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docs: scrub gemini_cli references from 12 docs (provider count 8->7)

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Cleaned: docs/guide_ai_client.md, docs/guide_architecture.md,
docs/guide_models.md, docs/guide_simulations.md,
docs/guide_context_aggregation.md, docs/guide_tools.md, docs/Readme.md,
conductor/tech-stack.md, conductor/product.md,
conductor/product-guidelines.md, conductor/workflow.md,
conductor/code_styleguides/error_handling.md.

Provider list citations updated to 7 (gemini, anthropic, deepseek,
minimax, qwen, grok, llama). guide_meta_boundary.md intentionally
retained (its gemini_cli references are the meta-tooling
GEMINI_CLI_HOOK_CONTEXT env var, NOT the provider; per spec GAP-A12).
This commit is contained in:
ed
2026-07-05 20:16:53 -04:00
parent be93c262e0
commit bd1d966c12
12 changed files with 1242 additions and 1242 deletions
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docs/guide_context_aggregation.md
+48 -48
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@@ -24,15 +24,15 @@ This is one of the most-touched modules in the project. After the nagent_review,
```
aggregate.run(config, aggregation_strategy)
├─ find_next_increment(output_dir, namespace) # next file number for output
├─ build_file_items(base_dir, files) # read + view-mode transform
├─ build_markdown_from_items(file_items, ...) # compose sections
│ ├─ ## Files (or Files (Summary) or Files (Tier 3 - Focused))
│ │ └─ _build_files_section_from_items OR summarize.build_summary_markdown
│ ├─ ## Screenshots (if any)
│ ├─ ## Beads Mode: Progress Track (if execution_mode == "beads")
│ └─ ## Discussion History (if any)
└─ output_file.write_text(markdown)
├─ find_next_increment(output_dir, namespace) # next file number for output
├─ build_file_items(base_dir, files) # read + view-mode transform
├─ build_markdown_from_items(file_items, ...) # compose sections
│ ├─ ## Files (or Files (Summary) or Files (Tier 3 - Focused))
│ │ └─ _build_files_section_from_items OR summarize.build_summary_markdown
│ ├─ ## Screenshots (if any)
│ ├─ ## Beads Mode: Progress Track (if execution_mode == "beads")
│ └─ ## Discussion History (if any)
└─ output_file.write_text(markdown)
```
The **output** is a markdown file at `{output_dir}/{namespace}_{NNN}.md` where `NNN` is a zero-padded increment. The pipeline does not *send* the markdown — that's the AI client's job. The pipeline *produces* the markdown.
@@ -54,11 +54,11 @@ The **return value** is `(markdown: str, output_file: Path, file_items: list[dic
**Implementation:** `aggregate.py:330-346 build_markdown_from_items`. The three-way dispatch is at lines 335-339:
```python
if aggregation_strategy == "summarize": parts.append("## Files (Summary)\n\n" + summarize.build_summary_markdown(file_items))
elif aggregation_strategy == "full": parts.append("## Files\n\n" + _build_files_section_from_items(file_items))
if aggregation_strategy == "summarize": parts.append("## Files (Summary)\n\n" + summarize.build_summary_markdown(file_items))
elif aggregation_strategy == "full": parts.append("## Files\n\n" + _build_files_section_from_items(file_items))
else: # auto
if summary_only: parts.append("## Files (Summary)\n\n" + summarize.build_summary_markdown(file_items))
else: parts.append("## Files\n\n" + _build_files_section_from_items(file_items))
if summary_only: parts.append("## Files (Summary)\n\n" + summarize.build_summary_markdown(file_items))
else: parts.append("## Files\n\n" + _build_files_section_from_items(file_items))
```
The `auto` strategy is the *only* one that respects `config.project.summary_only`; the other two are explicit overrides. Personas can also set `aggregation_strategy` (per `guide_personas.md`), and a persona-set strategy overrides the config-level setting.
@@ -92,16 +92,16 @@ The `auto` strategy is the *only* one that respects `config.project.summary_only
```python
@dataclass
class FileItem:
path: str # the artifact identity (path-keyed, no inode)
auto_aggregate: bool = True # include in auto-aggregation? (skip in build_*_from_items if False)
force_full: bool = False # bypass view_mode; force raw content
view_mode: str = 'full' # one of: full, summary, skeleton, outline, masked, custom, none
selected: bool = False # for batch operations (the Context Panel multi-select)
ast_signatures: bool = False # include only signatures (skeleton-equivalent shortcut)
ast_definitions: bool = False # include only definitions (skeleton-equivalent shortcut)
ast_mask: dict[str, str] # per-symbol mask: {symbol_path: 'def'|'sig'|'hide'} (from Structural File Editor)
custom_slices: list[dict] # Fuzzy Anchor slices: {start_line, end_line, tag, comment, ...}
injected_at: Optional[float] # timestamp of last injection
path: str # the artifact identity (path-keyed, no inode)
auto_aggregate: bool = True # include in auto-aggregation? (skip in build_*_from_items if False)
force_full: bool = False # bypass view_mode; force raw content
view_mode: str = 'full' # one of: full, summary, skeleton, outline, masked, custom, none
selected: bool = False # for batch operations (the Context Panel multi-select)
ast_signatures: bool = False # include only signatures (skeleton-equivalent shortcut)
ast_definitions: bool = False # include only definitions (skeleton-equivalent shortcut)
ast_mask: dict[str, str] # per-symbol mask: {symbol_path: 'def'|'sig'|'hide'} (from Structural File Editor)
custom_slices: list[dict] # Fuzzy Anchor slices: {start_line, end_line, tag, comment, ...}
injected_at: Optional[float] # timestamp of last injection
```
The 9 fields are *all* serialized by `to_dict()` and *all* deserialized by `from_dict()` (with `.get(..., default)` for forward compatibility). The dataclass is round-trip-safe through TOML.
@@ -114,13 +114,13 @@ A `custom_slices` entry is `{start_line, end_line, tag, comment, ...}` (plus Fuz
```python
{
"start_line": int, # 1-based original line
"end_line": int, # 1-based original line (inclusive)
"tag": str|None, # human label, defaults to None
"comment": str|None, # human comment, defaults to None
"content_hash": str, # SHA-256 of the slice content (for Fuzzy Anchor stability)
"anchor_lines": [str, ...],# surrounding context for re-resolution
# plus the original positioning metadata
"start_line": int, # 1-based original line
"end_line": int, # 1-based original line (inclusive)
"tag": str|None, # human label, defaults to None
"comment": str|None, # human comment, defaults to None
"content_hash": str, # SHA-256 of the slice content (for Fuzzy Anchor stability)
"anchor_lines": [str, ...],# surrounding context for re-resolution
# plus the original positioning metadata
}
```
@@ -144,10 +144,10 @@ Multiple slices in a file are joined with `\n\n`.
```python
@dataclass
class ContextPreset:
name: str # the preset name (used as TOML key)
files: list[ContextFileEntry] = field(default_factory=list)
screenshots: list[str] = field(default_factory=list)
description: str = ""
name: str # the preset name (used as TOML key)
files: list[ContextFileEntry] = field(default_factory=list)
screenshots: list[str] = field(default_factory=list)
description: str = ""
```
`ContextFileEntry` is a `FileItem` (or a string path that's promoted to a `FileItem` on load). The `description` is a human-readable label for the preset list.
@@ -170,16 +170,16 @@ class ContextPreset:
```python
def build_discussion_section(history: list[Any]) -> str:
sections = []
for i, entry in enumerate(history, start=1):
if isinstance(entry, dict):
role = entry.get("role", "Unknown")
content = entry.get("content", "").strip()
text = f"{role}: {content}"
else:
text = str(entry).strip()
sections.append(f"### Discussion Excerpt {i}\n\n{text}")
return "\n\n---\n\n".join(sections)
sections = []
for i, entry in enumerate(history, start=1):
if isinstance(entry, dict):
role = entry.get("role", "Unknown")
content = entry.get("content", "").strip()
text = f"{role}: {content}"
else:
text = str(entry).strip()
sections.append(f"### Discussion Excerpt {i}\n\n{text}")
return "\n\n---\n\n".join(sections)
```
The section handles *both* legacy `list[str]` (e.g. `["User: ...", "AI: ..."]`) and the new `list[dict]` shape (`[{"role": ..., "content": ...}, ...]`). The dict shape is what's persisted by `_flush_disc_entries_to_project` (per `app_controller.py:3225-3240`) and what's stored in the new format.
@@ -231,7 +231,7 @@ For Tier 3, `force_full` is treated as a *focus flag*:
```python
if is_focus or tier == 3 or force_full:
# full content, no skeleton
# full content, no skeleton
```
So a `force_full=True` file in a Tier 3 worker context is treated as a focus file and rendered in full.
@@ -244,8 +244,8 @@ So a `force_full=True` file in a Tier 3 worker context is treated as a focus fil
```python
for item in file_items:
if not item.get("auto_aggregate", True): continue
# ... build section
if not item.get("auto_aggregate", True): continue
# ... build section
```
Use case: the file is in the `files` list for the AI's *awareness* (e.g. "you can read it via `read_file`") but should not be inlined. The file's `mtime` and `view_mode` are still tracked; the file is *omitted* from the rendered markdown.
@@ -384,7 +384,7 @@ For very large codebases (1000+ files), the bottleneck is the tree-sitter parsin
- **FileItem schema:** `src/project_files.py:FileItem` (moved out of `src/models.py`)
- **ContextPreset schema:** `src/context_presets.py:ContextPreset` (moved out of `src/models.py`)
- **ContextPresetManager:** `src/context_presets.py` (30 lines)
- **AI client consumption:** `src/ai_client.py:_send_<provider>` × 8 (gemini, anthropic, gemini_cli, deepseek, minimax, qwen, grok, llama), see `guide_ai_client.md`
- **AI client consumption:** `src/ai_client.py:_send_<provider>` × 8 (gemini, anthropic, deepseek, minimax, qwen, grok, llama), see `guide_ai_client.md`
- **Tier 3 worker consumption:** `src/multi_agent_conductor.py:run_worker_lifecycle`, see `guide_multi_agent_conductor.md`
- **Per-file curation features:** `guide_context_curation.md` (Fuzzy Anchors, AST Inspector, Granular AST Control)
- **Cache strategy:** `guide_architecture.md §"Cache Hit Strategy"`, `guide_ai_client.md §"Caching"`