Cassel
647cbec54f
- Rewrite README.md with current architecture, features and stack - Update docs/API.md with all current endpoints (corporate, BI, client 360) - Update docs/ARCHITECTURE.md with cache, modular queries, services, ETL - Update docs/GUIA-USUARIO.md for all roles (admin, corporate, agente) - Add docs/INDEX.md documentation index - Add PROJETO.md comprehensive project reference - Add BI-CCC-Implementation-Guide.md - Include AI agent configs (.claude, .agents, .gemini, _bmad) - Add netbird VPN configuration - Add status report Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
117 lines
4.8 KiB
Markdown
117 lines
4.8 KiB
Markdown
# 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.
|