Add more items (research objects) to an existing outline.yaml. Optionally launches a web-search subagent to suggest items in the topic's domain.
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tessl review fix ./plugins/ultra-research/skills/research-add-items/SKILL.md/ultra-research:research-add-items
Manual-only via disable-model-invocation: true. Run between /ultra-research:research and /ultra-research:research-deep whenever the initial item list missed something.
Phase 2a of 5 (optional). Edits outline.yaml in place. When finished, Step 4 uses AskUserQuestion to route the user into the next phase (re-run this skill, /ultra-research:research-add-fields, or /ultra-research:research-deep) with (Recommended) annotations based on coverage of the two YAML files.
Glob */outline.yaml in the current working directory and read it. If multiple match, ask which.
Use AskUserQuestion to ask both:
⛔ Dispatch-only. If web search is requested, the orchestrator MUST NOT call WebSearch, WebFetch, or mcp__exa_websearch__* directly. Instead, launch one web-search-agent subagent (background, via the harness's Agent/Task tool) with a prompt asking for additional items in the topic's domain, returning each as name: short description. The orchestrator's job is dispatch + read the subagent's structured return, nothing else.
name (case-insensitive)outline.yaml (preserve existing entries) and saveAfter saving, Read the updated outline.yaml and the sibling fields.yaml. Apply the thinness rubric:
| Check | Trigger for (Recommended) |
|---|---|
outline.yaml items count | Still < 8 items, OR no category field used → recommend Add more items (run this skill again) |
fields.yaml total fields | < 6 fields, OR only one field_categories entry, OR no field with detail_level: detailed → recommend Add more fields |
If neither file is thin, recommend Start deep research instead.
Use AskUserQuestion with header: "Next step" and these options (append (Recommended) to the label when the rubric flags it):
/ultra-research:research-add-items again/ultra-research:research-add-fields/ultra-research:research-deepIn the question body, briefly state why each recommendation was made. Invoke the picked skill immediately via the Skill tool.
{topic}/outline.yaml updated in place, with the appended items echoed back to the user.
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