sdlc-framework 0.6.1

Local-only multi-AI SDLC framework with auto-detect engine, tier-aware DoD, and GitHub integration

SDLC Framework

Local-only, multi-AI SDLC framework. One sdlc CLI scaffolds a 4-phase project, auto-detects DoD evidence, dispatches the right specialist agent, and surfaces blockers via desktop notifications + GitHub issues.

Works with 8 AI coding tools out of the box: Claude Code · GitHub Copilot · Cursor · OpenAI Codex · Aider · Windsurf · Cline · Continue.

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What it does

flowchart LR
  Idea[Idea / brief] --> P1[📋 Phase 1<br/>Planning]
  P1 --> P2[🎨 Phase 2<br/>Design]
  P2 --> P3[💻 Phase 3<br/>Development]
  P3 --> P4[🚀 Phase 4<br/>Testing & Deploy]
  P4 -. metrics & feedback .-> P1

Phase	Output	DoD highlight
1. Planning	PRD, user stories, NFRs, stakeholder map, risks	PRD approved, NFRs quantified
2. Design	C4 architecture, API spec, DB schema, UI/UX, ADRs, threat model	C4 reviewed, OpenAPI lint clean, STRIDE done
3. Development	Coding/git/error/security standards, PR template, code-review checklist	Lint+test+security gating, AI disclosure in PR
4. Testing & Deploy	Test plan, CI/CD, runbook, SLOs, incident response	95% pass, SLO alerts wired, postmortem ready

The engine auto-scans your repo every 3 seconds (when in watch mode), evaluates 28 DoD rules per automation/rules.json, and writes a JSON state file that the dashboard renders. AI agents read the same rules to know what evidence to produce next.

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Installation

uv tool install sdlc-framework        # recommended (uv manages a clean tool env)
# or
pip install --user sdlc-framework

Verify:

sdlc --version
# sdlc 0.6.0

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Quickstart — new project

mkdir my-app && cd my-app
sdlc setup     # interactive: scaffold + pick which AI tool(s) to install commands for

The wizard asks which AI coding tools you use (Claude Code / Cursor / Copilot / Codex / Aider / Continue / Cline / Windsurf), auto-detects your stack from package.json / pyproject.toml / go.mod, and writes tool-native artifacts so you can stay inside your AI tool and use slash commands instead of a terminal CLI.

Or skip the prompts:

sdlc setup --tools=claude-code,cursor --stack=node

Then operate inside your AI tool

Claude Code — slash commands are wired:

/sdlc-start "Mobile task app for 5-person team"   begin Phase 1 (auto-invokes pm-agent subagent)
/sdlc-next                                        advance to the next pending item
/sdlc-scan                                        refresh progress summary
/sdlc-dispatch                                    inspect the full dispatch decision
/sdlc-dashboard                                   serve the live UI on localhost

The slash commands shell out to the sdlc CLI underneath; you don't need to context-switch to a terminal.

Cursor — rules in .cursor/rules/sdlc.mdc are auto-loaded. Just share your idea in chat; Cursor adopts the pm-agent persona. From Cursor's integrated terminal you can also run sdlc start "<idea>" etc.

GitHub Copilot Chat — prompt files in .github/prompts/sdlc-*.prompt.md are usable via the Copilot Chat prompts UI.

Codex / Aider / Continue / Cline / Windsurf — text rules at the project root (AGENTS.md, CONVENTIONS.md, .continuerules, .clinerules, .windsurfrules) are auto-loaded. From the tool's terminal you run the sdlc CLI commands directly.

Why an explicit subagent invocation?

In Claude Code, plugins like superpowers:brainstorming auto-fire on idea prompts and write design specs into the wrong folder. The slash commands this framework ships call use the pm-agent subagent explicitly, which forces Claude Code to load .claude/agents/pm-agent.md (this repo's authoritative role spec) and bypass conflicting plugins.

The first command scaffolds:

• 4 phase folders (01-Planning/, 02-Design/, 03-Development/, 04-Testing-Deploy/) with 24 templates + 4 standards docs + runbooks.
• 9 specialist agent definitions (agents/orchestrator.md, phase-1-pm.md, ..., cross/security-reviewer.md).
• 8 AI tool config files at the project root (CLAUDE.md, .cursorrules, .github/copilot-instructions.md, etc.) — generated from a single shared source.
• project.yaml template — fill in name, team, tier (1=critical / 2=standard / 3=experiment), owners, GitHub URL.
• automation/rules.json — declarative DoD detection rules.
• .github/workflows/ci.yml (when stack is detected) — Node/Python/Go matrix CI template.

After init, edit project.yaml:

schema_version: 1
name: my-app
team: platform
stack: node            # auto-detected; override with --stack at init
tier: 2                # 1=critical, 2=standard, 3=experiment
phase_target: 1        # the phase your team is currently focused on
repo_url: https://github.com/your-org/my-app
owners:
  sponsor: [e-mail]
  eng_lead: [e-mail]
  sre: [e-mail]
  # security: required when tier == 1
flags:
  ai_disclosure_required: true
  signoffs_strict: auto

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Quickstart — existing project

sdlc init is idempotent and non-destructive: it skips files that already exist. Drop it on top of any repo:

cd ~/Projects/existing-app           # has package.json / pyproject.toml / etc.
sdlc init                             # auto-detects stack, skips existing files
sdlc scan --print                     # see which DoD items are already satisfied

What you'll see on the first run:

• Phase 1 is mostly red because PRD / user stories / NFRs likely don't exist yet → fill them in 01-Planning/templates/.
• Phase 3 "AI tool configs" + "coding standards" green because sdlc init just wrote them.
• Phase 4 "CI/CD pipeline configured" green if the auto-detected ci.yml matches your stack; otherwise the existing workflow stays untouched.
• owners-defined red until you fill project.yaml.owners.

What sdlc init will NOT overwrite (unless --force):

• Your existing README.md, LICENSE, package.json, source code.
• .github/workflows/ci.yml if you already have one.
• project.yaml once you've edited it.

To force regeneration of a single file: delete it then re-run sdlc init. To force everything: sdlc init --force (use carefully).

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CLI reference

# getting started (you usually only need `sdlc setup`)
sdlc setup [--tools=...] [--stack=...] [--force]  interactive: scaffold + pick AI tools
sdlc init [--stack=node|python|go] [--force]      scaffold without picking tools (low-level)
sdlc start "<your idea>"                          save the idea (used by /sdlc-start)
sdlc next [--json]                                show next pending DoD item (used by /sdlc-next)
sdlc resume                                       "where am I?" — phase, last activity, next action
                                                  (aliases: status, where)
sdlc replan [--scope=...] [--reason=...] [--clear] mark items dirty after a major change
                                                   --scope ∈ requirements / ui-ux / tech-stack /
                                                             scale-nfr / custom

# workflow
sdlc scan [--watch] [--interval N] [--print]      run the engine; write state.json
sdlc dispatch [--json] [--brief]                  next-agent decision + ready-to-paste brief
sdlc dashboard [--port N]                         serve dashboard/index.html on localhost

# integrations
sdlc agent-log start|done|fail <agent> <item>  append activity entry (50-line ring buffer)
sdlc notify <kind> <id> <title> <body>         desktop notification (osascript / notify-send)
sdlc gh-issue --clarification PATH | --gate ID file a GitHub Issue (idempotent)
sdlc gh-pr [--ready] [--base BRANCH]           open a Draft PR (or reuse existing)
sdlc build-ai-configs                          regenerate the 8 AI tool configs from shared sources

sdlc --version                                 print version

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Concepts

project.yaml — tier-aware DoD strictness

The engine reads project.yaml and adjusts what evidence is required per tier:

Rule	Tier 1 (critical)	Tier 2 (standard)	Tier 3 (experiment)
nfr-quantified	needs p99 AND (RTO or RPO)	p95/p99	optional
threat-model	STRIDE + Mitigation per asset	STRIDE only	optional
tests-present	required	recommended	optional
prod-deploy-signoff	sponsor + eng_lead + sre + security	sponsor + eng_lead + sre	eng_lead only
owners-defined	sponsor + eng_lead + security + sre	sponsor + eng_lead + sre	sponsor + eng_lead

Missing project.yaml = engine uses tier: 2 defaults; the owners-defined DoD item stays red until you fill the file in.

Agent activity log

Every agent (Claude / Cursor / Codex / human dev) calls sdlc agent-log start|done|fail to append a JSONL entry to automation/agent-runs.jsonl. The dashboard renders the last 10 entries in a live "🤖 Agent activity" panel — so you can watch the loop without tailing a terminal.

SDLC_TOOL=claude-code sdlc agent-log start pm-agent prd-exists
# ... agent does work ...
sdlc agent-log done pm-agent prd-exists --summary="created 01-PRD.md"

Desktop notifications

When the engine runs in --watch mode, every newly appearing clarification or pending gate fires a desktop notification:

• macOS — osascript display notification (sound on gate)
• Linux — notify-send -u critical|normal --app-name=SDLC
• elsewhere — silent fallback (log line still written to automation/notifications.log)

Notifications are transition-only: an item that stays pending across many scans only fires once. Engine startup baselines and does not spam.

Custom items — track per-feature progress in phase 3

Phase 3 by default measures "development infrastructure ready" (standards published, AI configs wired, PR template, security checklist) — NOT "how many features you've shipped". To track per-feature/milestone progress, declare custom_items in project.yaml:

custom_items:
  - id: feature-auth
    label: "Auth feature: login + signup"
    phase: 3
    checks:
      - type: file_glob_min_count
        glob: "src/auth/**/*.ts"
        min: 1
      - type: file_glob_min_count
        glob: "src/auth/**/*.test.ts"
        min: 1
  - id: feature-billing
    label: "Billing: Stripe integration"
    phase: 3
    checks:
      - type: file_glob_min_count
        glob: "src/billing/**/*.ts"
        min: 1

The engine evaluates them like framework rules. They appear in the phase tracker and contribute to done_pct. Use any check type the engine supports (file_glob_min_count, file_contains_all, file_contains_any, regex_min_count, file_exists, etc.).

This lets you answer "code đến phần nào" — auth feature ✓, billing ✗ (still building), dashboard ✗.

Why phase 1 / 2 may be empty after sdlc init

DoD rules use non-recursive globs (01-Planning/*PRD*.md, not **/*PRD*.md) so that template files in templates/ directories don't count as deliverables. After sdlc init you'll see Phase 1 at 0/7 — that's correct: you haven't written the actual PRD / user stories / requirements yet. The framework ships templates as starting points; filling them in is your work.

Phase 3 starts at ~5/7 ✓ because the framework ships the standards + PR template + AI tool configs (real files). That's the development infrastructure being ready, not features being done. Use custom_items (above) to add feature-level tracking.

Resume support

Stepped away for a day or a week? sdlc resume answers "where am I and what do I do next" in a single screen:

$ sdlc resume
📍 my-app — 79% done (22/28 items)
⏳ Currently pending: phase 1 · owners-defined — Project owners defined per tier
🕐 Last activity: pm-agent finished prd-exists · 2h ago
✋ 1 open clarification(s):
     · automation/clarifications/002-target-rps.md
🔒 1 pending gate(s):
     · phase4-prod (phase 4) — needs sponsor, eng_lead, sre

Next steps:
  → Resolve clarification first: edit automation/clarifications/002-target-rps.md
    Change `STATUS: open` → `STATUS: resolved`, then `sdlc scan`.

The dashboard's Resume card surfaces the same info with a one-click "Copy /sdlc-next" button. Aliases: sdlc status, sdlc where. Use --json for scripting.

Replan workflow — handle mid-project changes

What if you're at Phase 3 and the requirements change? Or you decide to switch tech stacks? Edit the source files, then run sdlc replan to mark downstream DoD items dirty so the engine forces them red until you redo the work:

$ sdlc replan
What changed? Pick one:
  1. Functional requirements / user stories changed
  2. UI / UX scope or screens changed
  3. Tech stack / language / framework changed
  4. Scale / NFR targets (RPS, latency, SLO) changed
  5. Custom — I'll list the item ids myself
  6. Clear all dirty markers (I've redone the work)
> 3
This will mark 12 item(s) dirty:
     · architecture-doc
     · api-spec
     · db-schema
     ... (12 total)
Why (one line for the audit log)?
> switching from Node to Python
Apply? [Y/n] y

[sdlc replan] marked 12 item(s) dirty.
[sdlc replan] wrote audit clarification: automation/clarifications/replan-20260501T101200Z.md

The dashboard surfaces a top banner "🔁 Replan in progress — N items dirty" so the team sees it. The engine respects automation/.replan.json (gitignored) — affected items show ✗ even when their evidence files still exist. After redoing the work, run sdlc replan --clear to drop the markers.

Flag-mode (no prompts) for automation:

sdlc replan --scope=tech-stack --reason="Node → Python"
sdlc replan --scope=custom --items=api-spec,tests-present --reason="API redesign"
sdlc replan --clear

Each replan also writes an audit clarification under automation/clarifications/replan-<ts>.md so the change is visible alongside other STOP triggers.

Smart dispatcher

sdlc dispatch (or the engine's state.next_dispatch field) returns:

• The primary agent for the next pending DoD item.
• A parallel_agents list — cross-cutting agents the orchestrator should invoke in parallel (e.g. api-spec done → invite security-reviewer).
• A blocked_by field — names the earlier-phase blocker (gate / clarification / high-risk path) when phase preference can't be honored.

It also reads project.yaml.phase_target: if the team has declared they're focused on phase 3, the dispatcher prefers phase 3 items unless an earlier phase has a hard blocker.

GitHub integration

Two wrappers surface STOP triggers from the local repo to GitHub via the gh CLI:

sdlc gh-issue --clarification automation/clarifications/001-foo.md
# → creates a GitHub Issue with body=clarification content + sdlc-clarification label
# → writes `issue_url:` back into the file (idempotent on re-run)

sdlc gh-issue --gate phase4-prod
# → creates "🔒 Gate pending: <label>" issue, writes URL into automation/signoffs/<id>.yaml

sdlc gh-pr [--ready] [--base main]
# → opens a Draft PR (or reuses existing) with auto-summary

Both exit 0 when gh is missing/unauthenticated — agents call them unconditionally; nothing breaks offline. The engine reads issue_url back into state.json and the dashboard shows a small ↗ icon next to each item.

Autonomous loop (automation/AGENT-LOOP.md)

When operating without a human in the loop, AI agents follow AGENT-LOOP.md:

1. Read state.json → know what's pending.
2. Read rules.json → know what evidence to produce.
3. Plan → execute → verify (re-scan engine) → commit.
4. STOP triggers (write a clarification file then halt):
   • 🔍 Ambiguity (insufficient information)
   • ⚔️ Conflict (two sources disagree)
   • 🚪 Gate (sign-off needed)
   • 🛑 High-risk path (auth / crypto / payment / migrations / secrets)

Resolve a clarification: edit the file, change STATUS: open → STATUS: resolved. Approve a gate: write automation/signoffs/<gate-id>.yaml with approved: true.

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AI tool support

The package ships one shared source in ai-tools/shared-prompts/standards.md plus a small per-tool header. sdlc init (and sdlc build-ai-configs) generates 8 tool-specific configs:

Tool	Output path	Auto-loaded
Claude Code / Desktop / Agent SDK	CLAUDE.md	✅
GitHub Copilot	.github/copilot-instructions.md	✅
Cursor	.cursorrules	✅
OpenAI Codex / agent	AGENTS.md	✅
Aider	.aider.conf.yml + CONVENTIONS.md	✅
Windsurf	.windsurfrules	✅
Cline	.clinerules	✅
Continue	.continue/config.json + .continuerules	✅

Edit the shared source, run sdlc build-ai-configs, and all 8 files regenerate. Each generated file carries a <!-- AUTO-GENERATED --> header so devs don't hand-edit them.

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Specialist agents

The agents/ directory ships 9 role specs that any AI coding tool can read:

Agent	Role
orchestrator	Reads state, picks next agent (mirrors sdlc dispatch).
pm-agent (Phase 1)	PRDs, user stories with G/W/T, quantified NFRs.
architect-agent (Phase 2)	C4, API spec, DB schema, ADRs, STRIDE.
developer-agent (Phase 3)	Coding standards, PR/CR checklists.
sre-agent (Phase 4)	CI/CD, deploy runbooks, SLOs, incident response.
security-reviewer (cross)	AppSec review on auth/data/crypto-touching changes.
test-author (cross)	Test strategy + actual test files.
doc-keeper (cross)	Sync docs with behavior changes.
clarification-triager (cross)	Triages ambiguity into structured clarification files.

Same roster ships in .claude/agents/ (Claude Code subagent format with frontmatter). Other tools read agents/*.md directly per their conventions.

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Dashboard

A compact ops-console — single viewport, no swipe — auto-fetches dashboard/state.json every 3 seconds. Layout from top to bottom:

• Top header: live status dot (green = OK, amber = "needs you", red = offline), project name (clickable link to repo_url), team, tier, focus phase, overall %, branch, last scan time.
• Alert banners: prominent amber/red banners surface every open clarification, every pending gate, and any active replan. Each banner has a one-click [Copy path] button so you can paste the file path into your editor immediately. GitHub issue ↗ links appear when sdlc gh-issue has filed one.
• "You are here" card: phase + active item + failure reason inline + paste-ready /sdlc-next prompt with a [Copy] button.
• Phase tracker: vertical list of all 4 phases (no swiping needed). Each phase row shows progress bar + every item with ✓ / 🔒 / · markers and the failing-rule message inline. The currently-active item is outlined.
• Side column (desktop ≥ 1100px):
  • Resume card — last agent activity + relative timestamp ("2h ago") + Copy /sdlc-next button.
  • Project info — stack, tier, phase target, owners.
  • Activity timeline — last 12 agent runs with agent · item · duration and timestamps.
  • Quick links — README, AGENT-LOOP, rules.json.

Run sdlc scan --watch in one terminal and sdlc dashboard in another:

sdlc scan --watch                # re-scans every 3 s, fires desktop notifications on transitions
sdlc dashboard --port 8765       # serves http://localhost:8765/dashboard/

Buttons that actually work: every [Copy] button uses navigator.clipboard.writeText and fires a copied toast. Every alert links to the underlying file path. The status dot changes to amber automatically when a clarification or gate appears, so an open browser tab signals "needs you" without needing notifications enabled.

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Architecture

your-repo/
├── project.yaml                                # metadata (engine reads this)
├── 01-Planning/                                # Phase 1 templates + your filled-in artifacts
├── 02-Design/                                  # Phase 2 ditto
├── 03-Development/                             # Phase 3 ditto
│   └── standards/                              # coding-standards, git-workflow, error-handling, security-checklist
├── 04-Testing-Deploy/                          # Phase 4 ditto
│   └── templates/ci-workflows/                 # node-ci.yml / python-ci.yml / go-ci.yml
├── agents/                                     # role specs read by AI tools
├── ai-tools/
│   ├── shared-prompts/standards.md             # source of truth for all tool configs
│   └── <tool>/                                 # AUTO-GENERATED per-tool config files
├── automation/
│   ├── rules.json                              # declarative DoD detection
│   ├── state.json                              # AUTO-GENERATED snapshot (gitignored)
│   ├── clarifications/*.md                     # AI raises STOPs here, user resolves
│   ├── signoffs/<gate-id>.yaml                 # user approves gates here
│   ├── agent-runs.jsonl                        # AUTO-GENERATED activity log (gitignored)
│   └── notifications.log                       # AUTO-GENERATED notify history (gitignored)
├── dashboard/
│   ├── index.html                              # bundled UI
│   └── state.json                              # mirror of automation/state.json
├── .github/workflows/ci.yml                    # selected CI template
├── CLAUDE.md / .cursorrules / AGENTS.md / ...  # 8 AI tool configs (auto-generated)
└── .gitignore

State flows: AI agent does work → engine scans → writes state.json → dashboard fetches → dispatcher decides → next agent.

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Development (contributing to this framework)

git clone https://github.com/vuonglq01685/SDLC.git
cd SDLC
python3 -m pip install --user -e .
python3 -m pip install --user -r automation/requirements-dev.txt

python3 -m pytest automation/tests/ -v       # 47 tests
bash scripts/tests/test_setup.sh             # smoke
bash scripts/tests/test_agent_log.sh
bash scripts/tests/test_notify.sh
bash scripts/tests/test_gh_wrappers.sh

# After editing scaffold templates, sync into the package:
bash scripts/sync-scaffold.sh

# Build wheel + sdist:
python3 -m build

The framework eats its own dog food: it has its own project.yaml, runs its own engine on itself, and the owners-defined rule is intentionally red here so you can see it work in the dashboard.

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License

MIT.

Links

• PyPI
• GitHub
• C4 model — https://c4model.com
• OWASP ASVS — https://owasp.org/www-project-application-security-verification-standard/
• Conventional Commits — https://www.conventionalcommits.org