findings where they inform the implementation approach.
 2. **Skill Discovery ({{skillDiscoveryMode}}):**{{skillDiscoveryInstructions}}
-3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.
+3. Explore relevant code for this slice's scope. If a repo-intelligence MCP (e.g. Serena) is configured, prefer it for symbol lookup, references, and cross-file architecture mapping. For direct text inspection, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.
 4. **Documentation lookup — prefer DeepWiki first.** Use `ask_question` / `read_wiki_structure` / `read_wiki_contents` (DeepWiki) as the default for any GitHub-hosted library or framework — AI-indexed, no free-tier cap. Fall back to `resolve_library` → `get_library_docs` (Context7) for npm/pypi/crates packages DeepWiki doesn't have. **Context7 free tier is capped at 1000 requests/month — spend those on cases DeepWiki can't cover.** Skip both for libraries already used in this codebase.
 5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — try DeepWiki → Context7 → web search in that order. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.
 6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).