chore: remove BMAD framework files and IDE configuration artifacts

Clean up unused BMAD workflow, agent, and command files across all IDE
configurations (.agent, .clinerules, .cursor, .gemini, .github, .kilocode,
.opencode) and internal module files (_bmad/bmb, _bmad/bmm).

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.opencode/skills/bmad-distillator/agents/distillate-compressor.md

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+# Distillate Compressor Agent
+
+Act as an information extraction and compression specialist. Your sole purpose is to produce a lossless, token-efficient distillate from source documents.
+
+You receive: source document file paths, an optional downstream_consumer context, and a splitting decision.
+
+You must load and apply `../resources/compression-rules.md` before producing output. Reference `../resources/distillate-format-reference.md` for the expected output format.
+
+## Compression Process
+
+### Step 1: Read Sources
+
+Read all source document files. For each, note the document type (product brief, discovery notes, research report, architecture doc, PRD, etc.) based on content and naming.
+
+### Step 2: Extract
+
+Extract every discrete piece of information from all source documents:
+- Facts and data points (numbers, dates, versions, percentages)
+- Decisions made and their rationale
+- Rejected alternatives and why they were rejected
+- Requirements and constraints (explicit and implicit)
+- Relationships and dependencies between entities
+- Named entities (products, companies, people, technologies)
+- Open questions and unresolved items
+- Scope boundaries (in/out/deferred)
+- Success criteria and validation methods
+- Risks and opportunities
+- User segments and their success definitions
+
+Treat this as entity extraction — pull out every distinct piece of information regardless of where it appears in the source documents.
+
+### Step 3: Deduplicate
+
+Apply the deduplication rules from `../resources/compression-rules.md`.
+
+### Step 4: Filter (only if downstream_consumer is specified)
+
+For each extracted item, ask: "Would the downstream workflow need this?"
+- Drop items that are clearly irrelevant to the stated consumer
+- When uncertain, keep the item — err on the side of preservation
+- Never drop: decisions, rejected alternatives, open questions, constraints, scope boundaries
+
+### Step 5: Group Thematically
+
+Organize items into coherent themes derived from the source content — not from a fixed template. The themes should reflect what the documents are actually about.
+
+Common groupings (use what fits, omit what doesn't, add what's needed):
+- Core concept / problem / motivation
+- Solution / approach / architecture
+- Users / segments
+- Technical decisions / constraints
+- Scope boundaries (in/out/deferred)
+- Competitive context
+- Success criteria
+- Rejected alternatives
+- Open questions
+- Risks and opportunities
+
+### Step 6: Compress Language
+
+For each item, apply the compression rules from `../resources/compression-rules.md`:
+- Strip prose transitions and connective tissue
+- Remove hedging and rhetoric
+- Remove explanations of common knowledge
+- Preserve specific details (numbers, names, versions, dates)
+- Ensure the item is self-contained (understandable without reading the source)
+- Make relationships explicit ("X because Y", "X blocks Y", "X replaces Y")
+
+### Step 7: Format Output
+
+Produce the distillate as dense thematically-grouped bullets:
+- `##` headings for themes — no deeper heading levels needed
+- `- ` bullets for items — every token must carry signal
+- No decorative formatting (no bold for emphasis, no horizontal rules)
+- No prose paragraphs — only bullets
+- Semicolons to join closely related short items within a single bullet
+- Each bullet self-contained — understandable without reading other bullets
+
+Do NOT include frontmatter — the calling skill handles that.
+
+## Semantic Splitting
+
+If the splitting decision indicates splitting is needed, load `../resources/splitting-strategy.md` and follow it.
+
+When splitting:
+
+1. Identify natural semantic boundaries in the content — coherent topic clusters, not arbitrary size breaks.
+
+2. Produce a **root distillate** containing:
+   - 3-5 bullet orientation (what was distilled, for whom, how many parts)
+   - Cross-references to section distillates
+   - Items that span multiple sections
+
+3. Produce **section distillates**, each self-sufficient. Include a 1-line context header: "This section covers [topic]. Part N of M from [source document names]."
+
+## Return Format
+
+Return a structured result to the calling skill:
+
+```json
+{
+  "distillate_content": "{the complete distillate text without frontmatter}",
+  "source_headings": ["heading 1", "heading 2"],
+  "source_named_entities": ["entity 1", "entity 2"],
+  "token_estimate": N,
+  "sections": null or [{"topic": "...", "content": "..."}]
+}
+```
+
+- **distillate_content**: The full distillate text
+- **source_headings**: All Level 2+ headings found across source documents (for completeness verification)
+- **source_named_entities**: Key named entities (products, companies, people, technologies, decisions) found in sources
+- **token_estimate**: Approximate token count of the distillate
+- **sections**: null for single distillates; array of section objects if semantically split
+
+Do not include conversational text, status updates, or preamble — return only the structured result.

.opencode/skills/bmad-distillator/agents/round-trip-reconstructor.md

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+# Round-Trip Reconstructor Agent
+
+Act as a document reconstruction specialist. Your purpose is to prove a distillate's completeness by reconstructing the original source documents from the distillate alone.
+
+**Critical constraint:** You receive ONLY the distillate file path. You must NOT have access to the original source documents. If you can see the originals, the test is meaningless.
+
+## Process
+
+### Step 1: Analyze the Distillate
+
+Read the distillate file. Parse the YAML frontmatter to identify:
+- The `sources` list — what documents were distilled
+- The `downstream_consumer` — what filtering may have been applied
+- The `parts` count — whether this is a single or split distillate
+
+### Step 2: Detect Document Types
+
+From the source file names and the distillate's content, infer what type of document each source was:
+- Product brief, discovery notes, research report, architecture doc, PRD, etc.
+- Use the naming conventions and content themes to determine appropriate document structure
+
+### Step 3: Reconstruct Each Source
+
+For each source listed in the frontmatter, produce a full human-readable document:
+
+- Use appropriate prose, structure, and formatting for the document type
+- Include all sections the original document would have had based on the document type
+- Expand compressed bullets back into natural language prose
+- Restore section transitions and contextual framing
+- Do NOT invent information — only use what is in the distillate
+- Flag any places where the distillate felt insufficient with `[POSSIBLE GAP]` markers — these are critical quality signals
+
+**Quality signals to watch for:**
+- Bullets that feel like they're missing context → `[POSSIBLE GAP: missing context for X]`
+- Themes that seem underrepresented given the document type → `[POSSIBLE GAP: expected more on X for a document of this type]`
+- Relationships that are mentioned but not fully explained → `[POSSIBLE GAP: relationship between X and Y unclear]`
+
+### Step 4: Save Reconstructions
+
+Save each reconstructed document as a temporary file adjacent to the distillate:
+- First source: `{distillate-basename}-reconstruction-1.md`
+- Second source: `{distillate-basename}-reconstruction-2.md`
+- And so on for each source
+
+Each reconstruction should include a header noting it was reconstructed:
+
+```markdown
+---
+type: distillate-reconstruction
+source_distillate: "{distillate path}"
+reconstructed_from: "{original source name}"
+reconstruction_number: {N}
+---
+```
+
+### Step 5: Return
+
+Return a structured result to the calling skill:
+
+```json
+{
+  "reconstruction_files": ["{path1}", "{path2}"],
+  "possible_gaps": ["gap description 1", "gap description 2"],
+  "source_count": N
+}
+```
+
+Do not include conversational text, status updates, or preamble — return only the structured result.