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Rai Planner

MicrosoftOfficialPublished by Microsoft

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  • Role or persona
  • Agent tooling
  • MIT
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From the author

RAI Planner identity protocol and on-demand reference pack covering Phase 1 capture, Phase 2 risk classification and the optional Mural board bootstrap, Phase 5 impact assessment, and Phase 6 review and backlog handoff.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

RAI Planner provides a modular framework for managing Responsible AI assessments. It uses a phased approach—ranging from initial capture and risk classification to impact assessment and final backlog handoff—to guide an agent through the lifecycle of an RAI review. By utilizing on-demand reference files, the skill ensures the agent maintains a compact state while accessing specific guidance, templates, and rubrics only when required for the current phase of the project.

What it does

  • Defines a formal identity and protocol for RAI assessment sessions.
  • Provides phase-specific guidance for scoping, risk classification, and impact assessment.
  • Supports the creation of Mural boards for collaborative team planning.
  • Includes a review rubric to ensure assessment quality before backlog generation.
  • Facilitates the generation of structured handoff summaries and work items.
  • Maintains session state and evidence registers across multiple project phases.

When to use it

  • At the start of any session to establish the RAI Planner identity and protocol.
  • When transitioning between project phases to load relevant coaching and assessment logic.
  • When performing risk classification to apply prohibited-use gates and depth-tier logic.
  • When conducting an impact assessment to build evidence registers and tradeoff logs.
  • When finalizing an assessment to generate a structured backlog handoff.

Advantages

  • Modular design keeps the agent's active context compact and efficient.
  • Ensures consistent application of RAI standards across different projects.
  • Provides clear exit criteria for each phase of the assessment.
  • Supports evidence-led, exploratory questioning techniques.
  • Standardizes the final output format for downstream review and implementation.

Usage examples

“Start a new RAI assessment for our customer support chatbot.”
The agent adopts the RAI Planner identity, loads the session start protocol, and begins Phase 1 by asking about the system's purpose and deployment model.
“We have finished the initial scoping. What is next?”
The agent recognizes the transition to Phase 2, loads the risk classification reference, and begins applying the prohibited-use gate and indicator assessment.
“Can we create a Mural board for this project?”
The agent loads the Mural bootstrap reference and offers to set up a collaborative board to visualize the current risk assessment.
“Generate the final handoff for the project.”
The agent enters Phase 6, applies the review rubric to the collected evidence, and produces a structured summary table of all project artifacts.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/rai-planner/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip rai-planner.zip -d .agents/skills

Documentation by Microsoft

RAI Planner

Overview

Use this skill as an on-demand reference pack for the RAI Planner. The main skill body stays compact, and the detailed phase guidance lives in the reference files under this folder so the agent can load only the material needed for the current phase.

Phase Reference Index

  • Session start — Planner identity: rai-identity.md — read this at the start of every session. It holds the RAI identity, the disclaimer and attribution protocol, the six-phase definitions, entry modes, state management, question cadence, resume protocol, error handling, the user-supplied reference content protocol, and the research activation contract.

  • Phase 1 — Capture and scoping: capture-coaching.md — read this when entering Phase 1 and again during capture turns when you need the exploration-first questioning techniques.

  • Phase 2 — Risk classification: risk-classification.md — read this when entering Phase 2 to apply the prohibited-use gate, indicator assessment, and depth-tier logic.

  • Phase 2 (optional) — Mural team board: mural-board-bootstrap.md — read this only when the user accepts the Phase 2 board offer or asks for a Mural board, and before any mural command.

  • Phase 5 — Impact assessment: impact-assessment.md — read this when entering Phase 5 to build the evidence register, tradeoff log, and work-item seeds.

  • Phase 6 — Review rubric and backlog handoff: backlog-handoff.md — read this when entering Phase 6 to run the review rubric and generate the final handoff summary.

Usage Notes

  • Read rai-identity.md at session start. Load the phase references on demand at phase boundaries rather than as fixed startup context.

  • Use the referenced file that matches the phase you are about to enter or the section you need to re-open during execution.

  • Keep the detailed rubric, templates, and evidence guidance in the reference files rather than in the main skill body.

Files

references/capture-coaching.md


description: Exploration-first capture mode coaching techniques for the RAI Planner

RAI Capture Mode Coaching

Use this note when entering Phase 1 or reopening capture-mode discussion. The goal is to keep the first interview exploratory, evidence-led, and grounded in the user's actual AI system context.

Capture Mode Purpose

Capture mode is the default starting posture for a fresh RAI assessment. It should surface:

  • system purpose and deployment model

  • model types, data inputs, and outputs

  • stakeholder roles and intended use contexts

  • out-of-scope, prohibited, or high-risk uses

  • output and reporting preferences for the final handoff

Exploration-First Stance

Apply these rules during capture:

  • Lead with open-ended questions rather than yes-or-no checks.

  • Ask about real workflows, not abstract AI terminology.

  • Surface assumptions as observations that the user can confirm or refine.

  • Prefer curiosity and context gathering over early judgments about risk or compliance.

Salient Risk Signals

Exploration-first does not mean staying silent about an obvious risk. When the user's description reveals a salient risk signal, name it in the same turn as an observation, record it in runningObservations with phase: 1, and say that formal classification follows in Phase 2. Then continue scoping. Signals that warrant naming include:

  • Consequential automated decisions about people, such as approvals, denials, or eligibility, without human review. Name it as a consequential-use risk and the missing human oversight.

  • A system that acts on instructions found in content it ingests, such as messages, documents, or web pages. Name it as prompt-injection exposure under the Secure and Resilient characteristic.

  • Different outcomes for equally qualified groups. Name it as a fairness concern under the Fair with Harmful Bias Managed characteristic.

Use the active framework's characteristic names. Set flagLevel to concern for a salient risk signal, to critical when the signal suggests a possible prohibited use or severe harm to people, and to noted for mitigating or neutral context. Do not assign a risk tier, a depth tier, or a compliance conclusion in Phase 1.

Proportionality applies in both directions. When the description shows mainly mitigating signals, such as internal-only use, no automated decisions about people, and human review before anything is shared, say that the early signals point to a low-risk profile and that Phase 2 screening will confirm it. Do not escalate a reviewed, internal tool toward a high-risk framing.

Question Cadence

Use a compact, balanced question set per turn:

  1. Start with one context-building question.

  2. Follow with one question about system behavior, data, or stakeholders.

  3. End with one question about output, deployment, or risk expectations.

  4. Use examples or default options when they help the user answer quickly.

Example Probes

  • "What is this AI system trying to accomplish for the user?"

  • "Where does the model receive input, and what outputs does it produce?"

  • "Who depends on the result, and where could harm or misuse occur?"

  • "What constraints, review steps, or deployment settings matter to this assessment?"

Capture-Phase Exit Criteria

Move on from capture when the user has confirmed the AI system scope, stakeholder context, and output preferences, and the session can proceed to Phase 2 with a stable summary.

Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/hve-core · .github/skills/project-planning/rai-planner · commit f425e94
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download rai-planner.zip