hyperguild/AGENTS.md

Agent context — Mathias workspace

Who I am

I'm Mathias, a digital product manager and technology consultant based in Sweden. I build software, research emerging tech, and deliver consulting engagements for clients under NDA. I work across AI/ML, financial automation, web applications, and climate/sustainability tech.

How I work with agents

• I think like a product manager — I care about why before how
• I want agents to be opinionated and push back, not just execute blindly
• I prefer concise responses; skip ceremony and get to the point
• When I say "build this", I mean production-quality with tests, not a demo
• Ask me before making irreversible changes or adding heavy dependencies
• I work with confidential client data — never send it to cloud APIs unless I explicitly say it's OK

Behavior rules

These rules apply to every task across every project, regardless of harness.

0. Pre-task ritual — before ANY implementation (non-negotiable). Run this before writing a single line:

   • Query the brain (brain_query) for the domain + symptom. If the result changes your approach, surface it before acting. 5 seconds beats 5 hours.
   • Load the relevant skill — see trigger table in Engineering Skills below.
   • Write the failing test first. Name the test before the function. If the target is untestable (e.g. main() wiring), extract the logic into a testable function first. No implementation without a red test.
   • State the observable success criterion — what specific behavior, output, or passing test proves this is done?

   TDD is non-negotiable. "Tests pass" is not proof of correctness — only proof the tests ran. Write tests that would catch the bug before writing code that fixes it.

1. No assumptions. Don't hide confusion — surface it. Surface tradeoffs explicitly. Think before coding; if the problem is unclear, ask or state assumptions before acting.

2. Minimum viable code. Solve with the smallest change that works. Nothing speculative, no "while we're here" cleanups, no premature abstractions. Simplicity first.

3. Surgical changes. Touch only what the task requires. Leave unrelated code, files, and formatting alone. Diffs should be small and reviewable.

4. Goal-driven execution. Define clear success criteria up front for every task. Loop — implement, verify, refine — until those criteria are met. Don't claim completion without evidence (tests pass, command output, observed behavior).

5. Trunk-Based Development — commit directly to main. Every commit is one logical change (one tool, one fix, one test) with passing tests. Main is always deployable. Never create long-lived feature branches.

   Exception — parallel agents on same repo: If another agent is known to be actively working on the same repo simultaneously, create a short-lived branch (agent/<description>), finish the task, and merge to main within the same session. Do not leave agent branches open between sessions.

   Exception — external contributor or client four-eyes requirement: Use PR flow only when a human reviewer outside the project is required. Document the reason in PROJECT.md.

6. Close the loop — every substantive task ends with the same ritual. Shipping the code is not the end of the task; capturing it is. Run this unprompted:

   • Tag + bump SemVer on the change (annotated tag; minor for a feature or new/changed ADR, patch for a fix; docs in the same commit). Check the repo's actual last tag — stated versions in docs drift stale.
   • Push main and the tag (CI is the gate).
   • Persist generalizable learnings to the brain (brain_write, wing/hall) — the reusable patterns and the footguns that would bite anyone again, never project status. See Knowledge base — when to write below.
   • File discovered-but-deferred work as tracker issues on the project's own repo — token-budget gaps, recorded ADR limitations, v2 follow-ups. Don't let "out of scope, recorded" rot in a commit message; make it a ticket with a source pointer.
   • Surface the brain entries and issue numbers in the closing summary so the trail is auditable.

Default stack

Layer	Default	Fallback	Last resort
Language	Go	Python	TypeScript, Java, C
UI	HTMX + Templ	Server-rendered HTML	React (only if SPA is justified)
Build	Task (taskfile.dev)	Make	—
Containers	Docker Compose (dev), k3s (prod)	—	—
DB	PostgreSQL + sqlc	SQLite	—
Search	pgvector (vector), BM25	Qdrant (when >1M vectors or hybrid retrieval)	—
Logging	slog (structured)	stdlib logging w/ structured extra= (or structlog)	—
Testing	Table-driven, testify	—	—
Agents (Go)	google.golang.org/adk + pkg/litellm adapter	—	—

Exploratory: Rust, Zig — I'll tell you when I want these.

Code conventions

• Go style: golines, gofumpt, golangci-lint
• Python style (fallback language): ruff (format+lint, one tool), mypy --strict (non-negotiable, matches Go's static typing discipline), pytest + pytest-cov (table-driven via @pytest.mark.parametrize), uv (venv+deps+lock, one tool), pydantic-settings (typed env-var config — same principle as Go's typed structs), src-layout + pyproject.toml only (no setup.py)
• Errors: fmt.Errorf("operation: %w", err) — never naked, never log-and-return. Python: raise X from e (exception chaining, same principle) — never bare except, never silent pass
• Naming: stdlib conventions, no stuttering
• Architecture: prefer stdlib over frameworks, constructor injection, env-var config parsed into typed structs
• Git: conventional commits (feat:, fix:, chore:), commit directly to main, one logical change per commit, CI is the quality gate
• Never: long-lived feature branches, PRs for solo work, direct push without passing task check locally first
• Security: no secrets in code, govulncheck before adding deps, SOPS for encrypted config
• 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

Secret handling (every harness, every command)

Tool output is persisted: terminal → ~/.claude/projects transcripts → claudewatcher → brain/wiki → gitea history. A secret printed once is searchable forever, and clearing it means rotating the key. So:

1. Never print, echo, log, or transform a secret to inspect it. No base64/xxd/cat of a key, and never pipe a secret through a transform to defeat op run's output masking (it masks raw values; base64 hides them from the mask — that exact trick leaked a key on 2026-06-11).
2. Secrets stay in the subprocess. Reference them only as env vars consumed inside op run --env-file ~/.op-env -- <cmd>. Never place a literal secret in a command's argv (it lands in the tool call and the transcript).
3. Existence check without revealing the value: [ -n "$X" ] && echo set — never ${X:-...} (returns the value when set) and never echo a substring of it.
4. Cross-host secrets: run the secret-consuming command on the host that has the secret; do not forward a raw key over ssh argv/stdout.
5. If a secret does leak into output, say so immediately and flag it for rotation — don't bury it.

Infrastructure

Three machines on Tailscale:

Machine	Role	Key specs
koala	GPU inference, heavy compute	RTX 5070, runs k3s + llama-swap + shared postgres18/pgvector
iguana	Services, builds	M2 Ultra Mac
flamingo	Daily driver, edge	Mac mini, ~/dev is here

• Model routing: LiteLLM in front of llama-swap (local) + cloud APIs (when permitted)
• Orchestration: k3s cluster across all three machines
• Networking: Tailscale mesh

Project landscape

All development repos live at ~/dev/ (softlink from ~/Documents/local-dev/).

Organized in thematic folders:

Folder	Focus	Count
GO/	Go web frameworks, API integrations, learning projects	~10
AI/	ML research, AI frameworks (FinRL, DSPy, crawl4ai)	~6
AGENTS/	Autonomous agents, coding agents, MCP servers, infra	~15
QKX/	Invoice processing, financial automation, payment systems	~13
XT/	Climate data, sustainability (Klimatkollen, Garbo)	~2

See ~/dev/PROJECT_SUMMARY.md for detailed descriptions of each project.

Key active projects

• super-koala (AGENTS/) — multi-component agent stack with LangGraph, DSPy, MCP
• azure-tiger (QKX/) — invoice extraction → ISO 20022 payment instructions
• gocrwl (AGENTS/) — Go web crawler with containerized deployment
• koala-ai-stack (AGENTS/) — local AI server infrastructure management
• klimatkollen (XT/) — Swedish municipal climate data platform

Knowledge base — actively use it

A persistent brain (BM25 search + LLM-synthesised Q&A) survives across sessions, hosts, and harnesses. It holds 100+ hard-won entries: infra incident postmortems, Go pitfalls, framework gotchas, design principles, ADRs. It is not optional reference material — query it actively, not just when explicitly told.

When to query (treat as a reflex)

• Before starting a non-trivial task — search for prior art with the symptom AND the system component ("how did we solve X in Y?"). 5 seconds beats 5 hours.
• When debugging — search for the error string, the stack frame, the affected service. Past you may have already paid this tax.
• Before adopting a pattern, library, framework, or model name — check if it was tried and rejected, or what the integration footguns are.
• When making architectural decisions — search for the domain + "ADR" or "decision" to find prior reasoning before re-deriving it.
• When a recommendation feels novel — challenge yourself: "has this been documented?" The brain often has it.

When to write

After you discover something that future-you would forget and that isn't recoverable from the code, git log, or PR description alone:

• Bugs whose root cause is non-obvious and generalisable beyond this project.
• Framework / library / model-name quirks that bit you and would bite anyone.
• Design principles validated under fire (e.g. "every _get needs a _list").
• Postmortems for incidents: what broke, why, how diagnosed, what to do next time.

DON'T write project status, sprint progress, PR summaries, or "what I did this session" — those rot fast and the originals are in git/gitea anyway. Brain entries that age well are about why, how to avoid, and what to do when.

How to access (per harness)

Harness	Query	Write
Claude Code, Claude Desktop	brain_query (BM25), brain_answer (LLM-synth + sources) MCP tools	brain_write MCP tool
Crush, Pi, Antigravity, other MCP-capable	same MCP server: ingestion-brain (via the mcp__*_brain__* namespace once authenticated)	same
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":"..."}
Browser / human inspection	https://git.d-ma.be/mathias/hyperguild → knowledge/ and wiki/ markdown files

• Scoping: defaults to public collection; client projects filter to {client} + public.
• Routing: brain_answer's LLM uses berget.ai as primary, iguana ollama as fallback. Both are configurable in the supervisor/ingestion-deployment.yaml on the koala k3s cluster; don't hardcode local-only model names into the berget URL (see knowledge entry on namespace mismatches).

Quick reflex checks

If you find yourself about to say any of these out loud, you owe yourself a brain query first:

• "I think the issue might be..."
• "Let me try X and see..."
• "I'll just write a script to..."
• "This is probably a new bug..."
• "Has anyone done this before?" — yes, probably, go check.

Client work rules

When working on a project tagged with a client name:

1. Never send code, data, or context to cloud APIs — use local models only
2. Never reference other client projects or their data
3. Keep all artifacts within the client's git org / directory
4. Treat everything as confidential unless told otherwise

Harness-agnostic principles

This context is designed to work with any AI coding tool:

• Claude Code, Cursor, Aider, Open WebUI, Charmbracelet Mods/Crush
• Pi Coding Agent, Mistral Vibe, Antigravity
• Any tool that accepts a system prompt or reads a markdown context file

The canonical source is always .context/AGENT.md (root) and .context/PROJECT.md (per-project). Derived files are committed (see How context propagates below) so a git pull on any host yields full agent context with no setup.

How context propagates

Canonical sources of truth:

• Universal: ~/dev/.context/AGENT.md (this file)
• Project: <repo>/.context/PROJECT.md (per-repo)

Derived files (committed, regenerated by task context:sync):

• CLAUDE.md, AGENTS.md, .cursorrules, .aider.conventions.md, .context/system-prompt.txt

Workflow:

1. Edit a canonical file. Run task context:sync. Commit canonical and derived together. Push.
2. On any other host, git pull brings both. Claude Code (tree-walking) uses CLAUDE.md; Crush / Pi / Antigravity (cwd-only) use AGENTS.md; Cursor uses .cursorrules; Aider uses .aider.conventions.md.
3. task check runs context:sync then asserts git status --porcelain is empty over the derived files (catches both modified-tracked drift and missing-untracked adapters). A drift fails the check with a message telling you to stage the regenerated files.

Behavior rules in this file and per-project rules in PROJECT.md apply unconditionally on every host, every harness.

Engineering Skills

Shared engineering skills live in the mathias/skills repo (git.d-ma.be/mathias/skills). Clone it to ~/dev/skills/ and run SKILLS_CHECKOUT_DIR="$PWD" bash install.sh there to wire every skill into your harnesses (Claude Code, Crush, Antigravity, Mistral Vibe) as native, on-demand skills. (Use install.sh, not task install — the latter is currently broken, skills#7.) Load at task start — not "on demand" but on schedule, before writing code. Browse ~/dev/skills/SKILLS_INDEX.md for the full list.

Skill trigger table — load before starting, not after getting stuck:

Task type	Load
Any feature or bug fix	tdd
Refactor or design	clean-code or solid
Debug	problem-analysis
Review code or PRs	code-review
Frame a problem before coding	problem-analysis

---

Project context

Identity

• Name: supervisor
• Owner: Mathias
• Client: personal
• Repo:
• Status: active

Stack

• Primary language: Go
• UI layer: HTMX + Templ (when applicable)
• Fallback languages: Python, TypeScript (justify in PR if used)
• Build: Task (taskfile.dev), not Make
• Containers: Docker (compose for dev, k3s for deploy)
• Target infra: koala (GPU workloads), iguana (services), flamingo (edge)

Conventions

Code style

• Go: follow golines, gofumpt, golangci-lint with project config
• Tests: table-driven, in _test.go next to source, testify for assertions
• Errors: wrap with fmt.Errorf("operation: %w", err), no naked returns
• Naming: stdlib conventions, no stuttering (http.Client not http.HTTPClient)

Architecture preferences

• Prefer standard library over frameworks (net/http over gin/echo)
• Dependency injection via constructor functions, not containers
• Configuration via environment variables, parsed at startup into a typed struct
• Structured logging via slog

Git

• Conventional commits: feat:, fix:, chore:, docs:, refactor:
• Branch naming: feat/short-description, fix/short-description
• PRs: one concern per PR, description explains why not what

Security

• No secrets in code, ever — use env vars or SOPS-encrypted files
• Client data never leaves local network unless explicitly cleared
• Dependencies: audit with govulncheck before adding

MCP endpoints

Two MCP servers are live, both reachable over Tailscale and via HTTPS domain:

• brain at https://brain-mcp.d-ma.be/mcp (NodePort koala:30330) — brain_query, brain_write, brain_ingest, brain_ingest_raw, brain_answer, brain_classify, session_log. Hosted by the ingestion service. Auth: Dex JWT (claude.ai OAuth) or static BRAIN_MCP_TOKEN.
• routing at http://koala:30310/mcp — Mode 2 routing pod. Advertises review, debug, retrospective, trainer; per-call routes to local model or Claude based on brain /pass-rate. Bearer auth via ROUTING_MCP_TOKEN (opt-in). Only mode client-local registers this endpoint.

The supervisor MCP (koala:30320) was retired in Plan 7 (2026-05-12). Its skill workers (tdd, spec) are now SKILL.md files; routed skills moved to the routing pod; brain tools moved to the brain MCP.

The brain HTTP REST API (/query, /write, /ingest, /ingest-raw, /ingest-path, /backfill-refs, /pass-rate) remains available on port 3300 for shell scripts and non-MCP clients.

brain_answer(query) performs BM25 retrieval + LLM synthesis (berget.ai gemma4:31b → iguana fallback). brain_classify(text) infers doc type, title, and tags. Both require BRAIN_LLM_PRIMARY_URL to be set in the ingestion pod.

Agent instructions

When acting as a coding agent on this project:

1. Read this file and all SKILL.md files in .skills/ before starting work
2. Run task check before committing (lint + test + vet)
3. If unsure about a convention, check DECISIONS.md or ask
4. Never modify files outside the project root without explicit permission
5. When adding a dependency, explain why in the commit message
6. For client projects: never send code or context to cloud APIs — use local models via LiteLLM