feat: initial scaffold with context adapters and litellm pkg
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Mathias Bergqvist
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.aider.conventions.md
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# Agent context — Mathias workspace
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<!-- Canonical root context for all AI coding agents.
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Lives at: ~/dev/.context/AGENT.md
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Applies to every project under ~/dev/ unless overridden.
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Run `task context:sync` from ~/dev/ to regenerate harness-specific files.
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Project-level context in .context/PROJECT.md layers on top of this. -->
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## Who I am
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I'm Mathias, a digital product manager and technology consultant based in Sweden.
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I build software, research emerging tech, and deliver consulting engagements
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for clients under NDA. I work across AI/ML, financial automation, web applications,
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and climate/sustainability tech.
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## How I work with agents
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- I think like a product manager — I care about *why* before *how*
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- I want agents to be opinionated and push back, not just execute blindly
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- I prefer concise responses; skip ceremony and get to the point
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- When I say "build this", I mean production-quality with tests, not a demo
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- Ask me before making irreversible changes or adding heavy dependencies
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- I work with confidential client data — never send it to cloud APIs unless I explicitly say it's OK
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## Behavior rules
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These rules apply to every task across every project, regardless of harness.
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1. **No assumptions.** Don't hide confusion — surface it. Surface tradeoffs explicitly.
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Think before coding; if the problem is unclear, ask or state assumptions before acting.
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2. **Minimum viable code.** Solve with the smallest change that works. Nothing
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speculative, no "while we're here" cleanups, no premature abstractions. Simplicity first.
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3. **Surgical changes.** Touch only what the task requires. Leave unrelated code,
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files, and formatting alone. Diffs should be small and reviewable.
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4. **Goal-driven execution.** Define clear success criteria up front for every task.
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Loop — implement, verify, refine — until those criteria are met. Don't claim
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completion without evidence (tests pass, command output, observed behavior).
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5. **Trunk-Based Development — commit directly to main.** Every commit is one
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logical change (one tool, one fix, one test) with passing tests. Main is always
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deployable. Never create long-lived feature branches.
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**Exception — parallel agents on same repo:** If another agent is known to be
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actively working on the same repo simultaneously, create a short-lived branch
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(`agent/<description>`), finish the task, and merge to main within the same
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session. Do not leave agent branches open between sessions.
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**Exception — external contributor or client four-eyes requirement:** Use
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PR flow only when a human reviewer outside the project is required. Document
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the reason in PROJECT.md.
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## Default stack
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| Layer | Default | Fallback | Last resort |
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|-------|---------|----------|-------------|
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| Language | Go | Python | TypeScript, Java, C |
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| UI | HTMX + Templ | Server-rendered HTML | React (only if SPA is justified) |
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| Build | Task (taskfile.dev) | Make | — |
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| Containers | Docker Compose (dev), k3s (prod) | — | — |
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| DB | PostgreSQL + sqlc | SQLite | — |
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| Search | pgvector (vector), BM25 | Qdrant (when >1M vectors or hybrid retrieval) | — |
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| Logging | slog (structured) | — | — |
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| Testing | Table-driven, testify | — | — |
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| Agents (Go) | google.golang.org/adk + pkg/litellm adapter | — | — |
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Exploratory: Rust, Zig — I'll tell you when I want these.
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## Code conventions
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- **Go style**: golines, gofumpt, golangci-lint
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- **Errors**: `fmt.Errorf("operation: %w", err)` — never naked, never log-and-return
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- **Naming**: stdlib conventions, no stuttering
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- **Architecture**: prefer stdlib over frameworks, constructor injection, env-var config parsed into typed structs
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- **Git**: conventional commits (`feat:`, `fix:`, `chore:`), commit directly to main,
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one logical change per commit, CI is the quality gate
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- **Never**: long-lived feature branches, PRs for solo work, direct push without
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passing `task check` locally first
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- **Security**: no secrets in code, govulncheck before adding deps, SOPS for encrypted config
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- **Dependencies**: prefer stdlib. testify, slog, templ, sqlc, google.golang.org/adk (agent projects only) are pre-approved; anything else needs justification in the commit message
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## Infrastructure
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Three machines on Tailscale:
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| Machine | Role | Key specs |
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|---------|------|-----------|
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| koala | GPU inference, heavy compute | RTX 5070, runs k3s + llama-swap + shared postgres18/pgvector |
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| iguana | Services, builds | M2 Ultra Mac |
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| flamingo | Daily driver, edge | Mac mini, ~/dev is here |
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- **Model routing**: LiteLLM in front of llama-swap (local) + cloud APIs (when permitted)
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- **Orchestration**: k3s cluster across all three machines
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- **Networking**: Tailscale mesh
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## Project landscape
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All development repos live at `~/dev/` (softlink from `~/Documents/local-dev/`).
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Organized in thematic folders:
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| Folder | Focus | Count |
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|--------|-------|-------|
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| `GO/` | Go web frameworks, API integrations, learning projects | ~10 |
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| `AI/` | ML research, AI frameworks (FinRL, DSPy, crawl4ai) | ~6 |
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| `AGENTS/` | Autonomous agents, coding agents, MCP servers, infra | ~15 |
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| `QKX/` | Invoice processing, financial automation, payment systems | ~13 |
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| `XT/` | Climate data, sustainability (Klimatkollen, Garbo) | ~2 |
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See `~/dev/PROJECT_SUMMARY.md` for detailed descriptions of each project.
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### Key active projects
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- **super-koala** (`AGENTS/`) — multi-component agent stack with LangGraph, DSPy, MCP
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- **azure-tiger** (`QKX/`) — invoice extraction → ISO 20022 payment instructions
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- **gocrwl** (`AGENTS/`) — Go web crawler with containerized deployment
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- **koala-ai-stack** (`AGENTS/`) — local AI server infrastructure management
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- **klimatkollen** (`XT/`) — Swedish municipal climate data platform
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## Knowledge base — actively use it
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A persistent brain (BM25 search + LLM-synthesised Q&A) survives across sessions,
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hosts, and harnesses. It holds 100+ hard-won entries: infra incident postmortems,
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Go pitfalls, framework gotchas, design principles, ADRs. **It is not optional
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reference material — query it actively, not just when explicitly told.**
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### When to query (treat as a reflex)
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- **Before** starting a non-trivial task — search for prior art with the symptom
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AND the system component ("how did we solve X in Y?"). 5 seconds beats 5 hours.
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- **When debugging** — search for the error string, the stack frame, the affected
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service. Past you may have already paid this tax.
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- **Before adopting** a pattern, library, framework, or model name — check if it
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was tried and rejected, or what the integration footguns are.
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- **When making architectural decisions** — search for the domain + "ADR" or
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"decision" to find prior reasoning before re-deriving it.
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- **When a recommendation feels novel** — challenge yourself: "has this been
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documented?" The brain often has it.
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### When to write
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After you discover something that **future-you would forget** and that **isn't
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recoverable from the code, git log, or PR description alone**:
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- Bugs whose root cause is non-obvious and generalisable beyond this project.
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- Framework / library / model-name quirks that bit you and would bite anyone.
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- Design principles validated under fire (e.g. "every `_get` needs a `_list`").
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- Postmortems for incidents: what broke, why, how diagnosed, what to do next time.
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DON'T write project status, sprint progress, PR summaries, or "what I did this
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session" — those rot fast and the originals are in git/gitea anyway. Brain
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entries that age well are about *why*, *how to avoid*, and *what to do when*.
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### How to access (per harness)
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| Harness | Query | Write |
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|---------|-------|-------|
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| **Claude Code, Claude Desktop** | `brain_query` (BM25), `brain_answer` (LLM-synth + sources) MCP tools | `brain_write` MCP tool |
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| **Crush, Pi, Antigravity, other MCP-capable** | same MCP server: `ingestion-brain` (via the `mcp__*_brain__*` namespace once authenticated) | same |
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| **Anything HTTP-only (curl, scripts)** | `POST https://brain-mcp.d-ma.be/query` with `{"query":"..."}` (auth via `BRAIN_MCP_TOKEN`) | `POST .../write` with `{"content":"...","filename":"..."}` |
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| **Browser / human inspection** | `https://gitea.d-ma.be/mathias/hyperguild` → `knowledge/` and `wiki/` markdown files |
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- **Scoping**: defaults to `public` collection; client projects filter to `{client}` + `public`.
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- **Routing**: brain_answer's LLM uses berget.ai as primary, iguana ollama as
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fallback. Both are configurable in the `supervisor/ingestion-deployment.yaml`
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on the koala k3s cluster; don't hardcode local-only model names into the
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berget URL (see knowledge entry on namespace mismatches).
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### Quick reflex checks
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If you find yourself about to say any of these out loud, you owe yourself a brain query first:
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- "I think the issue might be..."
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- "Let me try X and see..."
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- "I'll just write a script to..."
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- "This is probably a new bug..."
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- "Has anyone done this before?" — *yes, probably, go check.*
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## Client work rules
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When working on a project tagged with a client name:
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1. Never send code, data, or context to cloud APIs — use local models only
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2. Never reference other client projects or their data
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3. Keep all artifacts within the client's git org / directory
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4. Treat everything as confidential unless told otherwise
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## Harness-agnostic principles
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This context is designed to work with any AI coding tool:
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- Claude Code, Cursor, Aider, Open WebUI, Charmbracelet Mods/Crush
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- Pi Coding Agent, Mistral Vibe, Antigravity
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- Any tool that accepts a system prompt or reads a markdown context file
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The canonical source is always `.context/AGENT.md` (root) and `.context/PROJECT.md` (per-project).
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Derived files are committed (see *How context propagates* below) so a `git pull` on any host yields full agent context with no setup.
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## How context propagates
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Canonical sources of truth:
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- Universal: `~/dev/.context/AGENT.md` (this file)
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- Project: `<repo>/.context/PROJECT.md` (per-repo)
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Derived files (committed, regenerated by `task context:sync`):
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- `CLAUDE.md`, `AGENTS.md`, `.cursorrules`, `.aider.conventions.md`,
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`.context/system-prompt.txt`
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Workflow:
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1. Edit a canonical file. Run `task context:sync`. Commit canonical and
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derived together. Push.
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2. On any other host, `git pull` brings both. Claude Code (tree-walking)
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uses `CLAUDE.md`; Crush / Pi / Antigravity (cwd-only) use `AGENTS.md`;
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Cursor uses `.cursorrules`; Aider uses `.aider.conventions.md`.
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3. `task check` runs `context:sync` then asserts `git status --porcelain`
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is empty over the derived files (catches both modified-tracked drift
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and missing-untracked adapters). A drift fails the check with a
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message telling you to stage the regenerated files.
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Behavior rules in this file and per-project rules in `PROJECT.md` apply
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unconditionally on every host, every harness.
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## Engineering Skills
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Shared engineering skills are available in `~/dev/.skills/`. Load on demand via the index.
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See `~/dev/.skills/SKILLS_INDEX.md` for the full list with descriptions and "use when" triggers.
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Key skills:
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- **TDD**: always write tests first — load `tdd` skill
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- **Code Review**: load `code-review` skill before any review
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- **SOLID/Clean Code**: load `solid` or `clean-code` skill for design work
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- **Problem first**: load `problem-analysis` skill before coding non-trivial features
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---
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# __PROJECT_NAME__
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## Identity
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- **Name**: __PROJECT_NAME__
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- **Owner**: Mathias
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- **Client**: personal
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- **Repo**: gitea.d-ma.be/mathias/__PROJECT_NAME__
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- **Status**: active
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## Stack
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Go + ADK + LiteLLM. See `~/dev/.context/AGENT.md` for cross-project conventions.
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## Agent
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TODO: describe what this agent does, what tools it has, and what it's responsible for.
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## Observability
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Traces → Jaeger via `OTLP_ENDPOINT`. Set `ADK_SERVICE_NAME=__PROJECT_NAME__` per deployment.
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Spans emitted: `invoke_agent`, `generate_content`. Tool spans require custom callbacks.
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