add-new-feature

name	add-new-feature
argument-hint	<feature description or existing doc path>
user-invocable	true
description	SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into executable task files.
compatibility	Python 3.11+
metadata	{"version":"1.0.0","last_updated":"2026-01-27"}

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{id}-{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 tracked issue — either a GitHub Issue (#N) or a beads issue ID (e.g. bd-a3f8). If an issue selector is present, discover existing artifacts registered on that issue.

flowchart TD
    Start([Parse feature_request]) --> Q{"Contains 'GitHub Issue: #N' / '#N'<br>OR 'Beads Issue: bd-ID' / bare beads ID?"}
    Q -->|Yes — issue selector found| List["Call artifact_list(item_id=N)<br>to discover registered artifacts<br>(N may be int for GitHub or str for beads)"]
    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(item_id={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.

Research artifacts as discovery pointers (not authoritative sources): Research entries record summary findings and upstream source references — they are NOT the primary source. Research artifacts are discovery pointers, not authoritative documents. The YAML frontmatter of a research artifact may contain resource_url and/or github_url fields pointing to the upstream content. Phase agents receiving a research artifact MUST check these fields and fetch the upstream source before adapting content from the summary. This is especially important for Phase 1 (feature-researcher) and Phase 3 (architect). Failure to fetch the primary source causes information loss from the summary layer to propagate into feature context and architecture decisions.

---

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.

The {quality_vigilance} template variable used in every phase delegation template below expands to the <quality_vigilance> block defined here — this is the single canonical definition.

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.

For claims derived from research artifacts: verify the claim against the upstream
primary source, not just the local research summary. If the research artifact frontmatter
contains `resource_url` or `github_url`, fetch that URL and check the claim against it.
Cite the fetched primary URL, not the research summary file path, when the claim originates
from external upstream content.
</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}

Research #{issue}: "{title}".
If research artifacts exist for this issue, read them via
artifact_read(item_id={issue}, artifact_type="research") before starting
discovery — they contain prior investigation findings that should be incorporated.
IMPORTANT: Research artifacts are discovery pointers, not authoritative documents.
After reading the research artifact, inspect its YAML frontmatter for `resource_url`
and `github_url` fields. If either is present, fetch the upstream source using
mcp__Ref__ref_read_url (or WebFetch as fallback) and treat it as the authoritative
primary source. Use the local research summary as an index to the primary source only.
If the URL is absent or unreachable (4xx/5xx/timeout), log a warning in the
`## Research Source Notes` section of the feature-context document and proceed from the
local summary — never hard-fail.
Produce feature-context-{slug}.md content with WHAT/WHY analysis — problem space, desired
outcome, stakeholders, risks, open questions.
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.

Register your deliverable and return:

1. Call `artifact_register` with the full feature-context content:

       mcp__plugin_dh_backlog__artifact_register(
           item_id={issue},
           artifact_type="feature-context",
           artifact_id="plan/feature-context-{slug}.md",
           content="<full feature-context markdown>",
           agent="feature-researcher"
       )

2. Return:

       STATUS: DONE
       path: plan/feature-context-{slug}.md

After the agent completes, verify the artifact was registered:

mcp__plugin_dh_backlog__artifact_list(item_id={issue}, artifact_type="feature-context")

If count == 0, the agent did not register the artifact. Re-dispatch with an explicit reminder that artifact_register(content=...) is the agent's responsibility, not the orchestrator's. The orchestrator MUST NOT call artifact_register as a workaround — the MCP-native rule is that agents own their artifact storage.

---

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 self-registers each document via artifact_register(content=...) and returns only STATUS: DONE with the artifact ID(s).

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}

Analyze {focus_area} for #{issue}: "{title}".
Produce {focus_area}.md content documenting what exists today — patterns,
conventions, constraints.
Do NOT prescribe changes.

Register each document and return:

1. Call `artifact_register` with the full codebase analysis content for each focus area:

       mcp__plugin_dh_backlog__artifact_register(
           item_id={issue},
           artifact_type="codebase-analysis",
           artifact_id="plan/codebase-{focus}-{slug}.md",
           content="<full codebase analysis markdown>",
           agent="codebase-analyzer"
       )

   Note: A single invocation covering multiple focus areas issues one `artifact_register` call per
   focus area with a distinct `artifact_id` per focus.

2. Return:

       STATUS: DONE
       path: plan/codebase-{focus}-{slug}.md

After the agent completes, verify the artifact was registered:

mcp__plugin_dh_backlog__artifact_list(item_id={issue}, artifact_type="codebase-analysis")

If count == 0, the agent did not register the artifact. Re-dispatch with an explicit reminder that artifact_register(content=...) is the agent's responsibility, not the orchestrator's. The orchestrator MUST NOT call artifact_register as a workaround — the MCP-native rule is that agents own their artifact storage.

---

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 example: python-engineering:python-cli-design-spec)"]
    ManifestFound -->|No| FB["Fallback: dispatch dh:task-worker<br>no specialist profile loaded"]
    Resolve --> Store["Store as {resolved_agent}<br>profile_load(agent_name='{resolved_agent}') in delegation prompt"]
    Store --> Delegate["Dispatch subagent_type='dh:task-worker'"]
    FB --> Delegate

Phase 3 always dispatches subagent_type="dh:task-worker". When a specialist is resolved from the language manifest, the orchestrator instructs task-worker to call mcp__plugin_dh_backlog__profile_load(agent_name="{resolved_agent}") at the start of its prompt — this is the agent_profile MCP tool on the backlog server and is how task-worker loads specialist behavior when no SAM task agent: field is available. Use {resolved_agent} as the agent= metadata in artifact_register to record which specialist produced the spec.

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.

Dispatch: subagent_type="dh:task-worker". Build the delegation prompt from the template below.

Delegation prompt template:

{specialist_skill_block}
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}

Design the implementation for #{issue}: "{title}".
Read the feature context via artifact_read(item_id={issue}, artifact_type="feature-context").
[If codebase analysis exists: Read via artifact_read(item_id={issue}, artifact_type="codebase-analysis").]
If research artifacts exist for this issue, read them via
artifact_read(item_id={issue}, artifact_type="research") for prior research
findings that should inform the architecture.
IMPORTANT: Research artifacts are discovery pointers, not authoritative documents.
After reading any research artifact, inspect its YAML frontmatter for `resource_url`
and `github_url` fields. If either is present, fetch the upstream source using
mcp__Ref__ref_read_url (or WebFetch as fallback) and verify currency and completeness
of the research summary against the primary source before designing. If the URL is
absent or unreachable (4xx/5xx/timeout), log a warning in the `## Research Source Notes`
section of the architect document and proceed from the research summary — never hard-fail.
Additionally, run a WebSearch for current best practices relevant to this feature before
finalizing the architecture. Training data is stale; current community practice may differ.
Produce architect-{slug}.md content with interfaces, contracts, data models, module boundaries.
Do NOT implement — define WHAT to build, not the code.

Register your deliverable and return:

1. Call `artifact_register` with the full spec content:

       mcp__plugin_dh_backlog__artifact_register(
           item_id={issue},
           artifact_type="architect",
           artifact_id="plan/architect-{slug}.md",
           content="<full spec markdown>",
           agent="{resolved_agent}"
       )

2. Return:

       STATUS: DONE
       path: plan/architect-{slug}.md

{specialist_skill_block} is built by the orchestrator before dispatch:

• When {resolved_agent} is set (manifest found): "Load your specialist profile before starting: mcp__plugin_dh_backlog__profile_load(agent_name='{resolved_agent}'). This is a BLOCKING prerequisite — complete it before reading any artifacts or designing.\n\n"
• When no manifest found (fallback): "" (empty string — task-worker executes directly without a specialist profile)

After the agent completes, verify the artifact was registered:

mcp__plugin_dh_backlog__artifact_list(item_id={issue}, artifact_type="architect")

If count == 0, the agent did not register the artifact. Re-dispatch with an explicit reminder that the agent must call artifact_register(content=...) itself — the orchestrator MUST NOT call artifact_register as a workaround. The MCP-native rule is that agents own their artifact storage.

---

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

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

• create plan/P{id}-{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}

Decompose #{issue}: "{title}" into executable tasks.
Read the architecture spec via artifact_read(item_id={issue}, artifact_type="architect").
Read the feature context via artifact_read(item_id={issue}, artifact_type="feature-context").
Create the plan via sam_plan 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.

Linking the plan to the backlog item is a step of add-new-feature itself, run in the same context that read this file — not part of the swarm-task-planner delegation above. Everything sent to that subagent lives inside the fenced delegation prompt block above; this step sits outside it. The swarm-task-planner never reads this file, so it cannot perform this step — a self-report such as "linked to issue #N" is not evidence the link exists. Confirm the link by reading state, not by trusting a report:

# 1. Read current state — plan_address is null until the link is written
mcp__plugin_dh_backlog__backlog_view(selector="#{issue}", summary=True)

# 2. If plan_address is null, write the link
mcp__plugin_dh_backlog__backlog_update(
    selector="{title}",
    plan="P{id}"
)

# 3. Re-read and confirm plan_address is non-null before proceeding to Phase 5
mcp__plugin_dh_backlog__backlog_view(selector="#{issue}", summary=True)

Note: sam_plan(action='create', issue={issue}) already auto-registers the task-plan artifact. Do NOT call artifact_register for the task-plan type — it is redundant and would create a duplicate entry.

The backlog_update(plan=...) call writes the plan address into the backlog item's metadata.plan field, surfaced by backlog_view(summary=True) as plan_address. This is a backend signal — it records that a plan exists and its address, not a filesystem path. work-backlog-item reads plan_address to route directly to implement-feature on subsequent invocations. The SAM MCP resolves P{id} 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}

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}

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

---

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}	Issue selector — GitHub issue number (int) or beads ID string (e.g. bd-a3f8); no type coercion
{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)
{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 after Phase 4 completes
{quality_vigilance}	Full <quality_vigilance>...</quality_vigilance> block — canonical text defined in §Shared Delegation Preamble above; substitute verbatim when constructing delegation prompts

---

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

---

Discovered During Implementation

[Date: 2026-05-23 / Session: issue #1527]

Architect specs routinely exceed 32KB. A real-world architect spec for a non-trivial feature can reach 32KB or more of markdown. The problem with the pre-#1527 pattern was that the orchestrator received the content inline in the agent response — the JSONL session output grew to 300KB or more, which the orchestrator cannot process. The fix is for the architect agent to call artifact_register(content=...) directly: the content stays within the agent's context window and is uploaded to the artifact backend (GitHub Gist); the orchestrator receives only STATUS: DONE. No Write tool step is needed or correct.

artifact_register without content= is a prohibited pattern. Calling artifact_register with a path but no content= stores only a pointer to a local file. That file is unreachable from worktree-isolated agents, CI environments, and any other machine. Always pass content= explicitly. Source: plugins/development-harness/CLAUDE.md — "Prohibited patterns" section.

Phase 4 (sam_plan(action='create')) auto-registers the task-plan artifact — no separate artifact_register call is needed or valid for that type.