# Aidlc Context

> Scan workspace, assess project landscape, and generate context document, blueprints, and platform shim. First phase of the AI-DLC specification workflow.

- Skill: `aws-samples/aidlc-context` (Agent Skill, multi-file: 11 files)
- Install (CLI): `npx skillmds@latest add aws-samples/aidlc-context`
- Raw SKILL.md: https://api.skillmd.com/api/skills/aws-samples/aidlc-context/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- License: MIT
- Author: aws-samples (https://skillmd.com/u/aws-samples)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/aws-samples/aidlc-context

---


# Context Assessment Skill

> **Base**: `shared/base.md` (full on first load, §Summary on chain). **Actions**: load per-step from `actions/`.

You assess the project landscape — existing code, technology stack, architecture patterns — and generate a context document plus blueprints (referenced by a thin platform shim) that inform all subsequent phases.

When active:
1. Follow ONLY the process below
2. WAIT for user approval before considering the phase complete
3. Never narrate your internal process — present only results, questions, and choices
4. ALL output in the user's language (read manifest `language` field) — no English narration

---

## Activation

```
✅ aidlc-context active — {platform} detected.
Ready to assess your project and generate context.
```

Then proceed to initialization.

---

## Quick Start

1. Scan workspace → classify greenfield/brownfield → detect stack and patterns
2. Generate `context.md` with findings and recommendations
3. Generate blueprints (product.md, tech.md, structure.md, resources.md) at `.aidlc/blueprints/` + the platform shim
4. Create manifest and audit trail
5. Present results with recommended workflow diagram → wait for approval
6. On approval → hand off to requirements

**Reads**: Workspace files (source, configs, README)
**Writes**: context.md, blueprints/* (canonical content), platform shim (`.kiro/steering/aidlc.md` or `.claude/CLAUDE.md`), manifest, audit.md

---

## Information Contract

### Required Inputs
| Information | Description | Accepted Formats |
|---|---|---|
| Feature request | What the user wants to build | Inline chat message |
| Workspace access | Ability to scan project files, configs, source code | Filesystem |

### Optional Inputs
| Information | Description | Accepted Formats |
|---|---|---|
| Existing architecture docs | README, architecture.md, or similar | Markdown, plain text, any doc |
| Prior context document | Previously generated context.md | Markdown |
| Reverse-engineer analysis | Deep codebase analysis | Files in `.aidlc/reverse-engineer/` |

If user provides existing context doc, enrich with workspace scan rather than starting from scratch.
If `.aidlc/reverse-engineer/` exists, read `overview.md` and `conventions.md` Summary sections.

### Outputs
| Artifact | Default Path |
|---|---|
| context.md | `{SPECS_DIR}/{feature}/context.md` |
| product.md | `{BLUEPRINTS_DIR}/product.md` |
| tech.md | `{BLUEPRINTS_DIR}/tech.md` |
| structure.md | `{BLUEPRINTS_DIR}/structure.md` |
| resources.md | `{BLUEPRINTS_DIR}/resources.md` |
| Platform shim (Kiro) | `.kiro/steering/aidlc.md` (Kiro only) |
| Platform shim (Claude) | `{PROJECT_ROOT}/.claude/CLAUDE.md` (Claude Code only) |
| aidlc-manifest.yaml | `{WORKFLOW_DIR}/{feature}/aidlc-manifest.yaml` |
| audit.md | `{WORKFLOW_DIR}/{feature}/audit.md` |

> `product/tech/structure/resources` are canonical and platform-agnostic in `{BLUEPRINTS_DIR}` (`.aidlc/blueprints/`). The platform shim references them (Kiro `#[[file:...]]`, Claude `@../`). `corrections.md` is added to blueprints on-demand by the learning loop, not during context.

---

## Initialization

1. Detect environment (per shared base)
2. Detect language from user's first message (ISO 639-1)
3. Get feature name from user
4. Check for existing manifest at `{WORKFLOW_DIR}/{feature}/aidlc-manifest.yaml`:
   - Found → resume scenario. Read manifest, present what exists, ask user.
   - Not found → fresh start.
5. Create folders: `{SPECS_DIR}/{feature}/`, `{WORKFLOW_DIR}/{feature}/`
6. **Create skeleton manifest** (fresh start only — captures language early for all subsequent steps):

```yaml
version: "1.0.0"
feature: "{feature}"
language: "{language}"
platform: "{platform}"
created: "{ISO timestamp}"
updated: "{ISO timestamp}"
state:
  status: "active"
  scope: null
  sharedPhases: []
  mode: null
  foundationSkipped: false
  implementationMode: null
  quickPath: false
artifacts: {}
implementation:
  totalTasks: 0
  completedTasks: 0
  currentTask: null
  currentWave: null
context-summary: {}
decisions: {}
steering: {}
units: []
```

7. **Create audit trail**: `{WORKFLOW_DIR}/{feature}/audit.md` with header `# Audit Trail — {feature}`

---

## Process

Execute actions sequentially. **Load the action file when you reach that step — not before.**

| Step | Action | Load |
|---|---|---|
| 1 | Assess workspace + generate artifacts | `{SKILL_DIR}/actions/assess.md` |
| 2 | Edit (if user requests changes) | `{SKILL_DIR}/actions/edit.md` |

---

## Skill Handoff

**Next skill**: `aidlc-requirements` (on user approval of context).

---

## Phase-Specific Rules

- **Errors**: follow error taxonomy from `shared/base.md` (❌ Fatal / ⚠️ Degraded / ℹ️ Info).
- **Audit actions**: assessment, approval, edit.

---

## Context Recovery

If context is lost mid-phase, follow `aidlc/shared/base.md` Context Recovery, then:
- Check `artifacts.context.status` — if `"draft"`, resume from Step 10 (present results)
- If no context artifact in manifest — restart from Step 1 (workspace scan)