workflow-research-entry — for Claude Code workflow-research-entry, community, for Claude Code, ide skills, Arguments: work_type =, work_unit =, topic =, (optional). Resolve topic: topic =, feature, resolved_filename = {topic}.md

v1.0.0

À propos de ce Skill

Scenario recommande : Ideal for AI agents that need this is phase 1 of the six-phase workflow:. Resume localise : A CLI that installs AI skills, agents, and hooks from git repos into projects, with multi-agent support and manifest-based tracking. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Fonctionnalités

This is Phase 1 of the six-phase workflow:
------------------- ---------------------------------------------------- --------
1. Research EXPLORE - ideas, feasibility, market, business ◀ HERE
2. Discussion WHAT and WHY - decisions, architecture, edge cases
3. Specification REFINE - validate into standalone spec

# Sujets clés

leeovery leeovery
[1]
[0]
Mis à jour: 3/21/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

Scenario recommande : Ideal for AI agents that need this is phase 1 of the six-phase workflow:. Resume localise : A CLI that installs AI skills, agents, and hooks from git repos into projects, with multi-agent support and manifest-based tracking. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Pourquoi utiliser cette compétence

Recommandation : workflow-research-entry helps agents this is phase 1 of the six-phase workflow:. A CLI that installs AI skills, agents, and hooks from git repos into projects, with multi-agent support and

Meilleur pour

Scenario recommande : Ideal for AI agents that need this is phase 1 of the six-phase workflow:.

Cas d'utilisation exploitables for workflow-research-entry

Cas d'usage : Applying This is Phase 1 of the six-phase workflow:
Cas d'usage : Applying ------------------- ---------------------------------------------------- --------
Cas d'usage : Applying 1. Research EXPLORE - ideas, feasibility, market, business ◀ HERE

! Sécurité et Limitations

  • Limitation : Do not act on gathered information until the skill is loaded - it contains the instructions for how to proceed
  • Limitation : Do not engage with the subject matter — your role is preparation, not processing
  • Limitation : ⚠️ ZERO OUTPUT RULE : Do not narrate your processing

About The Source

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Démo Labs

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ et étapes d’installation

These questions and steps mirror the structured data on this page for better search understanding.

? Questions fréquentes

Qu’est-ce que workflow-research-entry ?

Scenario recommande : Ideal for AI agents that need this is phase 1 of the six-phase workflow:. Resume localise : A CLI that installs AI skills, agents, and hooks from git repos into projects, with multi-agent support and manifest-based tracking. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Comment installer workflow-research-entry ?

Exécutez la commande : npx killer-skills add leeovery/agntc. Elle fonctionne avec Cursor, Windsurf, VS Code, Claude Code et plus de 19 autres IDE.

Quels sont les cas d’usage de workflow-research-entry ?

Les principaux cas d’usage incluent : Cas d'usage : Applying This is Phase 1 of the six-phase workflow:, Cas d'usage : Applying ------------------- ---------------------------------------------------- --------, Cas d'usage : Applying 1. Research EXPLORE - ideas, feasibility, market, business ◀ HERE.

Quels IDE sont compatibles avec workflow-research-entry ?

Cette skill est compatible avec Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Utilisez la CLI Killer-Skills pour une installation unifiée.

Y a-t-il des limites pour workflow-research-entry ?

Limitation : Do not act on gathered information until the skill is loaded - it contains the instructions for how to proceed. Limitation : Do not engage with the subject matter — your role is preparation, not processing. Limitation : ⚠️ ZERO OUTPUT RULE : Do not narrate your processing.

Comment installer ce skill

  1. 1. Ouvrir le terminal

    Ouvrez le terminal ou la ligne de commande dans le dossier du projet.

  2. 2. Lancer la commande d’installation

    Exécutez : npx killer-skills add leeovery/agntc. La CLI détectera automatiquement votre IDE ou votre agent et configurera la skill.

  3. 3. Commencer à utiliser le skill

    Le skill est maintenant actif. Votre agent IA peut utiliser workflow-research-entry immédiatement dans le projet.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

workflow-research-entry

A CLI that installs AI skills, agents, and hooks from git repos into projects, with multi-agent support and manifest-based tracking. This AI agent skill

SKILL.md
Readonly
Upstream Repository Material
The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

Act as precise intake coordinator. Follow each step literally without interpretation. Do not engage with the subject matter — your role is preparation, not processing.

⚠️ ZERO OUTPUT RULE: Do not narrate your processing. Produce no output until a step or reference file explicitly specifies display content. No "proceeding with...", no discovery summaries, no routing decisions, no transition text. Your first output must be content explicitly called for by the instructions.

Workflow Context

This is Phase 1 of the six-phase workflow:

PhaseFocusYou
1. ResearchEXPLORE - ideas, feasibility, market, business◀ HERE
2. DiscussionWHAT and WHY - decisions, architecture, edge cases
3. SpecificationREFINE - validate into standalone spec
4. PlanningHOW - phases, tasks, acceptance criteria
5. ImplementationDOING - tests first, then code
6. ReviewVALIDATING - check work against artifacts

Stay in your lane: Explore freely. This is the time for broad thinking, feasibility checks, and learning. Surface options and tradeoffs — don't make decisions. When a topic converges toward a conclusion, that's a signal it's ready for discussion phase, not a cue to start deciding. Park it and move on.


Instructions

Follow these steps EXACTLY as written. Do not skip steps or combine them. Present output using the EXACT format shown in examples - do not simplify or alter the formatting.

CRITICAL: This guidance is mandatory.

  • After each user interaction, STOP and wait for their response before proceeding
  • Never assume or anticipate user choices
  • Even if the user's initial prompt seems to answer a question, still confirm with them at the appropriate step
  • Complete each step fully before moving to the next
  • Do not act on gathered information until the skill is loaded - it contains the instructions for how to proceed

Step 1: Parse Arguments

Output the next fenced block as a code block:

── Parse Arguments ──────────────────────────────

Output the next fenced block as markdown (not a code block):

> Reading the handoff context and determining which
> research topic to work with.

Arguments: work_type = $0, work_unit = $1, topic = $2 (optional). Resolve topic: topic = $2, or if not provided and work_type is not epic, topic = $1.

Store work_unit for the handoff.

Resolve filename:

If work_type is feature

resolved_filename = {topic}.md

→ Proceed to Step 2.

If work_type is epic and topic resolved

resolved_filename = {topic}.md

→ Proceed to Step 2.

If work_type is epic and no topic

Deferred — gather-context will resolve it.

→ Proceed to Step 4.


Step 2: Check Phase Entry

Output the next fenced block as a code block:

── Check Phase Entry ────────────────────────────

Output the next fenced block as markdown (not a code block):

> Checking if research already exists for this topic.

Check if the research phase entry exists:

bash
1node .claude/skills/workflow-manifest/scripts/manifest.cjs exists {work_unit}.research.{topic}

If exists (true)

→ Proceed to Step 3.

If not exists (false)

→ Proceed to Step 4.


Step 3: Validate Phase

Output the next fenced block as a code block:

── Validate Phase ───────────────────────────────

Output the next fenced block as markdown (not a code block):

> Checking the status of this research — in progress
> or completed.

Load validate-phase.md and follow its instructions as written.

→ Proceed to Step 5.


Step 4: Gather Context

Output the next fenced block as a code block:

── Gather Context ───────────────────────────────

Output the next fenced block as markdown (not a code block):

> Collecting initial context to seed the research session.

If research context is already available in conversation

The caller already gathered context (idea description, motivation, constraints). Do not re-ask.

→ Proceed to Step 5.

Otherwise

Load gather-context.md and follow its instructions as written.

→ Proceed to Step 5.


Step 5: Invoke the Skill

Output the next fenced block as a code block:

── Invoke Research ──────────────────────────────

Output the next fenced block as markdown (not a code block):

> Handing off to the research process with all
> gathered context.

Load invoke-skill.md and follow its instructions as written.

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