Researcher Rigor Gate
Use before plan submission, major plan revision, and major stage transitions. Verify alignment, feasibility, rigor, completeness, and prevent unjustified regressions to earlier workflow phases.
Best use case
Researcher Rigor Gate is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use before plan submission, major plan revision, and major stage transitions. Verify alignment, feasibility, rigor, completeness, and prevent unjustified regressions to earlier workflow phases.
Teams using Researcher Rigor Gate should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/researcher-rigor-gate/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Researcher Rigor Gate Compares
| Feature / Agent | Researcher Rigor Gate | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use before plan submission, major plan revision, and major stage transitions. Verify alignment, feasibility, rigor, completeness, and prevent unjustified regressions to earlier workflow phases.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Researcher Rigor Gate This skill is the main scientific quality gate for the automated research commander. ## Four Required Checks ### Alignment - Does the active plan actually match the user’s current confirmed goal? - Are outdated assumptions or rejected directions still leaking into the workflow? ### Feasibility - Are assignments compatible with the responsible worker roles? - Are resources, compute, and time nodes realistic? - Are dependencies satisfiable in the current state? ### Rigor - Are baselines, controls, metrics, and validation logic explicit enough to reproduce? - Are conclusions proportional to the available evidence? ### Completeness - Are any mandatory stages or handoffs missing? - Are there unaddressed blockers, contradictions, or risk items? ## Anti-Regression Rule Do not recommend broad early-stage restarts once the workflow has reached a later phase such as experiment design, implementation, execution, synthesis, or final reporting, unless: - a specific blocking gap is identified; - a narrower correction is not sufficient; and - the change is surfaced to the user as a material workflow revision. Prefer targeted supplementation over phrases like "go back to round 1 literature search."
Related Skills
Researcher Replan And Recovery
Use when the workflow hits contradictions, missing evidence, failed runs, design flaws, or resource shifts. Diagnose the failure class, choose the narrowest safe correction, and escalate to the user when the core plan changes.
Researcher Plan Architect
Use when the Researcher must convert a confirmed scientific goal into a staged, executable research plan with role assignments, milestones, resources, checkpoints, and risk controls.
Researcher Dispatch Supervisor
Use after the user confirms the plan. Dispatch the next justified worker task, supervise progress, enforce artifact-backed completion, and keep the workflow aligned with the approved plan.
Researcher Context Audit
Use when the Researcher starts, resumes, or reaches a major decision point. Build a context inventory from workstation materials, prior messages, existing artifacts, requirements, and unfinished work.
Researcher Ambiguity Gate
Use when the research goal, evaluation target, scope, resources, timeline, or decision criteria are ambiguous, conflicting, or not operationally testable.
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