llo-feedback

Collect structured archetype-aligned feedback from Local Lead Operators after Connect opportunity closeout.

1|2|Updated Apr 1, 2026
One-click install
npx skills add https://github.com/dimagi-internal/ace --skill llo-feedback-dimagi-internal
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: llo-feedback
Source: https://github.com/dimagi-internal/ace/tree/main/skills/llo-feedback
Command: npx skills add https://github.com/dimagi-internal/ace --skill llo-feedback-dimagi-internal

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the risk of irrelevant, thin, or unactionable feedback from Local Lead Operators (LLOs) after a Connect opportunity closes, which occurs when survey questions do not match the actual work the LLO performed (e.g., asking about app usability for a focus group that never used an app). It ensures closeout documentation includes structured, archetype-aligned insights that can be used to improve future opportunities.

Core Features & Use Cases

  • Archetype-Adaptive Feedback Requests: Automatically adjusts survey questions based on the opportunity's archetype (atomic-visit, focus-group, or multi-stage) to ask only relevant questions about the LLO's actual work.
  • End-to-End Feedback Workflow: Coordinates email outreach to LLOs, monitors for responses via OCS transcripts, and compiles all feedback into a standardized closeout document tagged by improvement area.
  • Use Case: After closing a multi-stage Connect opportunity with 3 LLOs, use this Skill to send each LLO a tailored survey asking about stage transition clarity, facilitation experience, and app usability (if applicable), then aggregate all responses into a single closeout file for the learnings-summary and cycle-grade skills to use.

Quick Start

Use the llo-feedback skill to collect and document structured post-opportunity feedback from all LLOs involved in your latest closed Connect opportunity.

Frequently Asked Questions about llo-feedback

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I collect structured feedback from operators after an opportunity closeout?

To collect structured operator feedback after opportunity closeout, use archetype-adaptive surveys that automatically adjust questions for atomic-visit, focus-group, or multi-stage projects. This eliminates irrelevant questions by only asking about the actual work performed, ensuring documentation contains actionable insights.

Why are my post-project surveys returning thin and unactionable responses?

Post-project surveys return thin, unactionable responses when generic questions fail to match the operator's actual work scope. Archetype-adaptive feedback requests solve this by tailoring survey questions to the specific opportunity archetype, ensuring all responses are relevant and structured for downstream aggregation.

What is the best way to monitor feedback responses during opportunity closeout?

The best way to monitor feedback responses during opportunity closeout involves coordinating email outreach to operators and tracking replies via OCS transcripts. This end-to-end workflow compiles all responses into a standardized closeout document tagged by improvement area.

Can I use archetype surveys for multi-stage Connect projects?

Yes, archetype surveys support multi-stage Connect projects by asking targeted questions about stage transition clarity, facilitation experience, and app usability. They adapt automatically to all Connect archetypes including atomic-visit and focus-group formats.

How do I tag LLO feedback by improvement area for downstream aggregation?

To tag LLO feedback by improvement area for downstream aggregation, compile all operator responses into a standardized closeout document. This tagging process organizes insights by archetype dimension, making them ready for learnings-summary and cycle-grade skills.

Do I need Google Drive to document opportunity closeout feedback?

Google Drive is utilized for closeout documentation of operator feedback. The standardized documentation compiles structured insights into a file format that downstream skills can easily access and aggregate for project improvement.