feat: initial architecture specification and research
Phase 0→1 setup for alknet-firewall — a behavioral signal detection library that screens untrusted LLM inputs using small model activations. Architecture docs (5 specs, 10 ADRs, 7 open questions): - overview: vision, scope, dependencies, package structure - firewall: core API, alarm protocol, score composition, error handling - codebook: SVD basis, spline distributions, calibration, tensor format - model: activation extraction, model-agnostic interface, lazy loading - configuration: thresholds, model selection, detection tuning Research reports: - modern-python-project-setup: uv, pyproject.toml, src layout, ruff, CI - python-ml-packaging: optional PyTorch, HF Hub download, safetensors - llm-input-safety-landscape: threat taxonomy, defenses, academic evidence Agent role adaptations for Python project (replaced Rust conventions).
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@@ -112,17 +112,17 @@ If blocked → Safe Exit (see below)
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### 4. Self-Verify
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```bash
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# Build
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cargo build
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# Lint
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cargo clippy -- -D warnings
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# Run tests
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cargo test
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uv run ruff check src/ tests/
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# Format check
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cargo fmt --check
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uv run ruff format --check src/ tests/
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# Type check
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uv run mypy src/
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# Run tests
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uv run pytest
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```
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Check each acceptance criterion in the task file.
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@@ -131,7 +131,7 @@ Check each acceptance criterion in the task file.
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```bash
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# Stage only source code — NOT task files
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git add src/ test/ docs/ # or specific files as appropriate
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git add src/ tests/ docs/ # or specific files as appropriate
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git commit -m "feat(<task-id>): <description>"
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git push origin $(git branch --show-current)
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```
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@@ -200,8 +200,8 @@ When available, use memory tools to manage your context:
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assistant messages if you lose track
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- `memory({tool: "search", args: {query: "..."}})` — search past conversations
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for relevant context
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- `memory_compact()` — compact at natural breakpoints (e.g., after completing a
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subtask) when context is above 80%
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- `memory_compact()` — compact at natural breakpoints (e.g., after completing
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a subtask) when context is above 80%
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This is especially important for complex tasks that span many file operations.
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@@ -209,16 +209,23 @@ This is especially important for complex tasks that span many file operations.
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Read `AGENTS.md` at project root for full details. Key rules:
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1. **No comments in code** — Per project convention.
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2. **Error handling** — Use `anyhow::Result` for application code, `thiserror` for
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library error types. Never panic in library code.
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3. **Feature flags** — Transports are feature-gated (`tls`, `iroh`, `acme`). Base
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crate should compile lean.
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4. **Async runtime** — `tokio` is the async runtime. All I/O is async.
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5. **Naming conventions** — Rust standard: `snake_case` for functions/variables/
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modules, `PascalCase` for types/traits, `SCREAMING_SNAKE_CASE` for constants.
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6. **Module structure** — One module per component under `src/`. Re-export via
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`mod.rs` or `lib.rs` as appropriate.
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1. **Type hints required** — All public functions must have type annotations.
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Use `mypy --strict` for validation.
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2. **Error handling** — Use custom exception classes for library errors
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(subclass from `AlknetFirewallError`). Use explicit `Result` patterns or
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raised exceptions; never silently swallow errors.
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3. **Optional dependencies** — PyTorch is an optional dependency via extras.
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Use lazy imports with clear error messages when torch is not installed.
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4. **Naming conventions** — Python standard: `snake_case` for functions/variables/
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modules, `PascalCase` for classes, `UPPER_SNAKE_CASE` for constants.
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5. **Module structure** — One module per component under `src/alknet_firewall/`.
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Use `__init__.py` for public API re-exports.
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6. **Testing** — Unit tests in `tests/`, integration tests marked with
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`@pytest.mark.integration`. Mock ML model loading in unit tests; use tiny
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models for integration tests.
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7. **No comments in code** — Per project convention. Use docstrings for public API.
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8. **safetensors only** — Never load pickle-based `.pt`/`.bin` model files.
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Always use safetensors format for security.
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## Key Principles
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