Ddd Analysis

Project Context

• git installed: !which git 2>/dev/null || echo "not installed"
• CLAUDE.md: !find . -maxdepth 1 -name "CLAUDE.md" -type f
• project-discovery.md: !find . -maxdepth 3 -name "project-discovery.md" -type f
• personal config directory: !bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh" 2>/dev/null || echo "$HOME/.claude"
• project .han/config.md: !cat .han/config.md 2>/dev/null || echo ""

As your first action, use the Read tool on .han/config.md inside the personal config directory path above. A read that returns no file is no personal configuration: continue silently. When that file or the project .han/config.md probe supplies content, apply it per config-rule.md, which governs precedence between the two files, relative-path resolution, and what to do with a file that reads but cannot be used.

Operating Principles

Read these before dispatching anything. They constrain every step below.

• The entire repository is a valid scope. No focus area is required. If the user names a module or directory, restrict the analysis to it; otherwise, scope is the full repository. Do not ask for a focus area when none was given.
• Discovery and context model only. No migration plans. This skill characterizes what the domain structure is. It does not produce recommendations for service splits, migration steps, or refactoring work. When the context model surfaces a decision the team wants to act on, the right next step is han-planning:plan-a-feature (to specify a change) or han-coding:architectural-analysis (for a code-level view of a specific module). The context model informs those steps; it does not replace them.
• Analysis surface partitions organize agent work, not context proposals. The inventory step identifies tractable areas for structural and behavioral analysis. These partitions are analytical conveniences — they must never be mentioned in any agent brief as context candidates or suggested boundaries.
• The agents own the judgment; the skill orchestrates. The skill resolves scope, surveys the surface, classifies depth, briefs the agents, collects their output, and renders the report. It does not produce DL#, CAP#, OWN#, S#, B#, or BCM# findings itself.
• Negative results are valuable. A module with no domain events and only CRUD operations is evidence, not a gap. Agents must not fabricate findings to fill sections.
• Exactly one revision loop. The skill dispatches bounded-context-modeler, then bounded-context-critic, then bounded-context-modeler once more with the critique. The loop is closed after the revision pass. The skill does not self-escalate or repeat this cycle.
• The report template lives at references/ddd-analysis-report-template.md. The skill renders that template by synthesizing agent output into each section following the template's placeholder instructions. It does not invent a structure inline.
• The synthesized prose is written for a named reader. As the skill writes each section, it invokes han-communication:readability-guidance and applies the shared standard, holding one audience above the writing: the engineer or product manager reading the context model and deciding what to do next.

Run a DDD Analysis

Step 1: Resolve Scope and Load Project Context

Bind $size. If the user passed small, medium, or large as the first positional argument, bind $size to it. If $size is none provided and the project config supplies a default-swarm-size value via config-rule.md, adopt that value as $size and note the config as the source. If no value is available from either source, set $size to medium.

Resolve the focus area. Take the remaining argument and conversation context as the focus area. If a focus area was supplied, confirm it resolves to real files using Glob and Read. If it does not resolve to actual files, stop and ask the user to clarify before proceeding. If no focus area was supplied, the scope is the entire repository — do not ask for one.

Resolve project context. If CLAUDE.md is present (see Project Context), read its ## Project Discovery section for language, framework, and convention signals. Fall back to project-discovery.md if present. If neither exists, discovery agents will infer from surrounding code — note this in every agent brief.

Note git availability. Read the git installed value from Project Context. If it is empty or reads not installed, git is unavailable: note this in the agent briefs and in the report.

State the driving concern, if any. If the user named a concern ("I think billing and subscriptions overlap", "we need to understand where auth ends and identity begins"), capture it. Pass it to each discovery agent as a directing note — it biases attention without narrowing scope.

Resolve the run folder. Determine where this run's artifacts will land before dispatching any agent:

1. Configured output-directory: When the config read above supplied an output-directory, resolve it per config-rule.md (relative path resolves against the file that declared it; ~/ expands to home; full path is used as-is). Write the run folder beneath that resolved base.
2. No configured value: Write outside the repository — use ${TMPDIR:-/tmp} as the base. This keeps an unconfigured analysis out of version control, matching the same principle that governs code-overview.

Run date +%Y%m%d-%H%M%S via Bash to obtain a timestamp suffix. Name the run folder ddd-analysis-{timestamp} inside the resolved base. If that directory already exists, check for -2, -3 suffixes until the name is free. Run mkdir -p {run_folder}/discovery {run_folder}/synthesis to create the full directory tree. Set $run_folder to the absolute resolved path — this value threads through every subsequent step.

If the resolved base cannot be written (permission error, path does not exist), fall back to ${TMPDIR:-/tmp}/ddd-analysis-{timestamp} and record the failed path so Step 12 can name it.

Step 2: Inventory the Analysis Surface

Survey the repository structure within the resolved scope. Use Bash and Glob to identify:

• Top-level layout: the first two directory levels of the source tree (find {scope} -maxdepth 2 -type d | head -60)
• Entry points: files named main.*, index.*, app.*, server.*, cli.*, or bootstrap.*
• Persistence: directories and files named model, models, entity, entities, schema, schemas, migration, migrations, repository, repositories
• APIs and integrations: directories named routes, controllers, handlers, api, endpoints, resolvers, clients, adapters, connectors, external, integrations
• Jobs, workers, and events: directories and files containing job, worker, task, queue, consumer, subscriber, event, Event

Record the inventory as a compact list of paths — this is context for later briefs, not a finding.

For large repositories: If the resolved scope is the entire repository and the top-level source tree contains more than ten distinct application areas by directory, identify 3-5 meaningful partitions based on the directory structure and names (for example: billing, fulfillment, identity). Include these partitions in the han-core:structural-analyst and han-core:behavioral-analyst briefs in Step 4 to keep those analyses tractable. These partitions are not context candidates and must not be named as such.

Step 3: Classify Depth

Depth bands control the calibration directive passed to the discovery agents. They do not change which agents are dispatched; this skill always dispatches exactly five discovery agents.

• Small — surface scan. For language, capability, and ownership agents: highest-frequency terms, most obvious collisions, commands and domain events only, authority mapping and obvious contestation only. For structural and behavioral agents: top-level module boundaries and primary entry points only. Appropriate for an initial orientation pass or a very large repository.
• Medium (default) — full analysis. All dimensions for language, capability, and ownership agents; full static structure and behavioral analysis. Appropriate for most repositories.
• Large — exhaustive pass. All dimensions for language, capability, and ownership agents with emphasis on cross-module collision detection, workflow discovery, and contestation. For structural and behavioral agents: full analysis with emphasis on inter-module coupling, cross-module data flow, and integration boundaries. Appropriate for large, long-lived repositories or when the team needs a comprehensive model before a major architectural decision.

Step 4: Announce and Dispatch Discovery Agents

Announce in one line before dispatching:

Scope: {entire repository | focus area path}. Depth: {small | medium | large}. Dispatching han-ddd:domain-language-analyst, han-ddd:business-capability-analyst, han-ddd:domain-ownership-analyst, han-core:structural-analyst, and han-core:behavioral-analyst in parallel. Git {available | unavailable}.

State any driving concern the user supplied. Proceed without a blocking confirmation — this analysis is read-only and re-runnable.

Dispatch all five agents with concurrent Agent calls. Each agent reads the codebase independently.

Brief for han-ddd:domain-language-analyst:

• The resolved scope and the instruction to work across the full scope.
• The inventory summary from Step 2.
• Calibration: small — highest-frequency terms and most obvious collisions only; medium — full vocabulary inventory and all six dimensions; large — all six dimensions with emphasis on exhaustive cross-module semantic collision detection.
• The resolved project-context conventions, or a note that none were found.
• The driving concern, if any.
• Reminder: language-signal evidence only — no bounded-context proposals, no service-split recommendations.
• Ask the agent to prefix findings DL1, DL2, … exactly.
• Artifact path: write complete findings to $run_folder/discovery/domain-language.md and return only the path written, the total DL# count, and a two-sentence summary of the highest-value signals.

Brief for han-ddd:business-capability-analyst:

• The resolved scope and the instruction to work across the full scope.
• The inventory summary from Step 2.
• Calibration: small — commands and domain events only; medium — all six dimensions; large — all six dimensions with emphasis on cross-module workflow discovery and capability-boundary ambiguity.
• The resolved project-context conventions, or a note that none were found.
• The driving concern, if any.
• Reminder: capability evidence only — no bounded-context proposals, no service-split recommendations.
• Ask the agent to prefix findings CAP1, CAP2, … exactly.
• Artifact path: write complete findings to $run_folder/discovery/business-capabilities.md and return only the path written, the total CAP# count, and a two-sentence summary of the strongest behavioral signals.

Brief for han-ddd:domain-ownership-analyst:

• The resolved scope and the instruction to work across the full scope.
• The inventory summary from Step 2.
• Calibration: small — authority mapping and most obvious contestation signals only; medium — all six dimensions; large — all six dimensions with emphasis on cross-module consistency coupling and exhaustive contestation detection.
• The resolved project-context conventions, or a note that none were found.
• The driving concern, if any.
• Reminder: ownership evidence only — no bounded-context proposals, no service-split recommendations.
• Ask the agent to prefix findings OWN1, OWN2, … exactly.
• Artifact path: write complete findings to $run_folder/discovery/domain-ownership.md and return only the path written, the total OWN# count, and a two-sentence summary of the most significant authority signals.

Brief for han-core:structural-analyst:

• The resolved scope. If analysis partitions were identified in Step 2, include them and ask the agent to cover each partition in its analysis rather than the full repository tree.
• Calibration: small — top-level module boundaries and primary coupling patterns only; medium — full static structure analysis; large — full analysis with emphasis on inter-module coupling and duplication.
• Context that these findings will be used as structural evidence for strategic DDD analysis — no service-split or refactoring recommendations.
• Ask the agent to prefix findings S1, S2, … exactly.

Brief for han-core:behavioral-analyst:

• The resolved scope. If analysis partitions were identified in Step 2, include them and ask the agent to cover each partition in its analysis.
• Calibration: small — entry points and primary workflows only; medium — full behavioral analysis; large — full analysis with emphasis on cross-module data flow, error propagation, and integration boundaries.
• Context that these findings will be used as behavioral evidence for strategic DDD analysis — no refactoring recommendations.
• Ask the agent to prefix findings B1, B2, … exactly.

Wait for all five agents to return before proceeding.

Step 5: Persist Discovery Artifacts

The three han-ddd discovery agents have written their own artifacts and returned the paths. Use the Read tool to confirm each reported path exists. If an artifact is absent, note the shortfall — the skill continues without it, but Step 12 must name the missing file.

The han-core agents do not write their own artifacts. Write their verbatim returned output now:

• Write the structural-analyst's complete verbatim output to $run_folder/discovery/structural.md using the Write tool. Preserve every S# finding and its prefix exactly, including any "no findings" statements.
• Write the behavioral-analyst's complete verbatim output to $run_folder/discovery/behavioral.md using the Write tool. Preserve every B# finding and its prefix exactly, including any "no findings" statements.

The five discovery artifacts are now stable at:

• $run_folder/discovery/domain-language.md
• $run_folder/discovery/business-capabilities.md
• $run_folder/discovery/domain-ownership.md
• $run_folder/discovery/structural.md
• $run_folder/discovery/behavioral.md

Do not carry their contents transiently in context. All downstream stages read from these paths directly.

Step 6: Dispatch Bounded Context Modeler — First Pass

Dispatch han-ddd:bounded-context-modeler with one Agent call. The brief must contain:

• The five discovery artifact paths from Step 5. Instruct the agent to read each file with the Read tool before synthesizing — do not paste their contents into the brief.
• A calibration directive matched to the depth band: small — top 3-5 most strongly-evidenced CURRENT contexts; medium — all convergence zones supported by at least two independent evidence types; large — exhaustive, include all SPECULATIVE candidates with meaningful evidence from at least two types.
• Reminder: semantic context model only — no refactoring, migration, microservices, or topology recommendations.
• Synthesis artifact path: write complete output (all BCM# entries, IBN# entries, and the Bounded Context Model Summary) to $run_folder/synthesis/context-model-initial.md and return only the path written, the BCM# count by status, and the Bounded Context Model Summary.

Wait for the modeler to return. Capture the reported path as $context_model_initial. Use Read to verify the file was written. This is the first-pass model.

Step 7: Dispatch Bounded Context Critic

Dispatch han-ddd:bounded-context-critic with one Agent call. The brief must contain:

• The path $context_model_initial. Instruct the agent to read this file with the Read tool before evaluating.
• The five discovery artifact paths from Step 5. Instruct the agent to read each with the Read tool when verifying specific claims against evidence — do not paste their contents into the brief.
• Reminder: evaluation only — no context redesign, no replacement context map, no architectural or refactoring recommendations.
• Synthesis artifact path: write complete output (all BCR# entries and the Bounded Context Model Critique Summary) to $run_folder/synthesis/critique.md and return only the path written, the BCR# count with verdict distribution, and the Bounded Context Model Critique Summary.

Wait for the critic to return. Capture the reported path as $critique. Use Read to verify the file was written.

Step 8: Dispatch Bounded Context Modeler — Revision Pass

Dispatch han-ddd:bounded-context-modeler a second time with one Agent call. This is the final and only revision pass. The brief must contain:

• The path $context_model_initial. Instruct the agent to read this file with the Read tool.
• The path $critique. Instruct the agent to read this file with the Read tool.
• The five discovery artifact paths from Step 5. Instruct the agent to read any file it needs to verify synthesis claims — do not paste their contents into the brief.
• An explicit instruction: address every criticism the BCR# entries support with discovery evidence; reject every criticism that the BCR# entries acknowledge is unsupported or requires domain-expert input to resolve; preserve every BCM# entry the critic rated strong or plausible without modification unless the critique identified specific counter-evidence.
• Reminder: produce the final context model only — no refactoring, migration, or topology recommendations.
• Synthesis artifact path: write complete revised output (all BCM# entries, IBN# entries, and the Bounded Context Model Summary) to $run_folder/synthesis/context-model-final.md and return only the path written and a brief revision summary (what changed from the first pass and why).

Wait for the modeler to return. Capture the reported path as $context_model_final. Use Read to verify the file was written. The revision loop is now closed — do not dispatch the modeler or critic again.