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Agentic SDLC

Agents propose, a human merges, CI decides.

A GitHub template that runs your software delivery lifecycle with autonomous agents — and keeps every one of them behind a quality gauntlet, a two-model review, and a merge button only you click.

  issue opened  ──▶  steward triages  ──▶  PR opened  ──▶  two reviews
                                                                │
        ┌───────────────────────────────────────────────────────┘
        ▼
  23-gate gauntlet  ──▶  YOU merge  ──▶  filing agent verifies the fix landed

Why this one, in ten minutes: THE-SEVEN-IDEAS.md — the doctrine everything here is built on, each idea enforced by a test or a permission, not a promise. Proof it works: agentic-sdlc-demo, a real product adopted from this template with the adoption logged step by step.

Four doors in

You are… Do this
Starting a new repo Click Use this template, then run tools/adopt.sh — one resumable command that walks the whole adoption and never acts without your yes. Target: first merged agent loop in ~30 minutes.
Bringing an existing repo From a template clone: tools/upgrade.sh --install /path/to/your-repo. Copies the harness in, never overwrites your files, and stamps a manifest so future template releases are a computable three-way merge.
Sending your agent Hand Claude Code / Codex this repo and say "read ONBOARDING.md and adopt this." profiles/ has ready answer files — a platform team can publish one internal profile so every team adopts with a single command.
Just looking Open in a devcontainer/Codespace: tools/demo-local.sh runs the ~650-test suite, the adoption map, and a dry-run agent command — three minutes, offline, zero credentials.

Not ready to hand over write access? Set mode: observe in .agents/config.yml: the whole fleet runs report-only for a trial week — reviews and reports still post, but pushing is mechanically impossible, because observe runs simply never receive a write token.

What you get

  • An event-driven steward that triages every new issue and answers mentions — it writes fixes as pull requests; it never merges them.
  • Two reviews from different model families on every PR (a second draw from the same distribution shares the same blind spots), plus a referee that settles their genuine disagreements against the code. Verdicts are advice; you overrule at merge.
  • A 23-gate gauntlet — tests, coverage, mutation, architecture, migrations, e2e/a11y, secrets, bundle size, and the harness's own guards — with ratcheted floors calibrated to your codebase, never someone else's. Floors ship as loud unset sentinels until tools/measure-floors.sh measures your baseline; from then on they only move up.
  • Eleven scheduled agents (health, quality, audit, chief-of-staff, challenger, docs freshness, backlog groomer, test gap, dependency steward, code hygiene, release drafter) — all shipped off, enabled one at a time when you're ready.
  • A second brain (docs/knowledge/): agents propose distilled lessons as cards, a human merges them, and every future session reads the 80-line index first. History (ledgers), knowledge (cards), and steering (agent-modes.md) are three memory tiers separated by write permission — the template's signature idea.
  • ~650 tests that test the machine itself: 129 incident-derived lessons pinned so they cannot be lost quietly, plus executable guards on the agents' own plumbing.
  • Provider-neutral by construction: models are addressed by role (judge / execute / challenge), vendors appear in exactly one adapter directory, and a guard fails the build if a vendor name leaks anywhere else. Works with a flat agent-CLI subscription; behind corporate proxies; on GitHub Enterprise.
  • Everything degrades visibly, never silently. A missing optional credential announces itself and the run continues; absence of a heartbeat is the alert; a dead agent and a healthy agent never look the same.

What you need

  • A GitHub repository and one agent-CLI subscription (Claude Code today; codex/gemini adapters ship as documented stubs to finish). Normal cost: your existing flat monthly plan. One optional API key for a different model family unlocks the adversarial second review — docs/runbooks/credentials-and-cost.md has the honest numbers.
  • Any language. The agent process is stack-agnostic from day one; only the measured gates ship as reference implementations (Java + React) you swap for your own tools — docs/runbooks/porting-to-your-stack.md has the exact table.

Turning on the routines

The eleven scheduled agents ship disabled — nobody should meet this system as eleven crons and an alert firehose. Dry-run each one first (tools/run-agent.sh <agent> --dry-run prints the exact command and invokes nothing), then flip its enabled: true in .agents/config.yml, one at a time. Note: GitHub auto-disables schedules after ~60 days of repo inactivity — if ledgers go stale, re-enable from the Actions tab. Details: docs/runbooks/agent-routines.md.

Steering, and your ten minutes a week

docs/runbooks/agent-modes.md is the only channel agents obey — it lives on the protected branch, so steering is always a reviewed pull request. The one fleet-wide switch (mode: active | observe) lives in .agents/config.yml. Once running, budget ~10 minutes a week: read review summaries, click merge, glance at the daily brief. The full operator walkthrough is docs/runbooks/agent-operator-guide.md; lost at any point, tools/status.sh prints the map with your position on it.

When to use something else

Solo prototyping doesn't need a gauntlet. If you want unattended merges, this is deliberately the wrong tool — the human merge is the design. If you only want spec discipline or dependency bumps, a spec tool or Renovate alone is lighter. This template is for teams who want autonomous agents doing real work and a mechanical reason to trust every change that lands.

Help, upgrading, license

Something behaving oddly? docs/runbooks/troubleshooting.md maps symptoms to causes. Upgrading a fork: tools/upgrade.sh plan/apply <new-template> computes it from your adoption manifest. Full capability tour: DEMO.md. License: MIT. Contributing: CONTRIBUTING.md — lessons learned in forks accumulate upstream.

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Provider-agnostic GitHub template for an autonomous-agent SDLC - agents propose, CI decides, a human merges

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