| name | sdlc-agents |
| description | Use when the user wants to install, configure, or onboard the SDLC Agent Fleet (workitems, researcher, docwriter, and others) into a new project or AWS account. Drives the conversation from tool discovery through agent selection, provisioning, and verification. Delegates each step to the narrower sdlc-agents-* skills. |
Install SDLC Agent Fleet in a new project
The SDLC Agent Fleet is a set of autonomous agents that cover the software development lifecycle — project management, documentation, business analysis, and ADR linking. Each agent runs on Amazon Bedrock AgentCore. Shipping agents integrate with Asana (PM) and GitHub (SCM); additional tools (Jira, GitLab, Slack, Salesforce, Datadog) are planned but not yet supported end-to-end.
Not every customer uses every agent. Your job is to have a conversation that:
- Figures out which tools the customer already uses
- Matches those tools to the subset of agents that make sense
- Provisions and configures only those agents
Installer repo vs. target project
This flow runs from inside a clone of the installer repo (the one containing agents/, infra/, cedar/, and these skills). It installs the fleet onto a target project — usually a different repository whose CI/CD you are wiring up.
Never assume the cwd is the target. The /sdlc-agents slash command resolves a TARGET_REPO absolute path (and TARGET_REMOTE origin URL) from the user's argument before handing off to this flow — if those aren't set yet, stop and resolve them before Step 1. All target-side filesystem operations (ADR detection, .sdlc-agents/selection.yaml, .github/workflows/ edits, git commands for branch/remote checks) use TARGET_REPO. All installer-side reads (agent source, templates, skill files, scripts) come from the cwd.
Golden rules
- Never assume tool choice. Ask. Most customers have strong opinions about their PM tool, their source-control host, their chat platform. Don't pick GitHub just because it's the common default.
- Never provision agents the customer didn't ask for. More agents = more IAM roles, runtimes, webhooks to manage. Fewer is usually better.
- One decision at a time. Don't dump a 20-question survey on the user. Ask one thing, record it, move on.
- Surface cost and coupling as you go. Each agent adds an AgentCore Runtime (~$30–100/mo cold), an ECR repo, an IAM role, and one or more webhook integrations. Tell the user what they're opting into before they opt in.
- Work from the top-level skill, delegate to narrower skills. Each step has its own skill (see the step list below). When you're ready for that step, invoke the matching skill and follow its checklist. Don't inline its logic here.
Flow
Step 1 — Discover the customer's toolchain
Ask, don't scan. Start with the minimum viable set of questions:
- What do you use for project management? (Asana / Jira / Linear / Trello / Aha! / other / none) — only Asana is supported today
- What do you use for source control? (GitHub / GitLab / Bitbucket / other) — only GitHub is supported today
- What AWS account and region do you want the fleet to live in?
Record answers. If the user names a tool that doesn't have a shipping connect skill (e.g. Jira, GitLab), tell them so immediately — don't let the conversation go ten questions deep before surfacing that their PM or SCM isn't supported yet.
Step 2 — Propose an agent selection
With the tool list in hand, invoke the sdlc-agents-select skill. It has the authoritative agent roster + which integrations each agent needs, and it presents the filtered list to the user. The user confirms or edits.
All repo-specific checks inside the select skill (ADR directory detection in particular) must be rooted at TARGET_REPO, not the installer cwd.
After selection: write the chosen list to $TARGET_REPO/.sdlc-agents/selection.yaml. Later steps read from there so the user can re-run any single step without restating their selection.
Step 3 — Provision AWS infrastructure
Invoke the sdlc-agents-provision-aws skill. For each selected agent it creates:
- IAM runtime role (named
<agent>-agentcore-runtime) with the specific policies that agent needs
- ECR repo (
sdlc-agents/<agent>)
- AgentCore Runtime (created on first container push; the deploy workflow handles this)
Idempotent — safe to re-run. Walks the user through prerequisites (Bedrock model access, deploy-role OIDC trust) if they aren't already set up.
Step 4 — Wire up integrations (per tool)
For each tool the customer uses, invoke the matching connect skill:
- Asana → sdlc-agents-connect-asana (OAuth app setup, MCP vs API app, PAT for webhook Lambda)
- GitHub → sdlc-agents-connect-github (GitHub App or fine-grained PAT for MCP, deploy-role OIDC for CI)
Other tools (Jira, GitLab, Slack, Salesforce, Datadog) don't have connect skills yet — the shipping agents all work against Asana + GitHub. If the user picked one of those other tools during discovery, tell them honestly that the connect path isn't written yet and point them at the vendor's remote MCP docs; don't fabricate setup steps.
Each connect skill knows the specific pitfalls of its tool (Asana's MCP-app-vs-API-app distinction is the classic one) and walks past them.
Step 5 — Register webhooks and bot accounts
Invoke sdlc-agents-register-triggers. This is the step that turns a provisioned fleet into one that actually reacts to @agent mentions:
- Asana webhook → API Gateway endpoint, with handshake secret stored in SSM
- GitHub:
agent-dispatch.yml workflow enabled, trigger list updated for selected agents
- Slack app event subscriptions, etc.
Step 6 — Verify
Invoke sdlc-agents-verify. Runs a smoke test per agent (health-check invocation + one end-to-end trigger test through the actual integration surface). Reports pass/fail per agent so the user knows exactly which integrations are live.
Step 7 — (Optional) Enable Claude Code on Bedrock for issue coding + PR review
After the fleet is verified, ask the user:
Do you want to enable @claude in this repo so Claude Code can code against issues and review PRs? It uses the same Amazon Bedrock model as the fleet, authenticated via GitHub Actions OIDC — no Anthropic API key needed.
If yes, invoke sdlc-agents-setup-claude-code. It's scoped to GitHub + Bedrock (not tied to the agent fleet's AgentCore runtimes), so users who want @claude but not the fleet can also run it standalone.
When to stop and get help
If any step fails in a way the skill didn't anticipate:
- Capture the exact error (tool name, response body, timestamps)
- Check CloudWatch Logs for the relevant Lambda or runtime
- Don't paper over the error with a retry loop — figure out why before retrying
- If it's an AWS permissions issue (SCPs, org policies), stop and tell the user; you can't fix those from inside the skill
Reading material
docs/agents/<agent>.md — one file per agent with role, triggers, guardrails, and infra notes. Read the relevant ones before walking a customer through that agent's setup.docs/agent-fleet-implementation-plan.md — the full multi-phase rollout plan..dispatch/agents.yaml — canonical agent registry format. Your end state should match this structure.AGENTS.md (at repo root) — step-by-step runbook for configuring the fleet manually. Skills automate this; when the skill flow diverges from the runbook, the skill is wrong — update it.
