add-new-feature

Add New Feature (SAM Workflow)

You MUST convert the user's request into durable SAM artifacts registered via MCP artifact and SAM tools:

• feature-context-{slug}.md (discovery)
• codebase/{FOCUS}.md (optional, analysis)
• architect-{slug}.md (architecture/design spec)
• P{NNN}-{slug}.yaml (executable task plan with Agents, deps, and verification)

<feature_request> $ARGUMENTS </feature_request>

---

Artifact Discovery (Pre-Phase)

Before starting any phase, check whether the feature request references a GitHub Issue. If an issue number is present, discover existing artifacts registered on that issue.

flowchart TD
    Start([Parse feature_request]) --> Q{Contains "GitHub Issue: #N"<br>or "Issue: #N" or "#N"?}
    Q -->|Yes — issue number found| List["Call artifact_list(issue_number=N)<br>to discover registered artifacts"]
    Q -->|No — no issue reference| Skip[Skip artifact discovery<br>Proceed normally]
    List --> Found{Artifacts returned?}
    Found -->|Yes| Store["Store artifact list as discovered_artifacts<br>Include paths and types in each<br>phase delegation prompt"]
    Found -->|No or empty| Skip
    Store --> Phase1([Proceed to Phase 1])
    Skip --> Phase1

When discovered_artifacts is non-empty, append this block to each phase delegation prompt:

<prior_artifacts>
The following artifacts are already registered for this issue. Read any relevant
ones via artifact_read(issue_number={issue}, artifact_type="{type}") before
starting your work — they contain prior research and analysis that should
inform your output.

{for each artifact: "- {artifact_type}: {path}"}
</prior_artifacts>

Research-type artifacts (artifact_type="research") are especially valuable — they contain investigation findings gathered before planning began. Phase agents should read these first when present.

---

Orchestrator Discipline

You are an orchestrator. You coordinate work across specialized agents. Prefer delegating discovery and analysis.

---

Shared Delegation Preamble

Every phase delegation prompt starts with this block. Fill {work_type}, {feature_name}, and {issue} from the values in the Template Variables section at the bottom of this skill.

You are part of a team that is currently working on the {work_type} {feature_name}.
Read the details about the milestone and plan you are a part of at backlog_view(selector="#{issue}").

<quality_vigilance>
Your task among all other things you are doing is to be consistently striving for
product quality improvements and aligning with the design intent. If you see something
that seems misaligned, verify it, and then note your concerns and findings concisely
in your response in a <concerns></concerns> block. Point out duplication, contradictions,
statements of fact without citation, code smells, missing documentation.
</quality_vigilance>

---

Plan Artifact Taxonomy

Plan artifacts are either human-decision (immutable — backlog items, grooming output, interview transcripts) or generated (mutable but intent-bound — feature context, architecture spec, task plan). Full taxonomy and divergence rules: Plan Artifact Lifecycle Policy.

---

Phase 1: Discovery (@dh:feature-researcher)

WHAT / WHY only. The feature-researcher produces problem space and desired outcome — not implementation approach. Output describes what is wanted and why; it does not prescribe how to build it.

Delegation prompt template:

You are part of a team that is currently working on the {work_type} {feature_name}.
Read the details about the milestone and plan you are a part of at backlog_view(selector="#{issue}").

<quality_vigilance>
Your task among all other things you are doing is to be consistently striving for
product quality improvements and aligning with the design intent. If you see something
that seems misaligned, verify it, and then note your concerns and findings concisely
in your response in a <concerns></concerns> block. Point out duplication, contradictions,
statements of fact without citation, code smells, missing documentation.
</quality_vigilance>

Research #{issue}: "{title}".
If research artifacts exist for this issue, read them via
artifact_read(issue_number={issue}, artifact_type="research") before starting
discovery — they contain prior investigation findings that should be incorporated.
Produce feature-context-{slug}.md content with WHAT/WHY analysis — problem space, desired
outcome, stakeholders, risks, open questions.
Return the full markdown content in your response. Do NOT write to disk.
Do NOT prescribe HOW to build it.
If the feature involves replacing or migrating a local module to an external tool,
you MUST perform a Replacement Coverage Analysis: enumerate all capabilities of the
local module, enumerate the replacement's capabilities, and produce a coverage matrix
(COVERED/PARTIAL/MISSING for each capability). Include the matrix in the feature-context
document. Surface any PARTIAL or MISSING capabilities as questions.

After the agent writes the feature-context file, register it as an artifact on the GitHub Issue:

mcp__plugin_dh_backlog__artifact_register(
    issue_number={issue},
    artifact_type="feature-context",
    path="plan/feature-context-{slug}.md",  # state-relative path
    agent="feature-researcher"
)

---

Phase 2: Codebase Analysis (@dh:codebase-analyzer)

WHAT exists today only. The codebase-analyzer maps existing patterns, conventions, and constraints — not proposed designs. Output describes what is there; it does not prescribe what to add or change.

If helpful, delegate to @dh:codebase-analyzer for one or more focus areas:

• patterns
• architecture
• testing
• conventions

Each focus area produces a markdown document. The agent returns the content in its response; the orchestrator then registers it as an artifact.

Delegation prompt template (one per focus area):

You are part of a team that is currently working on the {work_type} {feature_name}.
Read the details about the milestone and plan you are a part of at backlog_view(selector="#{issue}").

<quality_vigilance>
Your task among all other things you are doing is to be consistently striving for
product quality improvements and aligning with the design intent. If you see something
that seems misaligned, verify it, and then note your concerns and findings concisely
in your response in a <concerns></concerns> block. Point out duplication, contradictions,
statements of fact without citation, code smells, missing documentation.
</quality_vigilance>

Analyze {focus_area} for #{issue}: "{title}".
Produce {focus_area}.md content documenting what exists today — patterns,
conventions, constraints.
Return the full markdown content in your response. Do NOT write to disk.
Do NOT prescribe changes.

After the agent writes each codebase analysis file, register it as an artifact:

mcp__plugin_dh_backlog__artifact_register(
    issue_number={issue},
    artifact_type="codebase-analysis",
    path="plan/codebase/{FOCUS}.md",  # state-relative path
    agent="codebase-analyzer"
)

---

Phase 3: Architecture Spec (design-spec role)

HOW only. The design-spec agent designs the implementation approach — interfaces, data models, module boundaries, and call flows. Output prescribes structure and contracts; it does not re-describe the problem or re-map existing code.

Resolve the design-spec role from the language manifest before delegating:

flowchart TD
    Scan[Scan project root for language markers] --> Found{Marker found?}
    Found -->|pyproject.toml| Py[Search Python language manifest]
    Found -->|package.json| TS[Search TypeScript language manifest]
    Found -->|Cargo.toml| Rust[Search Rust language manifest]
    Found -->|None| FB[Fallback: dh:task-worker<br>(no specialist profile loaded)]
    Py --> ManifestFound{Manifest exists?}
    TS --> ManifestFound
    Rust --> ManifestFound
    ManifestFound -->|Yes| Resolve["Resolve design-spec role from manifest<br>(Python: @python3-development:python-cli-design-spec)"]
    ManifestFound -->|No| FB
    Resolve --> Delegate[Delegate to resolved agent]
    FB --> Delegate

Domain Signal Detection — Config-Driven (.dh/skill_discovery.yaml)

Before constructing the architect delegation prompt, populate {domain_skills} by reading the project's skill discovery configuration.

Step 1: Locate the config file

Check whether .dh/skill_discovery.yaml exists in the project root.

flowchart TD
    A{".dh/skill_discovery.yaml\nexists?"} -->|YES| B[Read and parse YAML]
    B --> C{Valid YAML?}
    C -->|NO| W["Emit warning\ndomain_skills = empty\ncontinue"]
    C -->|YES| D{"skill_discovery\nmode?"}
    D -->|off| E["domain_skills = empty"]
    D -->|auto| F["Collect always_use_skills\n+ evaluate skill_rules"]
    D -->|suggest| S["Collect always_use_skills\n+ evaluate skill_rules"]
    F --> G["Apply avoid_skills filter\nDe-duplicate\ndomain_skills populated"]
    S --> SN["Emit note listing\ncandidate skills"]
    SN --> SE["domain_skills = empty\n(no injection)"]
    SE --> G2["Continue to injection\ntemplate check\n(sees empty — skips)"]
    A -->|NO| I["Invoke wizard:\nSkill(skill='dh:setup-skill-discovery', args='--auto')"]
    I --> J{File written?}
    J -->|YES| B
    J -->|"WIZARD_DECLINED\nin output"| K["suggest mode fallback\ndomain_skills = []\ncontinue with note"]

Step 2: Collect {domain_skills}

When file is present, valid YAML, and skill_discovery mode is not off:

1. Add all always_use_skills entries unconditionally
2. For each skill_rules entry: evaluate when: using LLM reasoning against the feature_request text — if the condition is satisfied, add all skills in use: to the set
3. Remove any skills listed in avoid_skills
4. prefer_skills entries are tiebreaker advisory — do not add unconditionally
5. De-duplicate the collected set

Suggest mode: When skill_discovery mode is suggest, Steps 1–5 above still execute (so the candidate list is accurate), but the collected skills are emitted as a note to the user listing what would be injected — they are not actually injected. After emitting the note, set domain_skills to empty and continue to the injection template check (which sees empty and skips injection).

when: Evaluation Semantics — LLM Reasoning

Evaluate each when: field as a natural-language condition against the semantic content of the feature_request. The rule fires if the condition is unambiguously satisfied. Do not fire speculatively when uncertain.

Example: when: "involves Python, pytest, or uv" + feature about "pytest fixture for database" → fires

Example: same rule + feature about "dark mode toggle button" → does not fire

Step 3: Inject into architect prompt

If {domain_skills} is non-empty, prepend this block at the very top of the architect delegation prompt, before the shared preamble and before any artifact reads.

Ordering constraint when <prior_artifacts> is also present: the domain skill block MUST come first — it is a blocking prerequisite that must complete before any artifact reads occur. Place domain skills → then prior_artifacts → then the delegation prompt body. If {domain_skills} is empty, prior_artifacts placement is unchanged.

Wizard Invocation Protocol

When .dh/skill_discovery.yaml does not exist:

1. Inform the user: "No .dh/skill_discovery.yaml found. Running skill discovery wizard..."
2. Invoke: Skill(skill="dh:setup-skill-discovery", args="--auto") (programmatic context)
3. After wizard returns: re-check for the file (recurse once only)
4. If file still absent (WIZARD_DECLINED in wizard output): apply suggest-mode fallback

Suggest-mode fallback note:

Note: No .dh/skill_discovery.yaml configured. Skills that may be relevant: [judgment-based list based on feature_request content]. To configure permanently: /dh:setup-skill-discovery. Continuing without domain skill injection.

Set {domain_skills} to empty and continue without blocking.

Before starting design work, load these domain skills — they define the schemas, APIs, and delivery conventions required for this feature type:

{domain_skills formatted as Skill(skill="...") calls}

These are BLOCKING prerequisites — do not read any artifacts or write any design until all Skill() calls above have completed. Training data is not a substitute for live schema documentation loaded by these skills.

If {domain_skills} is empty, do not add any skill-loading block — proceed directly to the delegation prompt below without modification.

Delegation prompt template:

You are part of a team that is currently working on the {work_type} {feature_name}.
Read the details about the milestone and plan you are a part of at backlog_view(selector="#{issue}").

<quality_vigilance>
Your task among all other things you are doing is to be consistently striving for
product quality improvements and aligning with the design intent. If you see something
that seems misaligned, verify it, and then note your concerns and findings concisely
in your response in a <concerns></concerns> block. Point out duplication, contradictions,
statements of fact without citation, code smells, missing documentation.
</quality_vigilance>

Design the implementation for #{issue}: "{title}".
Read the feature context via artifact_read(issue_number={issue}, artifact_type="feature-context").
[If codebase analysis exists: Read via artifact_read(issue_number={issue}, artifact_type="codebase-analysis").]
If research artifacts exist for this issue, read them via
artifact_read(issue_number={issue}, artifact_type="research") for prior research
findings that should inform the architecture.
Produce architect-{slug}.md content with interfaces, contracts, data models, module boundaries.
Return the full markdown content in your response. Do NOT write to disk.
Do NOT implement — define WHAT to build, not the code.

After the agent writes the architect spec, register it as an artifact:

mcp__plugin_dh_backlog__artifact_register(
    issue_number={issue},
    artifact_type="architect",
    path="plan/architect-{slug}.md",  # state-relative path
    agent="python-cli-design-spec"
)

---

Phase 4: Task Decomposition (@dh:swarm-task-planner)

Delegate to @dh:swarm-task-planner to:

• create plan/P{NNN}-{slug}.yaml (via sam create)
• ensure every task has:
  • Status, Dependencies, Priority, Complexity, Agent
  • Acceptance Criteria (3+)
  • Verification Steps (3+)

Delegation prompt template:

You are part of a team that is currently working on the {work_type} {feature_name}.
Read the details about the milestone and plan you are a part of at backlog_view(selector="#{issue}").

<quality_vigilance>
Your task among all other things you are doing is to be consistently striving for
product quality improvements and aligning with the design intent. If you see something
that seems misaligned, verify it, and then note your concerns and findings concisely
in your response in a <concerns></concerns> block. Point out duplication, contradictions,
statements of fact without citation, code smells, missing documentation.
</quality_vigilance>

Decompose #{issue}: "{title}" into executable tasks.
Read the architecture spec via artifact_read(issue_number={issue}, artifact_type="architect").
Read the feature context via artifact_read(issue_number={issue}, artifact_type="feature-context").
Goal: {goal_from_feature_request}
Create the plan via sam_plan (deprecated: sam_create) with CLEAR+CoVe task definitions.

REQUIRED — skills field propagation:
The domain skills identified in Phase 3 are:
{domain_skills}

Every task in the generated plan MUST include a `skills` field populated with ALL of
these domain skills as a YAML list. Example:

  skills:
    - plugin-creator:hook-creator
    - plugin-creator:hooks-io-api

If `{domain_skills}` is empty (no domain signals were detected in Phase 3), omit the
`skills` field from all tasks — do not add an empty list.

The `skills` field is consumed by `implement-feature` to inject skill-loading instructions
into each implementation agent's prompt. Omitting it means implementation agents proceed
without domain schema context.

After the agent writes the task plan, register it as an artifact and write the plan path back to the backlog item:

mcp__plugin_dh_backlog__artifact_register(
    issue_number={issue},
    artifact_type="task-plan",
    path="plan/P{NNN}-{slug}.yaml",  # state-relative path; auto-registered by sam_plan (deprecated: sam_create) when issue is set
    agent="swarm-task-planner"
)

mcp__plugin_dh_backlog__backlog_update(
    selector="{title}",
    plan="P{NNN}"
)

The backlog_update(plan=...) call writes the plan address into the backlog item's metadata.plan field. This is a backend signal — it records that a plan exists and its address, not a filesystem path. work-backlog-item uses this to route directly to implement-feature on subsequent invocations. The SAM MCP resolves P{NNN} to the full plan without filesystem access.

---

Phase 5: Plan Validation Gate (@dh:plan-validator)

Delegation prompt template:

You are part of a team that is currently working on the {work_type} {feature_name}.
Read the details about the milestone and plan you are a part of at backlog_view(selector="#{issue}").

<quality_vigilance>
Your task among all other things you are doing is to be consistently striving for
product quality improvements and aligning with the design intent. If you see something
that seems misaligned, verify it, and then note your concerns and findings concisely
in your response in a <concerns></concerns> block. Point out duplication, contradictions,
statements of fact without citation, code smells, missing documentation.
</quality_vigilance>

Validate plan P{N} for #{issue}: "{title}".
Check: AC coverage, dependency DAG, agent assignments, verification steps,
impact radius coverage.
Return READY or BLOCKED with specific gaps.

If the validator returns BLOCKED, do not proceed to Phase 6. Fix the identified gaps and re-run Phase 4 before retrying Phase 5.

---

Phase 6: Context Manifest (@dh:dh-context-gathering)

Delegation prompt template:

You are part of a team that is currently working on the {work_type} {feature_name}.
Read the details about the milestone and plan you are a part of at backlog_view(selector="#{issue}").

<quality_vigilance>
Your task among all other things you are doing is to be consistently striving for
product quality improvements and aligning with the design intent. If you see something
that seems misaligned, verify it, and then note your concerns and findings concisely
in your response in a <concerns></concerns> block. Point out duplication, contradictions,
statements of fact without citation, code smells, missing documentation.
</quality_vigilance>

Add context manifest to plan P{N} for #{issue}: "{title}".
Read the plan via sam_plan (deprecated: sam_read). Write the context manifest via sam_plan (deprecated: sam_update).

---

Template Variables

Fill these values before constructing each delegation prompt. All values come from context already in scope — no pre-gathering required.

Variable	Source
{issue}	GitHub issue number from the backlog item or user request
{title}	GitHub issue title from backlog_view(selector="#{issue}")
{slug}	Kebab-case identifier derived from the issue title (e.g., agent-profile-mcp-tool)
{work_type}	"production of the feature" for new features; "fixing of an issue in" for bug fixes
{feature_name}	Human-readable feature name from the issue title
{focus_area}	One of: patterns, architecture, testing, conventions (Phase 2 only)
{goal_from_feature_request}	The one-sentence goal extracted from the feature context doc (Phase 4 only)
{domain_skills}	Pre-formatted YAML list lines (e.g., - plugin-creator:hook-creator) collected by the Phase 3 domain signal scan; empty string if no signals matched; passed verbatim into Phase 4 delegation prompt
{N}	SAM plan number returned by sam_plan (deprecated: sam_create) after Phase 4 completes

---

Success Outcome

When all phases complete, provide the user:

• the feature slug
• the task file path
• next step: run the implement-feature skill with the slug or task file path