/feedback -- Record stakeholder input

SkillDev tools

Record stakeholder input, new constraints, corrections, or direction changes.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the /feedback -- Record stakeholder input skill

What this skill tells your AI

The instructions your AI receives, as published by grainulation/grainulator in skills/feedback/SKILL.md and read by ahel’s review.

The user is relaying feedback from stakeholders that should be incorporated into the sprint.

Arguments

$ARGUMENTS

Instructions

  1. Parse the feedback: Identify what the stakeholder said. This could be:

    • A new constraint ("CTO says prioritize speed over cost")
    • A correction ("compliance says we need SOC2 Type II, not Type I")
    • A direction change ("skip the custom build, focus on Auth0 vs Clerk")
    • A new question ("what about latency in EU regions?")
  2. Create feedback claims as f### claims:

    • Type: constraint for hard requirements, feedback for opinions/preferences
    • Evidence tier: stated (stakeholder said it, not independently verified)
    • Tag with the stakeholder's name or role
  3. Check for conflicts: Does this feedback contradict existing claims? If a stakeholder says "budget is $10K max" but research shows a solution at $15K, that's a conflict. Set conflicts_with on both claims.

  4. Run grainulator.compile to surface any new conflicts.

  5. Print result:

    Feedback recorded:
    - <f001>: <summary>
    - <f002>: <summary>
    
    Conflicts introduced: <N>
    
    Auto
    
    - <authorized next action>
    
    Manual
    
    - <action requiring the user, or None.>
    

Host access

Use available grainulator MCP tools, passing the active sprint dir explicitly for evidence operations. If a tool is unavailable, use the local grainulator CLI (or node <checkout>/bin/grainulator.js). Read sibling skill files directly when slash commands are unavailable. Resolve template paths relative to this skill’s checkout when CLAUDE_PLUGIN_ROOT is unset. Optional external connectors are not required for local work; use local code, supplied documents, or available web tools. Do not write managed ledger files directly to bypass a missing MCP connection.

Next-step output

After a meaningful pass, use the current compiler's next_actions to present exactly two bullet lists labeled Auto and Manual. Auto is work the agent can continue under existing authorization. Manual is only work requiring the user's decision, access, or action. Classify using the current request and constraints; compiler suggestions never grant permission. Continue authorized Auto work without asking again.

Keep 2–3 useful actions total when available, use short concrete labels and commands where useful, and show None. for an empty group. Do not invent work to fill a quota. Never omit next steps merely because compilation is ready or the answer should be brief. Refresh stale compilation first and exclude work the user removed from scope. When the user asks only for next steps, output only these two lists: no findings recap, counts, reasons, or offer to continue.

Signals

GitHub stars
86
Forks
6
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
feedback-grainulation
Source
github.com/grainulation/grainulator