How do I install the Research Add Items skill?

Run npx skillmds@latest add julianobarbosa/research-add-items in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Research Add Items safe to use?

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Which AI agents work with Research Add Items?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Research Add Items free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Research Add Items?

julianobarbosa (@julianobarbosa) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Research Add Items by julianobarbosa

Name: Research Add Items
Rating: 4.2 (5 reviews)
Author: julianobarbosa

Research Add Items

Append new items (research objects) to an in-progress research outline's `outline.yaml` — sourced from your direct input, a web-search agent, or both. Use mid-`/research-outline` when you've realised the items list is incomplete (a new competitor surfaced, an important historical entry was missed, a category needs broader coverage) before running `/research-deep`, so the new items are part of the parallel deep pass instead of needing a separate run.

File contents

Research Add Items — Append to the Items List

In-place updates outline.yaml with additional research objects.

Trigger

/research-add-items

Pipeline position

/research-outline → ► /research-add-items ◄ → /research-deep → /research-report

Reachable any time after /research-outline has produced outline.yaml, but most useful before /research-deep so the new items get researched in the same parallel pass.

Workflow

Step 1 — Auto-locate outline

Glob */outline.yaml from the current working directory. Read it to know the existing items (avoid duplicates) and the current execution config.

Step 2 — Gather candidates from two sources

These two sources are gathered sequentially (AskUserQuestion blocks waiting on the user; web search can run in the background after). Do them in this order:

AskUserQuestion: "Which items do you want to add? Any specific names?" — capture whatever the user already has in mind.
AskUserQuestion: "Should I also run a web-search agent for more candidates?" — if yes, launch a research subagent via the Task tool (subagent_type: general-purpose, run_in_background: true) seeded with the topic, the existing items list, and any items the user named in step 1 (so suggestions don't duplicate).

Step 3 — Merge, deduplicate, confirm

Combine the user-supplied items and the subagent's suggestions.
Deduplicate by case-insensitive name match against the existing outline.yaml.
Show the merged candidate list to the user. AskUserQuestion for which to accept.
For each accepted item, capture: name (required), category (optional), description (optional, short).

Step 4 — Save update

Append accepted items to outline.yaml, preserving existing structure. Save in place.

Item shape (matches `/research-outline`)

items:
  - name: <item>
    category: <optional>
    description: <optional, short>

Output

Updated {topic}/outline.yaml — in-place modification, user confirms before save.

Gotchas

Duplicate detection is case-insensitive name match only. GPT-4 and GPT 4 (different by one character) are not detected as duplicates. Eyeball the merged list before saving if you suspect near-duplicates.
Items added after /research-deep has already run are not auto-researched. You either need to re-run /research-deep (its Step 2 resume check will skip already-completed items and only fan out the new ones), or research the new items manually.
The web-search subagent operates outside the conversation context. Hand it the topic AND the current items list explicitly, so its proposals don't repeat what's already there.
outline.yaml.execution.batch_size controls how many deep agents /research-deep runs in parallel. If you add many items at once, you may want to bump batch_size here too — but that's outside this skill's scope; edit outline.yaml directly or re-run /research-outline's Step 4 prompts.

---name: research-add-itemsdescription: Append new items (research objects) to an in-progress research outline's `outline.yaml` — sourced from your direct input, a web-search agent, or both. Use mid-`/research-outline` when you've realised the items list is incomplete (a new competitor surfaced, an important historical entry was missed, a category needs broader coverage) before running `/research-deep`, so the new items are part of the parallel deep pass instead of needing a separate run.---# Research Add Items — Append to the Items ListIn-place updates `outline.yaml` with additional research objects.## Trigger`/research-add-items`## Pipeline position```/research-outline → ► /research-add-items ◄ → /research-deep → /research-report```Reachable any time after `/research-outline` has produced `outline.yaml`, but most useful *before* `/research-deep` so the new items get researched in the same parallel pass.## Workflow### Step 1 — Auto-locate outline`Glob` `*/outline.yaml` from the current working directory. `Read` it to know the existing items (avoid duplicates) and the current `execution` config.### Step 2 — Gather candidates from two sourcesThese two sources are gathered sequentially (`AskUserQuestion` blocks waiting on the user; web search can run in the background after). Do them in this order:1. `AskUserQuestion`: "Which items do you want to add? Any specific names?" — capture whatever the user already has in mind.2. `AskUserQuestion`: "Should I also run a web-search agent for more candidates?" — if yes, launch a research subagent via the `Task` tool (`subagent_type: general-purpose`, `run_in_background: true`) seeded with the topic, the existing items list, and any items the user named in step 1 (so suggestions don't duplicate).### Step 3 — Merge, deduplicate, confirm- Combine the user-supplied items and the subagent's suggestions.- Deduplicate by case-insensitive name match against the existing `outline.yaml`.- Show the merged candidate list to the user. `AskUserQuestion` for which to accept.- For each accepted item, capture: `name` (required), `category` (optional), `description` (optional, short).### Step 4 — Save updateAppend accepted items to `outline.yaml`, preserving existing structure. Save in place.## Item shape (matches `/research-outline`)```yamlitems: - name: <item> category: <optional> description: <optional, short>```## OutputUpdated `{topic}/outline.yaml` — in-place modification, user confirms before save.## Gotchas- **Duplicate detection is case-insensitive name match only.** `GPT-4` and `GPT 4` (different by one character) are not detected as duplicates. Eyeball the merged list before saving if you suspect near-duplicates.- **Items added after `/research-deep` has already run are not auto-researched.** You either need to re-run `/research-deep` (its Step 2 resume check will skip already-completed items and only fan out the new ones), or research the new items manually.- **The web-search subagent operates outside the conversation context.** Hand it the topic AND the current items list explicitly, so its proposals don't repeat what's already there.- **`outline.yaml.execution.batch_size`** controls how many deep agents `/research-deep` runs in parallel. If you add many items at once, you may want to bump `batch_size` here too — but that's outside this skill's scope; edit `outline.yaml` directly or re-run `/research-outline`'s Step 4 prompts.

Frequently asked questions