Create Artifact (MCP-native storage) Skill

Create Artifact (MCP-native storage)

Register your deliverable using mcp__plugin_dh_backlog__artifact_register. This is the ONLY correct storage path for plan artifacts. Do NOT use Write to disk and do NOT return content inline.

Why disk writes and inline returns are wrong

Disk writes produce a file only accessible from the root worktree. Worktree-isolated agents and CI environments cannot reach ~/.dh/... paths via filesystem — they must use artifact_read(item_id, artifact_type) over MCP.
Inline returns are truncated by the task-notification summary when the agent runs as a background-dispatched task. The orchestrator receives a partial summary, not the full document.

MCP-native storage uploads content to a GitHub issue comment where any agent — regardless of worktree or environment — can retrieve it via artifact_read.

Correct invocation (verified against `backlog_core/server.py:2385`)

mcp__plugin_dh_backlog__artifact_register(
    item_id=<int>,                # GitHub issue number — REQUIRED
    artifact_type=<str>,          # Artifact type string — REQUIRED (see table below)
    artifact_id=<str>,            # Logical identifier — REQUIRED (see path format below)
    status="current",             # Lifecycle status: draft | current | superseded | archived
    agent=<str>,                  # Name of the producing agent (default: "")
    content=<str | None>,         # Full artifact content — include this to store in GitHub
)

Return value: dict with keys registered (bool), artifact_count (int), action ("added" or "updated"), content_stored (bool), messages, warnings. Check action in your STATUS: DONE report — do NOT paste the full content.

Parameters

`artifact_type`

One of the recognized type strings:

| artifact_type | Producing agent | When to use | |---|---|---| | feature-context | feature-researcher | Discovery document: WHO/WHAT/WHEN/WHY analysis | | codebase-analysis | codebase-analyzer | Codebase pattern/architecture/testing documents | | architect | python-cli-design-spec | Architecture spec with interfaces and contracts | | task-plan | swarm-task-planner | SAM task plan — auto-registered by sam_plan(action='create', issue=N), do NOT register manually | | T0-baseline | t0-baseline-capture | Pre-implementation baseline of acceptance criteria | | TN-verification | tn-verification-gate | Post-implementation verification results | | research | any research agent | Investigation findings, coverage analysis, rationale |

`artifact_id`

Logical identifier for the artifact. Two valid formats:

Repo-relative path for file artifacts that exist on disk in the root worktree: plan/feature-context-{slug}.md, plan/architect-{slug}.md
Logical id for artifacts that do NOT write a repo file: codebase-patterns-{slug}, codebase-architecture-{slug}, T0-baseline-{slug}, TN-verification-{slug}

Use a logical id (not a path) when the agent stores content via content= without writing a file to disk. Using a path that doesn't exist on disk causes a warning when content=None and is misleading to artifact consumers.

Do NOT use ~/.dh/... paths — these are MCP-server internals, not stable agent interfaces.

`content`

Pass the full markdown string. When content is provided, it is stored as a structured GitHub issue comment retrievable via artifact_read(item_id, artifact_type) from any environment.

When content is None: the server attempts to read a local file at artifact_id (resolved against the root worktree). If no file exists, a manifest-only entry is registered and a warning is emitted. For background-dispatched agents, always pass content= explicitly.

Examples by artifact type

feature-context

mcp__plugin_dh_backlog__artifact_register(
    item_id=1770,
    artifact_type="feature-context",
    artifact_id="plan/feature-context-my-feature.md",
    content=feature_context_markdown,
    agent="feature-researcher",
)

codebase-analysis (one call per focus area, logical id — no filesystem path)

mcp__plugin_dh_backlog__artifact_register(
    item_id=1770,
    artifact_type="codebase-analysis",
    artifact_id="codebase-patterns-my-feature",  # logical id: codebase-{focus}-{slug}
    content=patterns_markdown,
    agent="codebase-analyzer",
)

mcp__plugin_dh_backlog__artifact_register(
    item_id=1770,
    artifact_type="codebase-analysis",
    artifact_id="codebase-architecture-my-feature",  # logical id: codebase-{focus}-{slug}
    content=architecture_markdown,
    agent="codebase-analyzer",
)

architect

mcp__plugin_dh_backlog__artifact_register(
    item_id=1770,
    artifact_type="architect",
    artifact_id="plan/architect-my-feature.md",
    content=architect_markdown,
    agent="python-cli-design-spec",
)

task-plan

sam_plan(action='create', issue=N) auto-registers this artifact. Do NOT call artifact_register for task-plan — it creates a duplicate entry.

research (secondary documents, rationale, coverage analysis)

mcp__plugin_dh_backlog__artifact_register(
    item_id=1770,
    artifact_type="research",
    artifact_id="plan/swarm-rationale-my-feature.md",
    content=rationale_markdown,
    agent="swarm-task-planner",
)

STATUS: DONE report format

Do NOT paste the full document content. Report only:

STATUS: DONE
ARTIFACT: type={artifact_type}, action={action}, content_stored={content_stored}, chars={len(content)}

Include a <concerns> block if quality issues were found during the work.

Agent Skills: Create Artifact (MCP-native storage)

Install this agent skill to your local

Skill Files