AIToday
AI Coding AssistantsOpen-Source AIHacker NewsPublished: Aug 11, 2026, 16:01 JST5 min read

Se-harness: AI coding tool unifies instructions across Claude, Gemini, Copilot

Se-harness: AI coding tool unifies instructions across Claude, Gemini, Copilot

Key takeaway

  • Se-harness is an open-source CLI tool that generates unified instruction files for AI coding assistants, eliminating the need to maintain separate rulesets for Claude Code, Gemini, Copilot, and other agents.

  • Users define rules once in a global AGENTS.md file and per-project modules, then run seh sync to auto-generate tool-specific files, keeping them in sync.

  • Shareable harness packages allow teams to version and distribute common rules and reusable skills (called Skills) across machines.

3 Key Points

  1. What happened

    A portable, open-source tool called Se-harness (seh) generates unified instruction files (AGENTS.md) that work across multiple AI coding assistants (Claude Code, Codex, Gemini, Pi, OpenCode, GitHub Copilot). Users manage a single source of truth and seh automatically creates tool-specific files so they do not need to copy rules separately into CLAUDE.md, GEMINI.md, .github/copilot-instructions.md, and other agent-specific paths.

  2. Why it matters

    Developers and teams currently must maintain duplicate rulesets for each AI coding agent they use, creating version-drift risk and manual overhead. Se-harness eliminates this by letting users write one global ruleset (~/.seh/AGENTS.md per machine) plus one project-level index (AGENTS.md + .seh/ directory), then syncing once to regenerate all agent-specific files in lockstep. Harness packages—versioned git repos of rules, templates, and reusable skills—can be shared across teams and machines.

  3. What to watch

    The tool is available now via curl install (https://raw.githubusercontent.com/manuuuel/seh/main/scripts/install.sh). A sandboxed demo is available for try-before-install. Supported technologies include JavaScript, TypeScript, Python, Go, C, Rust, and Java; users must select at least one technology during project setup.

In Depth

Read the full story

Se-harness is a CLI tool that generates unified instruction files for AI coding assistants. Installation is one curl command: curl -fsSL https://raw.githubusercontent.com/manuuuel/seh/main/scripts/install.sh | sh. After that, a user runs seh init --global --agents claude,codex --yes (once per machine) to set up a unified global ruleset at ~/.seh/AGENTS.md with optional symlinks to agent-specific directories (like ~/.claude/CLAUDE.md). Then, per project, seh init --tech typescript --yes detects the project's technology stack and scaffolds AGENTS.md (a short project index) plus .seh/ subdirectories containing project.md (mission and constraints), domain/ modules (architecture and glossary), and stack/ modules (per-technology best practices). Users edit these .seh/ sources and run seh sync to regenerate all agent-specific files in lockstep.

The resolution order is: harness package (if active) → ~/.seh/ (global) → seh bundled core. Global rules apply everywhere; project layers extend them but never override. Generated files (CLAUDE.md, AGENTS.md, GEMINI.md, .github/copilot-instructions.md) must not be hand-edited—seh sync regenerates them from sources. The global AGENTS.md is one self-contained file, led by a forced Craftsmanship principle (small, sharp, elegant code). The project AGENTS.md is the canonical file, a short preamble plus a progressive index linking focused modules.

Harness packages are plain git directories versioned and shared across teams. A harness contains harness.json (package metadata including name, version, and modelTag), a CHANGELOG.md (human-authored reasoning), global/ (global ruleset and agent symlink config), templates/stack/ and templates/project/ (scaffolding templates), projects/ (per-repo overlays), and skills/ (reusable capabilities). Skills can be vendored (committed to the package repo) or referenced (fetched at install time). Each skill has a routing mode: always (invoke every response), when (invoke on matching condition), or optional (agent decides). The routing is stored in harness.json and rendered into a ## Skills section in both the project AGENTS.md and global ~/.seh/AGENTS.md so agents always know which skills to invoke and when.

Supported agents are Claude Code, Codex, Pi, Gemini, OpenCode, GitHub Copilot, and a cross-agent interoperability path (agents). Supported technologies for per-technology modules are JavaScript, TypeScript, Python, Go, C, Rust, and Java; users must select at least one. Key commands include seh init (project setup), seh sync (regenerate from sources), seh check (detect drift), seh link (manage symlinks), seh package init (scaffold a new harness), seh package use (activate a package), seh package install (install artifacts onto the host), seh skills add (add a skill from GitHub), and seh skills update (re-fetch referenced skills). The tool is available for immediate use with a sandboxed demo for experimentation.

Context & Analysis

Se-harness addresses a fragmentation problem in AI-assisted development: as teams adopt multiple coding assistants, they accumulate duplicate instruction files. Each tool—Claude Code, GitHub Copilot, Gemini CLI, and others—reads from different paths (CLAUDE.md, .github/copilot-instructions.md, GEMINI.md) and maintains no shared awareness of one another, forcing developers to manually sync rules across seven or more files whenever a guideline changes. Se-harness solves this by introducing a layered resolution order: packages (optional, shareable git repos of versioned rules) take precedence over global rules (~/.seh/ on the user's machine), which override bundled core content. A project index (AGENTS.md + .seh/ modules) extends global rules but never contradicts them; running seh sync regenerates all tool-specific files atomically from these sources. This model also generalizes to Skills—reusable capability modules that route to each agent (always invoke, invoke-only-on-condition, or optional), eliminating duplication of prompts like debugging procedures or code-formatting checklists across teams.

The tool's design emphasizes avoiding vendor lock-in and keeping git as the sole external versioning system. Harness packages are plain directories, not proprietary containers; symlinks are used purely as pointers, and generated files are gitignored (not committed), so drift detection is straightforward. The new machine workflow—git clone a harness, seh package use it, seh package install --all—mirrors familiar git workflows, reducing onboarding friction for technical teams. By allowing teams to commit .seh/project.md and stack modules while ignoring generated CLAUDE.md and AGENTS.md files, the system keeps a single source of truth in version control without duplicating intent.

FAQ

What AI coding tools does Se-harness support?
Se-harness generates files for Claude Code, Codex, Gemini, Pi, OpenCode, GitHub Copilot, and a cross-agent interoperability path (agents). It reads AGENTS.md, CLAUDE.md, GEMINI.md, .github/copilot-instructions.md, and other agent-specific paths automatically.
What programming languages can I set up rules for?
Supported technologies are JavaScript, TypeScript, Python, Go, C, Rust, and Java. At least one technology must be selected during project setup; there is no generic fallback.
How do I share rules with my team?
Create a harness package—a versioned git repo containing your global rules (global/AGENTS.md), per-technology stack modules (templates/stack/), project scaffolds (templates/project/), and reusable skills (skills/)—then teammates clone it and run seh package use <path> followed by seh package install --all.
What is a Skill in Se-harness?
A Skill is a reusable SKILL.md-based capability package (e.g. a brainstorming guide or systematic debugging procedure) stored in a harness package and distributed to every agent that supports a skill directory. Skills can be marked always-invoke, invoke-when-condition-matches, or optional via routing flags.

Get the latest AI Coding Assistants news every morning

AI-summarized, only the topics you pick — one digest a day via Email, Slack, or Discord.

Free · takes 30 seconds · unsubscribe anytime

Ask AI

Ask AI anything about this article. Q&As are published on this page for other readers too.

Related Articles

Next articleCatcher cites component shortages, R&D costs for 2Q26 revenue drop

The AI news that matters, in one minute each morning.

Sign up free