Loop/ralphAutomationhigh riskadvancedsafety C · 60 · open-endedcommunity

Execute SkillFoundry agent pipeline

Run all PRD-driven agents across database, backend, and frontend layers until the full development lifecycle completes.

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→ Claude
# SkillFoundry Framework — Agent Instructions for OpenAI Codex Version 5.22.1 | Hexa-Platform: Claude Code · Cursor · Copilot · Codex · Gemini · Grok Build | 107 Skills | 20 MCP Tool Agents --- ## What This Is SkillFoundry (Agents & Skills) is a production-ready AI development framework with 60 specialized agents covering the full software development lifecycle. This file provides always-on context for OpenAI Codex CLI. ## Philosophy - Cold-blooded logic over flattery — Honest, structured, production-ready evaluations only - ONLY REAL LOGIC — No placeholders, TODOs, mocks, or stubs. Every feature works end-to-end - Three-Layer Completeness — Every feature verified across DATABASE → BACKEND → FRONTEND - PRD-First Development — Non-trivial features start with a Product Requirements Document - Implement Test Iterate — Every feature tested before considered done ## How to Use Skills Each SkillFoundry agent is available as a Codex Skill in .agents/skills/ . Invoke explicitly or let Codex auto-select based on your prompt. Explicit invocation: $go # Execute all PRDs from genesis/ $coder
claude-code · codex · cursor

Source: samibs ↗graded C · 60/100 — how grades work →

More automation loops

Run an autonomous dev team across GitHub repos (looper)

Loop/ralphcommunityB

Runs Claude Code/Codex as an autonomous multi-role dev team — planner → reviewer ↔ fixer → worker — across all of a user's GitHub repos, entirely driven by issue labels. Each loop runs in its own git worktree so multiple repos/issues proceed in parallel without collisions.

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→ Claude
Register a repo with looper, then label an issue `looper:plan` and assign it to yourself. The planner reads the issue, explores the repo, drafts a spec, critiques and revises it, and opens a spec PR labeled `looper:spec-reviewing`. A reviewer re-reads the PR on every commit and posts inline review threads; a fixer pulls those threads, addresses them in its own worktree, and pushes, ping-ponging with the reviewer until every thread is resolved. Once labeled `looper:spec-ready`, a worker implements the spec, runs checks, and iterates on its own output until checks pass and the PR is ready for human review and merge. Every phase transition is gated on a GitHub label via `looperd`, so a human can pause or take over at any boundary.
automationmedium riskclaude-code
nexu-io/looper ↗

Loop cost guardrails pattern

Loop/ralphcommunityC

The four-part cost guardrail every agent loop should ship with: a hard iteration cap, a stop-after-N-turns clause in the goal text, a budget limit on SDK loops, and a /cost check inside the loop body.

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→ Claude
Every loop gets: `MAX_ITER=20` hard cap, "or stop after N turns" in the /goal text, `max_budget_usd` on SDK loops, and a /cost check in the loop body.
automationmedium riskclaude-code
Developers Digest ↗

claude-progress.txt harness pattern (Anthropic)

Loop/ralph★ AnthropicA

Anthropic's first-party file-as-memory harness for long-running agents: every fresh-context session recovers state from a progress file and the git log, does one unit of work, updates the file, commits, and exits.

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→ Claude
Long-running agent harness: each fresh-context session starts by reading `claude-progress.txt` + git log to recover state, does one unit of work, updates the progress file, commits, exits. Initializer session sets up the file; coder sessions loop. Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
automationmedium riskclaude-code
Anthropic Engineering ↗