templates/skills/cli/opencode/SKILL.md
OpenCode (open-source terminal coding agent by SST). Reads AGENTS.md natively. Rulebook generates opencode.json plus .opencode/commands, .opencode/agents, .opencode/skills.
npx skillsauth add hivellm/rulebook OpencodeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Tool: OpenCode — open-source terminal coding agent.
When Rulebook detects OpenCode (opencode.json / .opencode/ / opencode
binary on PATH), it generates:
opencode.json with $schema, mcp.rulebook, and instructions.
Existing user keys (model, theme, permission, custom agents) are preserved..opencode/commands/<name>.md for every user-invocable Rulebook slash
command, with OpenCode-shaped frontmatter (description)..opencode/agents/<role>.md for every Rulebook role agent
(researcher, implementer, tester, code-reviewer, architect, docs-writer,
security-reviewer, build-engineer, team-lead, etc.) with model tier
mapped (haiku → claude-haiku-4-5, sonnet → claude-sonnet-4-6,
opus → claude-opus-4-7) and a synthesized permission block..opencode/skills/<normalized-name>/SKILL.md for every Rulebook dev
skill, name-normalized to [a-z0-9](-[a-z0-9])* (≤64 chars) and
description bounded to ≤1024 chars..opencode/.rulebook-managed.json — sidecar listing managed keys so
rulebook update knows what to refresh vs. preserve.Files carrying the <!-- RULEBOOK:START --> marker are managed and
refreshed on every rulebook update. Files without the marker are
treated as user-owned and left untouched.
OpenCode follows the same Rulebook discipline as Claude Code:
--no-verify.mcp.rulebook in opencode.json exposes rulebook_task_*,
rulebook_memory_*, rulebook_decision_*, rulebook_knowledge_*,
rulebook_learn_*. Prefer MCP tools over shell commands when both
exist.
AGENTS.md and .rulebook/specs/*.md./rulebook-task-create, /rulebook-task-list, /handoff.@researcher, @implementer, @tester).rulebook_memory_save, rulebook_knowledge_add).research
Author a rulebook task spec interactively — research, draft, ask the user clarifying questions, confirm, then create the tasks in rulebook ready for /rulebook-driver. Use when the user wants to plan/spec a feature before implementing.
development
Behavioral guidelines to reduce common LLM coding mistakes — overcomplication, sloppy refactors, hidden assumptions, weak goals. Use when writing, reviewing, or refactoring code. Auto-applies; invoke explicitly via /karpathy-guidelines or 'follow karpathy discipline'.
data-ai
Autonomous AI agent loop for iterative task implementation (@hivehub/rulebook ralph)
data-ai
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