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docs: reduce redundant content across files; map references to canonical sources

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Per user 'a bunch of docs just committed had redundant content across
files. Can we do a reduction of that and instead map references to
other files?'

This commit reduces content duplication across 9 files. The
canonical sources are kept as detailed references; the other
files now point to them.

Reductions (table replaced with 'see canonical' reference):

1. data_oriented_design.md §9: the 4-dim memory table
   (canonical: conductor/code_styleguides/agent_memory_dimensions.md §0)

2. guide_agent_memory_dimensions.md §0: the 4-dim memory table
   (canonical: conductor/code_styleguides/agent_memory_dimensions.md §0)

3. guide_caching_strategy.md §1: the 12-layer model
   (canonical: conductor/code_styleguides/cache_friendly_context.md §1)

4. guide_ai_client.md 'Cache strategy' section: the 12-layer model recap
   (canonical: conductor/code_styleguides/cache_friendly_context.md §1)

5. guide_knowledge_curation.md §1: the 5 category file details
   (canonical: conductor/code_styleguides/knowledge_artifacts.md §1)

6. product-guidelines.md 'Memory Dimensions' section: the 4-dim table
   (canonical: conductor/code_styleguides/agent_memory_dimensions.md §0)

7. guide_mma.md '4 memory dimensions' section: the MMA scope table
   (canonical: conductor/code_styleguides/agent_memory_dimensions.md §0)

8. docs/AGENTS.md §0 + §5-§8: 4-dim table + caching/knowledge/RAG/
   feature flag tables (canonical: the per-topic styleguides in
   conductor/code_styleguides/)

9. AGENTS.md 'Code Styleguides' section: the 6-styleguide list
   (canonical: docs/AGENTS.md §2)

The principle: each piece of content has ONE source of truth; other
places point to it. The data-oriented way. Files retain their
narrative flow and the 'what this is' intros, but the detailed
tables are now in their canonical home.

Net effect: -2100 bytes across 9 files (without losing any
information - the canonical sources are unchanged). The
'cross-references' sections are kept; the duplicated content
is removed.
This commit is contained in:
Edward R. Gonzalez
2026-06-12 14:10:30 -04:00
parent 35c6cca134
commit 434b6d0d54
9 changed files with 58 additions and 191 deletions
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docs/guide_agent_memory_dimensions.md
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@@ -10,14 +10,9 @@
## 0. The 30-second version
Manual Slop has 4 memory dimensions for the conversation data:
Manual Slop has 4 memory dimensions for the conversation data. **The full canonical table is in `conductor/code_styleguides/agent_memory_dimensions.md` §0** (curation / discussion / RAG / knowledge, with the SSDL shape tag per dim).
| # | Dim | Where it lives | What it stores | Status |
|---|---|---|---|---|
| 1 | **Curation** | `FileItem` + `ContextPreset` + Fuzzy Anchors | *How to render a file* in the AI's context window | Existing, strong |
| 2 | **Discussion** | `app.disc_entries` + branching + UISnapshot | *What was said* in the conversation | Existing, strong |
| 3 | **RAG** | `src/rag_engine.py` (ChromaDB) | *Semantic fingerprints* of indexed files | Opt-in |
| 4 | **Knowledge** | `~/.manual_slop/knowledge/*.md` + per-file + digest | *Durable learnings* from past sessions | Proposed (Candidate 8) |
**The one-line summary:** curation is per-file structural; discussion is per-turn conversational; RAG is opt-in semantic; knowledge is per-project durable. Pick the matching dimension; don't reach for the wrong shape.
**The decision tree:**