Awesome-omni-skill research-leads

Research new capabilities and changes for tracked AI coding agents. Use this skill when assigned a research-leads issue to discover new features, or when asked to revise a research PR.

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/research-leads" ~/.claude/skills/diegosouzapw-awesome-omni-skill-research-leads && rm -rf "$T"
manifest: skills/data-ai/research-leads/SKILL.md
source content

Research Leads Skill

You proactively research developments for AI coding agents tracked in this repository.

Context

This repository tracks capabilities of these AI coding agents:

  • claude-code (Anthropic) — CLI agent for code generation, editing, debugging
  • copilot-cli (GitHub) — CLI companion for terminal workflows
  • gemini-cli (Google) — CLI agent powered by Gemini models
  • vscode-copilot (GitHub) — VS Code integrated AI coding assistant

Each agent's capabilities are stored in

agents/<name>/capabilities/current.json
.

Lead Types

Research these categories of changes:

  1. New capability — A feature not yet tracked (e.g. a new tool, mode, or integration)
  2. Changed capability — An existing feature that evolved significantly (renamed, expanded, restructured)
  3. Version / model release — A new version, model update, or platform change
  4. Deprecation notice — A feature being removed or replaced
  5. New agent — A new AI coding agent worth adding to the tracker

Confidence Levels

  • High — Official documentation or changelog explicitly confirms the change
  • Medium — Blog post, release notes, or credible announcement confirms it
  • Low — Inference from indirect evidence only (pre-uncheck these in the PR)

Research Mode

When assigned an issue labelled

research-leads
:

Step 1: Understand current state

Read each

agents/<name>/capabilities/current.json
to understand what's already tracked. Note capability names, descriptions, and source URLs.

Step 2: Research each agent

For each tracked agent, check:

  • Official documentation sites for new or restructured pages
  • Changelog / release notes for recent updates
  • GitHub releases (for open-source agents)
  • Product announcement blogs
  • Any other authoritative sources you find

Look for capabilities, features, or integrations not yet in the data.

Step 3: Create the PR

Open a PR with the actual proposed changes to

current.json
files in the diff.

PR description format — this IS the triage interface:

## Research Leads — YYYY-MM-DD

Uncheck items to skip. Edit inline to correct a source URL.
Comment `@copilot revise` after making changes.

---

### claude-code (N leads)
- [x] **New: <Capability Name>** · High confidence
      Source: <url>
      Action: Add entry to current.json

- [x] **Update: <Capability Name>** · Medium confidence
      Source: <url>
      Action: Update description / add source

- [ ] **New: <Capability Name>** · Low confidence
      Source: <url>
      Action: Add entry (excluded by default — check to include)

### gemini-cli (N leads)
...

### No leads
- vscode-copilot: No new developments found

Step 4: Validate

Before creating the PR, run:

python3 framework/scripts/validate_framework.py

Fix any errors before committing.

Revise Mode

When someone comments

@copilot revise
on a research PR:

  1. Re-read the PR description to see which items are checked/unchecked
  2. Remove commits/changes for unchecked items
  3. Apply any inline edits the reviewer made (corrected URLs, descriptions)
  4. Re-run
    python3 framework/scripts/validate_framework.py
  5. Push the updated commits

Schema Reference

Each capability entry in

current.json
must have:

{
  "category": "<one of: code-completion, code-generation, chat-assistance, code-explanation, code-refactoring, testing, debugging, documentation, command-line, multi-file-editing, context-awareness, language-support, ide-integration, api-integration, customization, security, performance, collaboration, model-selection, agent-orchestration, observability>",
  "name": "Capability Name",
  "description": "What this capability does, in 1-2 sentences",
  "available": true,
  "tier": "<free|pro|business|enterprise>",
  "maturityLevel": "<experimental|beta|stable|deprecated>",
  "status": "active",
  "sources": [
    {
      "url": "https://...",
      "description": "Brief source label",
      "verifiedDate": "YYYY-MM-DD",
      "sourceGranularity": "<dedicated|section|excerpt>",
      "excerpt": "Required 50-300 char verbatim quote when sourceGranularity is excerpt"
    }
  ]
}

Adding a New Agent

If you discover a new AI coding agent worth tracking:

  1. Create the directory:
    agents/<agent-name>/capabilities/
  2. Create
    current.json
    with the agent info and initial capabilities
  3. Create
    agents/<agent-name>/docs-registry.json
    listing authoritative doc sources
  4. Run
    python3 framework/scripts/validate_framework.py
    to confirm schema compliance
  5. Mark this lead as "New agent" in the PR description with Low confidence

Important Rules

  • NEVER fabricate capabilities. Every lead must cite a real, accessible source URL.
  • Verify all source URLs return HTTP 200:
    curl -Is URL | head -1
  • Pre-uncheck low-confidence leads so the reviewer must opt in.
  • When adding a source with
    sourceGranularity: "excerpt"
    , include a verbatim 50-300 character quote from the page.
  • Commit messages:
    research: add <capability> to <agent>
    or
    research: update <capability> for <agent>