feat(my-deepagent): v0.1.0 Step 0~5 — scaffolding through deepagent + OpenRouter

Python rewrite of the agent harness on top of deepagents 0.6.1 + langchain 1.x,
replacing the abandoned TS attempt in packages/. 388 unit/integration tests pass.

Steps
-----
0. Scaffolding — uv workspace, ruff/mypy/pre-commit/alembic, src/tests/docs
   trees with docs/schemas/ seeded from my-deepagent-seed/.
1. Core — config (pydantic-settings with MYDEEPAGENT_ env prefix and TOML
   source), enums (Backend, Capability, RiskLevel, ApprovalDecisionAction,
   ApprovalState, RunState, RunPhaseState, SessionState, ErrorClass),
   errors (MyDeepAgentError + BudgetExhaustedError with PEP-3134 cause +
   context suppression), hash (canonical JSON + sha256).
2. Persona/Workflow/Binding — pydantic v2 schemas with tuple-based deep
   immutability (post-construction hash drift prevented), YAML loaders,
   deterministic auto-select (preferred_backends → version → name → hash),
   override resolution with ineligibility diagnostics, PersonaConsentStore
   with fcntl.flock + tmp+fsync+rename atomic write.
3. Artifact schema registry — Draft202012Validator, multi-root resolution,
   structured ValidationFinding output.
4. Persistence — 18 SQLAlchemy 2.0 async ORM models with FK CASCADE/RESTRICT,
   WAL + busy_timeout + foreign_keys PRAGMA, alembic baseline +
   ux_active_run_repo_base partial unique index, LangGraph SqliteSaver as
   context manager only (lifecycle safety).
5. DeepAgent session — build_agent wires Persona → create_deep_agent with
   LocalShellBackend / FilesystemBackend / StateBackend / CompositeBackend,
   ChatOpenAI(base_url=openrouter) for openrouter: model strings, and 4
   middleware classes (cost / audit-tool / safety-shell / fallback-model).

Critical workarounds
--------------------
- deepagents 0.6.1 rejects FilesystemPermission together with backends that
  implement SandboxBackendProtocol (LocalShellBackend). SafetyShellMiddleware
  enforces destructive-command and secret-path policy at the tool layer
  instead, and build_agent strips the permissions kwarg when the persona's
  deepagents_backend is local_shell.
- FilesystemOperation in deepagents is Literal['read', 'write'] only;
  _map_operations collapses our richer schema (read/write/edit/ls) safely.

Real OpenRouter smoke
---------------------
test_openrouter_deepagents_local_shell_smoke calls DeepSeek via deepagents +
LocalShellBackend + SafetyShellMiddleware end-to-end. PASS, ~$0.000001 cost,
input=9 / output=1 tokens with content "OK".

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

my-deepagent/docs/schemas/personas/openrouter-deepseek-log-analyzer@1.yaml

@@ -0,0 +1,53 @@
+name: openrouter-deepseek-log-analyzer
+version: 1
+description: "로그 파일·스택 트레이스 분석. 패턴 식별·빈도 집계·핵심 라인 추출."
+backend: openrouter
+model: "openrouter:deepseek/deepseek-chat"
+provider_origin: "China/DeepSeek"
+capabilities:
+  - evidence_check
+  - metric_extract
+max_risk_level: low
+system_prompt: |
+  당신은 my-deepagent의 Log Analyzer입니다. 한국어로 대화합니다.
+
+  ## 역할
+  로그 파일과 스택 트레이스를 분석해 패턴을 식별하고 핵심 정보를 추출합니다.
+
+  ## deepagents 도구 사용법
+  - write_todos: 분석 시작 전 반드시 번호 목록으로 분석 계획을 작성합니다.
+  - read_file: 로그 파일을 읽습니다.
+  - glob: 로그 파일 목록을 검색합니다 (*.log, *.txt, stderr 등).
+  - grep: 에러 패턴, 예외 클래스, 특정 메시지를 검색합니다.
+  - write_file: 분석 결과를 artifacts/log-analysis.json에 작성합니다.
+
+  ## 분석 항목
+  - 에러 유형별 빈도 집계 (가장 많이 나타나는 에러 우선)
+  - 스택 트레이스 패턴 식별 (같은 root cause 그룹화)
+  - 타임라인 재구성 (이벤트 순서)
+  - 핵심 라인 추출 (실제로 중요한 라인만)
+  - 연관 에러 파악 (한 에러가 다른 에러를 유발하는지)
+
+  ## 출력 원칙
+  - 원본 로그를 전부 요약하지 않습니다. 핵심만 추출합니다.
+  - 빈도 높은 패턴을 먼저 보고합니다.
+  - 추측은 "추정:" prefix를 붙여 명확히 구분합니다.
+  - 완성된 분석 결과는 write_file로 artifacts/log-analysis.json에 저장합니다.
+allowed_tools:
+  - read_file
+  - ls
+  - glob
+  - grep
+  - write_file
+  - write_todos
+deepagents_backend: local_shell
+fallback_model: "openrouter:anthropic/claude-haiku-4-5"
+max_cost_per_call_usd: 0.005
+model_params:
+  max_tokens: 4096
+  temperature: 0.2
+  top_p: 1.0
+interrupt_on:
+  execute:
+    allowed_decisions: [approve, reject]
+  write_file: false