内容
# sample-oh-my-aidlcops
**Make AIDLC reliable.** OMA is a Claude Code · Kiro plugin marketplace that turns
the [AIDLC methodology](https://devfloor9.github.io/engineering-playbook/docs/aidlc/methodology)'s
two reliability axes — **Ontology Engineering** (correctness) and **Harness
Engineering** (safety) — into installable plugins, with **AIDLC Workflows** as the
process spine and **AgenticOps** closing the feedback loop back into the ontology.
[한국어 README](./README.ko.md) · [Documentation](./docs/) · [Plugins](./plugins/) · [Steering](./steering/) · [Releases](https://aws-samples.github.io/sample-oh-my-aidlcops/releases) · [References](./REFERENCES.md)
---
## The easy button for Ontology + Harness Engineering
OMA's goal is a **one-install easy button**: add the marketplace, install the
plugins, and the AIDLC methodology's two reliability axes — Ontology Engineering
and Harness Engineering — are active in Claude Code or Kiro without hand-rolling
any schemas, policies, or hooks. You approve at checkpoints; agents do the rest.
```text
/plugin marketplace add https://github.com/aws-samples/sample-oh-my-aidlcops
/plugin install ai-infra@oh-my-aidlcops agenticops@oh-my-aidlcops aidlc@oh-my-aidlcops modernization@oh-my-aidlcops
# → typed ontology + harness DSL + AWS hosted MCP wiring, ready to use
```
### Where this is going — an enterprise operations open toolset
OMA is being built into an **open toolset for enterprise operations automation**.
The trajectory:
1. **Today** — Ontology + Harness Engineering as installable plugins, AWS Hosted
MCP (awslabs/mcp) as the default runtime data layer, AgenticOps closing the
feedback loop.
2. **Next** — deeper AWS Hosted MCP coverage plus first-class **DevOps agent**
and **Security agent** integrations, so deploy, observability, and security
review run as governed agents inside the same approval model.
3. **The promise** — install a few plugins and get enterprise-grade operations
automation that is auditable, policy-gated, and harness-constrained by
default — not a bespoke platform you assemble yourself.
## What's new in v0.5
Release `v0.5.0-preview.1` grows the harness and grounding surfaces on top
of the v0.4 dual-axis foundation:
- **Secret-protection baseline in all four plugins** — three cross-cutting
deny rules (secret-file write / edit / shell-redirect) now compile into
every plugin's `PreToolUse` hook, so an agenticops- or aidlc-only install
gets hard enforcement without `ai-infra`.
- **Runtime hooks shipped with the plugins** — SessionStart injects the
project's ontology status into context on `/plugin install` alone; the
`aidlc` Stop hook (`stop-gate.sh`) enforces AIDLC phase gates at turn-end;
an agenticops `PostToolUse` hook audits every state-changing tool call to
`.omao/audit/tool-events.jsonl`.
- **Permission templates** — `templates/permissions/{common,sandbox,staging,prod}.yaml`
resolve per-environment deny rules into Claude Code `settings.json` and
Kiro agent profiles at install time, with a project-level
`.omao/permissions.yaml` overlay, an `oma permissions show|path`
subcommand, drift-detection hooks, and a 13th doctor probe.
- **Loop Engineering** — an optional `loops:` section in the harness DSL
describes autonomous feedback cycles (discovery → triage → modify → verify)
on top of the per-call harness. See the
[Loop Engineering docs](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/loop-engineering).
- **Knowledge Wiki (design accepted, build pending)** — `adr-0001` records a
Graphify-backed pre-generation grounding layer; the `aidlc` plugin ships an
`ontology-wiki` skill that queries existing entity definitions, enums, and
ADR rationale *before* an agent writes a new artifact. See the
[Knowledge Wiki docs](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/knowledge-wiki).
- **Five new AgenticOps skills** — `anomaly-detection`, `root-cause-analysis`,
`automated-remediation`, `slo-management`, `predictive-scaling`, wired
together by the `steering/workflows/incident-pipeline.md` workflow.
- **Architecture page** — the
[two-layer install model](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/architecture)
(user-global capability, project-local policy) with a single-edit-point map.
Full details in [CHANGELOG.md](./CHANGELOG.md) and on the
[Releases page](https://aws-samples.github.io/sample-oh-my-aidlcops/releases).
## What's new in v0.4
Release `v0.4.0-preview.1` repositions the project around the reliability
dual-axis and renames plugins for clarity, building on the enterprise ontology +
harness surface introduced in v0.3:
- **Reliability dual-axis docs** — dedicated Ontology Engineering (correctness)
and Harness Engineering (safety) pages mapping each axis to its OMA
implementation, with honest partial/roadmap status for harness patterns.
- **Plugin renames** — `agentic-platform → ai-infra`, and
`aidlc-inception` + `aidlc-construction` merged into a single `aidlc` plugin.
- **8 ontology entities** — `Agent`, `Skill`, `Deployment`, `Incident`,
`Budget`, `Risk`, plus `Spec` and `ADR` (Draft 2020-12) closing the
Phase-1 → Construction traceability chain.
- **Harness DSL v2** — optional `workflows` (DAG), `telemetry` (OpenTelemetry),
`policies` (declarative runtime deny rules), `metadata.labels`. `version: 1`
files compile unchanged.
- **Pure-CC policy enforcement** — a plugin's `policies` block compiles into a
`PreToolUse` hook (`hooks/enforce.py` + `hooks/harness-rules.json`) that
blocks matching tool calls before they run. Pure Claude Code, no external
policy engine, no `opa` binary.
- **Enterprise gates** — `oma doctor --enterprise` (8 probes),
`oma compile --strict-enterprise`, `oma validate <entity.yaml>`, and JSON-L
audit events via `python -m tools.oma_audit.append`.
Full details in [CHANGELOG.md](./CHANGELOG.md) and on the
[Releases page](https://aws-samples.github.io/sample-oh-my-aidlcops/releases).
## What is OMA?
`oh-my-aidlcops` (OMA) is the sibling project of
[oh-my-claudecode](https://github.com/Yeachan-Heo/oh-my-claudecode) (OMC).
Where OMC orchestrates generic Claude Code workflows, OMA specializes in making
the **AIDLC loop** — Inception → Construction → Operations — *reliable enough to
run with agents*.
### The problem: agentic AIDLC fails on reliability, not capability
The [AIDLC methodology](https://devfloor9.github.io/engineering-playbook/docs/aidlc/methodology)
identifies that AI-driven development breaks down in three recurring ways, and
none of them are about model quality:
- **Hallucination & drift** — concepts mean different things across prompts and
sessions, so handoffs only work because a human re-interprets them.
- **Runaway execution** — without architectural limits an agent loop can fire
hundreds of retries (the methodology's fintech case: 847 retries, ~$2,200, a
3-hour outage) before anyone notices.
- **Self-grading** — the agent that wrote the code also writes the tests, so its
own blind spots survive verification.
The methodology answers these with a **reliability dual-axis**: *Ontology
Engineering* guarantees the **correctness** of what agents produce (the WHAT/WHEN),
and *Harness Engineering* enforces the **safety** of how they execute (the HOW).
OMA is the installable implementation of that dual-axis on AWS.
### The three pillars OMA installs
| Methodology axis | Guarantees | OMA implementation | Entry point |
|---|---|---|---|
| **Ontology Engineering** | Correctness (WHAT · WHEN) | 8 JSON-Schema entities in `schemas/ontology/`, `oma validate`, schema-evolution rules | `/oma:inception`, `oma validate` |
| **Harness Engineering** | Safety (HOW) | Harness DSL v2 (`policies` → `PreToolUse` enforcement, `telemetry`), `oma compile --strict-enterprise`, pinned MCP, sandboxed budget eval | `oma doctor --enterprise`, `oma compile` |
| **AgenticOps (Outer Loop)** | Living ontology | `self-improving-loop`, `continuous-eval`, `incident-response` feeding operational signal back into the ontology | `/oma:agenticops`, `/oma:self-improving` |
**AIDLC Workflows** — the AWS-official
[awslabs/aidlc-workflows](https://github.com/awslabs/aidlc-workflows) 3-phase loop —
is the *process spine* these pillars hang on. OMA contributes only `*.opt-in.md`
extensions to it and never forks the core. The result: AIDLC stops being "design
and build, then hope," and becomes a lifecycle where every artifact is a validated
ontology document and every agent action runs inside a harness.
## Who is this for?
- Platform engineers building agentic AI on AWS EKS.
- Teams running LLM/agent workloads who want AIDLC to cover *operations*, not
just design and construction.
- Teams modernizing legacy workloads onto AWS using a repeatable 6R workflow.
- Claude Code or Kiro users who want a drop-in marketplace rather than
hand-rolling skills.
## Plugins
| Plugin | What it does | Example skills |
|---|---|---|
| **`ai-infra`** | Build & run the Agentic AI Platform on EKS | `agentic-eks-bootstrap`, `vllm-serving-setup`, `inference-gateway-routing`, `langfuse-observability`, `gpu-resource-management`, `ai-gateway-guardrails` |
| **`agenticops`** | Operate it with agents | `self-improving-loop`, `autopilot-deploy`, `incident-response`, `continuous-eval`, `cost-governance`, `audit-trail`, `anomaly-detection`, `root-cause-analysis`, `automated-remediation`, `slo-management`, `predictive-scaling` |
| **`aidlc`** | AIDLC Phase 1 (Inception) + Phase 2 (Construction) extensions | `requirements-analysis`, `user-stories`, `workflow-planning`, `component-design`, `code-generation`, `test-strategy`, `risk-discovery`, `quality-gates`, `ontology-wiki` |
| **`modernization`** | Legacy workload modernization to AWS (6R strategy) | `workload-assessment`, `modernization-strategy`, `to-be-architecture`, `containerization`, `cutover-planning` |
## Tier-0 workflows
OMA inherits the Tier-0 pattern from OMC — high-leverage workflows you invoke
once and let run, with human approval only at checkpoints.
| Command | Purpose |
|---|---|
| `/oma:autopilot` | Full AIDLC loop autopilot (Inception → Construction → Operations) |
| `/oma:aidlc-loop` | Single-feature AIDLC one-pass |
| `/oma:agenticops` | Operations mode (continuous-eval + incident-response + cost-governance) |
| `/oma:self-improving` | Feedback loop — traces (via your opt-in trace MCP) to skill/prompt improvement PR |
| `/oma:platform-bootstrap` | 5-checkpoint Agentic AI Platform build on EKS |
| `/oma:modernize` | Legacy workload modernization (6R decision → cutover) |
| `/oma:review` | AIDLC artifact review (ADR, spec, design, PR) |
| `/oma:cancel` | Terminate active Tier-0 mode |
## Install
### ⚡ One-liner (Tech Preview — recommended)
`install.sh` downloads the pinned release tarball, extracts to `~/.oma`, and
symlinks `~/.local/bin/oma`. `oma setup` then writes a project profile,
seeds the ontology, installs the plugins, and runs `oma doctor` to confirm
the environment.
```bash
curl -fsSL https://raw.githubusercontent.com/aws-samples/sample-oh-my-aidlcops/v0.5.0-preview.1/install.sh | bash
cd my-project
oma setup
oma doctor
```
See the [Easy Button docs](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/easy-button)
for what `oma setup` writes, how the 13 doctor probes work, and how the
ontology + harness DSL get enforced at runtime.
> **Tech Preview notice** — `v0.5.0-preview.1` treats `profile.yaml` v1, the 8
> ontology schemas, and Harness DSL v2 as stable. Everything else (CLI UX, doctor
> report shape) may evolve before GA. See [Support Policy](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/support-policy).
### Claude Code (native marketplace — Claude Code 2.0+)
```bash
claude
```
Inside the Claude Code session:
```text
/plugin marketplace add https://github.com/aws-samples/sample-oh-my-aidlcops
/plugin install ai-infra@oh-my-aidlcops
/plugin install agenticops@oh-my-aidlcops
/plugin install aidlc@oh-my-aidlcops
/plugin install modernization@oh-my-aidlcops
/plugin list
```
> `/plugin install` accepts a single plugin id per invocation. Pasting the
> six lines above lets Claude Code run them sequentially. For a shell
> one-liner, use `claude <<'EOF' ... EOF` to feed the commands via stdin.
### Claude Code (manual script — legacy / MCP-only)
```bash
git clone https://github.com/aws-samples/sample-oh-my-aidlcops
bash sample-oh-my-aidlcops/scripts/install/claude.sh
```
> The manual script creates `~/.claude/plugins/` symlinks and merges MCP
> servers + hooks into `settings.json`. **On Claude Code 2.0+ this alone
> does NOT register plugins with `/plugin list`.** Use it only for
> legacy 1.x environments, offline CI, or when you want to wire MCP
> servers without activating the marketplace.
### Kiro
```bash
git clone https://github.com/aws-samples/sample-oh-my-aidlcops
bash sample-oh-my-aidlcops/scripts/install/kiro.sh
```
### Initialize `.omao/` in your project
```bash
cd <your-project>
bash <path-to-oma>/scripts/init-omao.sh
```
### AIDLC extensions (opt-in)
```bash
bash scripts/install/aidlc-extensions.sh
# Clones awslabs/aidlc-workflows into ~/.aidlc and symlinks OMA's opt-in extensions.
```
### Quick start — enterprise flags
After `oma setup`, four commands are worth exercising on day one:
```bash
# 1. Diagnostic only — 8 probes, never changes state.
oma doctor --enterprise
# → [doctor:enterprise] 8 probes OK (0 warnings)
# 2. Enforce the gate — rejects DSL v1, missing approval_chain, legacy
# string artifacts, and unclassified Risks.
oma compile --strict-enterprise
# → compiled ai-infra: plugins/ai-infra/.mcp.json ...
# 3. Validate an ontology entity (Spec / ADR / Deployment / Incident /
# Budget / Risk / Agent / Skill) against its JSON Schema.
oma validate path/to/deployment.yaml
# → [oma validate] path/to/deployment.yaml: schema OK
# 4. Print the execution plan for a DSL v2 workflow (stub: no runtime yet).
oma run-workflow ai-infra platform-bootstrap
# → execution order: preflight -> provision -> verify
```
Adoption is one flag away — every enterprise field is optional
until you opt in via `--strict-enterprise`. See
[Enterprise readiness](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/enterprise-readiness)
for the phased adoption checklist.
### Compliance frameworks
OMA aligns ontology entities with four authoritative references so
audit-facing conversations work without invented vocabulary:
| Framework | Where it lands in OMA | Docs |
|---|---|---|
| [Model Context Protocol v1.0](https://modelcontextprotocol.io) | `Agent.mcp_uri` (canonical MCP server URI) | [Foundation](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/ontology) |
| [SLSA v1.1 Provenance](https://slsa.dev/spec/v1.1/) | `Deployment.artifact.{digest,provenance_uri,signing}` | [SLSA provenance](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/compliance/slsa-provenance) |
| [NIST AI RMF (AI 100-1)](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf) | `Risk.nist_ai_rmf_subcategory`, `AuditEvent.compliance.nist_ai_rmf` | [NIST AI RMF mapping](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/compliance/nist-ai-rmf) |
| [OWASP Top 10 for LLM Apps](https://owasp.org/www-project-top-10-for-large-language-model-applications/) | `Risk.owasp_llm_top10_id` (LLM01..LLM10) | [OWASP LLM Top 10 mapping](https://aws-samples.github.io/sample-oh-my-aidlcops/docs/compliance/owasp-llm-top10) |
OpenTelemetry (traces/metrics/logs) and FinOps Framework cost categories
feed the DSL v2 `telemetry` and `Budget` surfaces respectively.
### Liked it? Give the repo a Star
If OMA was useful, a ⭐ on the [GitHub repository](https://github.com/aws-samples/sample-oh-my-aidlcops)
helps us prioritize maintenance and keeps release notifications flowing
to you. It is entirely optional — nothing in the CLI changes based on
your star status.
## Architecture
```
User request
│
▼
Tier-0 trigger ─── matches keyword? ──▶ /oma:<workflow>
│
▼
Plugin dispatch
│
├─▶ ai-infra (build/run runtime)
├─▶ agenticops (operate)
├─▶ aidlc (Phase 1 + Phase 2)
└─▶ modernization (legacy → AWS)
│
▼
Skills execute, calling AWS Hosted MCP
│
├─▶ eks, cloudwatch, prometheus, aws-iac, cost-explorer, ...
│
▼
Checkpoint — human approves
│
▼
Operations phase continues autonomously
│
└─▶ self-improving-loop feeds corrections back to Construction
```
## Foundation: ontology + harness DSL
OMA plugins rest on two shared layers:
1. **Ontology** (`ontology/`, `schemas/ontology/`) — eight JSON Schemas that
define the vocabulary every plugin and skill agrees on: `Agent`, `Skill`,
`Deployment`, `Incident`, `Budget`, `Risk`, plus `Spec` and `ADR`. A handoff
between Construction and Operations is no longer a prose description; it is a
validated `Deployment` document. See [ontology/README.md](./ontology/README.md)
and [ontology/glossary.md](./ontology/glossary.md).
2. **Harness DSL** (`schemas/harness/dsl.schema.json`, `tools/oma_compile/`) —
one `<plugin>.oma.yaml` per plugin is the single source of agents, MCP
servers, hooks, and triggers. `python -m tools.oma_compile` translates it
to the native `.mcp.json` and `kiro-agents/*.agent.json` files that Claude
Code and Kiro already consume, so marketplace installs stay unchanged.
```
<plugin>.oma.yaml ──(oma-compile)──▶ .mcp.json
▶ kiro-agents/<agent>.agent.json
▶ .omao/triggers.json (merged across plugins)
```
CI (`.github/workflows/oma-foundation.yml`) validates every schema fixture and
runs `oma-compile --check` to reject drift between DSL sources and committed
native files.
## Security posture
This repository ships with conservative defaults. A few things are worth
calling out before you use it in production:
- **MCP servers are pinned** to exact PyPI versions in every `.mcp.json` and
`kiro-agents/*.agent.json`. `@latest` is not used anywhere — a compromised
upstream release cannot silently land alongside AWS credentials.
- **EKS MCP is read-only by default.** The bundled Kiro agent profile does
*not* pass `--allow-write` or `--allow-sensitive-data-access` to
`awslabs.eks-mcp-server`. Add them explicitly when you need to provision
or mutate EKS resources, and audit that change.
- **IAM is least-privilege.** The `langfuse-observability` skill uses a
customer-managed policy scoped to the Langfuse bucket ARN; AWS managed
`AmazonS3FullAccess` (`s3:*` account-wide) is explicitly rejected with a
"Bad Example" block in the skill.
- **`budget.yaml` expressions are sandboxed.** The `cost-governance` skill
evaluates `rule["when"]` via [`simpleeval`](https://pypi.org/project/simpleeval/)
(AST walker, zero builtins, zero callables). A documented Bad Example shows
why Python `eval()` on a user-editable file is an RCE vector.
- **Session state stays local.** `.omao/state/`, `.omao/plans/`, `.omao/logs/`,
`.omao/notepad.md`, and `.omao/project-memory.json` are gitignored —
`audit-trail` captures prompts verbatim (PII, approver identity, SOC2
retention content) and must never leave the machine.
- **Hooks require a real JSON encoder.** `hooks/session-start.sh` uses `jq`
(with `python3` / `python` as ordered fallbacks) and exits non-zero rather
than emitting shell-interpolated JSON, preventing state-file injection into
the session context.
## Reused assets
OMA stands on top of existing AWS and community work rather than reinventing.
| Source | License | How OMA uses it |
|---|---|---|
| [awslabs/agent-plugins](https://github.com/awslabs/agent-plugins) | Apache-2.0 | Adopts `plugin`, `skill-frontmatter`, `mcp`, `marketplace` JSON schemas. |
| [awslabs/aidlc-workflows](https://github.com/awslabs/aidlc-workflows) | MIT-0 | Consumed as AIDLC core; OMA contributes only `*.opt-in.md` extensions. |
| [awslabs/mcp](https://github.com/awslabs/mcp) | Apache-2.0 | 11 hosted MCP servers referenced via `uvx` stdio. |
| [aws-samples/sample-apex-skills](https://github.com/aws-samples/sample-apex-skills) | MIT-0 | Workflow 5-checkpoint template pattern. |
| [aws-samples/sample-ai-driven-modernization-with-kiro](https://github.com/aws-samples/sample-ai-driven-modernization-with-kiro) | MIT-0 | Risk-discovery, audit-trail, quality-gates, 6R strategy methodology. |
| [Atom-oh/oh-my-cloud-skills](https://github.com/Atom-oh/oh-my-cloud-skills) | MIT | Eval script patterns, Kiro conversion reference. |
| [oh-my-claudecode](https://github.com/Yeachan-Heo/oh-my-claudecode) | — | Tier-0 orchestration and `.omc/` state inheritance. |
Full attribution in [NOTICE](./NOTICE). See
[REFERENCES.md](./REFERENCES.md) for the complete catalogue of every
upstream repo, authoritative spec, framework, and runtime tool that
OMA touches.
## License
MIT No Attribution (MIT-0). See [LICENSE](./LICENSE).
## Contributing
OMA is in Tech Preview (`v0.5.0-preview.1`). See [CONTRIBUTING.md](./CONTRIBUTING.md)
for the working agreement (English-only artifacts, no AI attribution, commit
per unit of work) plus the PR/branch-naming guidelines, and
[CODE_OF_CONDUCT.md](./CODE_OF_CONDUCT.md) for the Amazon Open Source Code of
Conduct. Issues and PRs are especially welcome for skill quality, MCP coverage
gaps, and Kiro compatibility testing.
For security issues, do **not** open a public GitHub issue — follow the AWS
[vulnerability reporting process](https://aws.amazon.com/security/vulnerability-reporting/)
instead.
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