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mathiasandClaude Opus 4.8 c6f8ea477b
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refactor(template): match single-harness (hyperguild) architecture (infra#178)
The template carried the old multi-tool/agentsquad-era CAD-executor scaffold.
Refresh it to the consolidated single-harness reality (hyperguild#75/#76):
Claude Code + hyperguild, session-based, brain + gitea MCP. No dispatch
automation added (out of scope — dispatch stays manual until real friction
warrants it).

Removed (multi-tool + dead generator + deployed-agent-era):
- .aider.conf.yml, .aider.conventions.md, .cursorrules
- .context/ (mcp.json, PROJECT.md, system-prompt.txt)
- agent-policy.yaml (k8s NetworkPolicy scaffold)
- scripts/context-sync.sh (generated the above from .context/PROJECT.md — now
  input-less) + its context:sync* Taskfile tasks

Added / updated:
- .mcp.json (root, hyperguild-shaped: brain + gitea, Bearer env tokens)
- CLAUDE.md / AGENTS.md / README.md → single-harness workflow (issue →
  hyperguild session → PR → report-back comment → brain capture)
- AGENT_BOUNDARIES.md kept (harness-agnostic egress/scope guidance); repointed
  its two references to the removed agent-policy.yaml

Left alone: cd.yml, Dockerfile, Taskfile (build/test), go.mod, cmd/, internal/,
pkg/, .claude/. No dangling refs to removed files (verified). Refs infra#178.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-01 22:20:37 +02:00

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Markdown

# __PROJECT_NAME__
Go agent built on [Google ADK](https://google.golang.org/adk) with a LiteLLM adapter for local model routing.
## Quick start
```bash
cp .env.example .env
# edit .env with your LITELLM_API_KEY
go mod tidy
task run
```
## Harness & workflow
Single harness: **Claude Code + hyperguild** — session-based (start a session, it works, it
stops). No push-triggered dispatch. MCP connections live in `.mcp.json`: **brain** (knowledge)
and **gitea** (issues/PRs — the audit trail); provide `BRAIN_MCP_TOKEN` and `GITEA_MCP_TOKEN`.
Work flows: **issue → hyperguild session → PR (`task check` gate) → report-back comment on the
issue → brain capture if the learning is durable**. See `AGENTS.md` / `CLAUDE.md` for the full
context, `AGENT_BOUNDARIES.md` for runtime scope.
## Observability
Set `OTLP_ENDPOINT=http://jaeger.d-ma.be:4318` to emit traces. Each invocation produces:
- `invoke_agent __PROJECT_NAME__` span
- `generate_content <model>` child span with `gen_ai.request.model` attribute
## Structure
```
cmd/__PROJECT_NAME__/ agent entrypoint
pkg/litellm/ OpenAI-compat ADK adapter + OTLP telemetry helper
```