agentleFS
Sign inStart free
alectivismalectivism/maven-template/.claude/skills/lab-summary/SKILL.md

lab-summary

Skill1 starsChanged 6 months agoMITView on GitHub

Summarize MMA Future Lab results (CAP, A3, ACE, SIFT, AURA, ARC) for execs, members, marketing, or detail. Use for "lab summary", "Future Lab brief".

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Ensure all data points are accurate — flag any numbers that seem outdated

    line 102

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Future Lab Summary Generator

Lab Reference Data

CAP (Consortium for AI Personalization)

  • What: AI-driven ad personalization at scale
  • Key metric: +160% average lift vs BAU
  • Scale: 29 experiments, 18 brands, 4 countries
  • Top results: Kroger (259% lift), AT&T (222%), GM, Home Depot, Uber, MLB
  • Current testing: Video/CTV, brand-lift, retail foot-traffic, D2C, Amazon/Twitch
  • Partner: Claritas / Rex Briggs
  • Cost: $35k + media spend
  • Maturity: Most mature lab, strongest evidence base

A3 (Agentic AI Advertising)

  • What: End-to-end AI video ad production (research → storyboard → final assets)
  • Key metric: Dramatic time/cost reduction in creative production
  • Scale: 2 pilots completed, recruiting waves 2-3
  • Partner: Monks (Monks.flow)
  • Cost: Free to participate
  • Maturity: Early stage, high interest

ACE (AI Creative Effectiveness)

  • What: Pre-launch creative scoring using historical performance patterns
  • Key features: Brand/platform compliance checking, edit suggestions
  • Partners: Vidmob, Celtra
  • Cost: Free to participate
  • Maturity: Active testing phase

SIFT (Semantic & Intent Fusion Targeting)

  • What: Contextual targeting using pre-computed LLM embeddings
  • Key metric: ~4B URL index, refreshed hourly; vector similarity at auction time
  • Partner: Cognitiv
  • Cost: Free to participate
  • Maturity: Technology proven, scaling

AURA (AI Uncovering Responsive Audiences)

  • What: AI-powered audience discovery
  • Key metrics: 300 campaigns analyzed, avg +24% lift, 90% showed incremental conversion improvement
  • Partner: Claritas / Rex Briggs
  • Cost: Free to participate
  • Maturity: Strong evidence, scaling

RAIL (Responsible AI Innovation Lab)

  • What: Governance frameworks and practical guides for responsible AI in marketing
  • Deliverables: 8 published guides (Implementation Checklist, Governance Framework, Risk Evaluation, Principles Template, Policy Template, Vendor Evaluation, Regulatory Landscape, Measuring AI Success)
  • Scale: 60+ leaders, 45+ companies
  • Maturity: Published, ongoing

ARC (AEO via Reddit Communities)

  • What: Tests how Reddit communities influence Answer Engine Optimization and branded LLM responses
  • Partner: Reddit
  • Status: Work in progress

Summary Formats

Executive Summary (for board, CMOs)

Length: 1 page max Structure:

  • Headline finding (lead with the number)
  • 3-bullet summary of methodology, scale, and result
  • Business implication in 1 sentence
  • Next steps / opportunity to participate

Member Brief (for newsletter, member comms)

Length: 300-500 words Structure:

  • Hook: why this matters for marketers
  • What was tested and with whom
  • Key findings with data
  • How members can participate or apply learnings
  • Link to full report

Marketing Material (for sales, recruitment)

Length: Varies Structure:

  • Bold headline stat
  • Visual-friendly data points
  • Testimonial or brand quote if available
  • CTA to join the lab

Detailed Research Summary (for research team, think tank)

Length: 1000-2000 words Structure:

  • Methodology
  • Sample details
  • Full results with statistical context
  • Limitations
  • Implications
  • Next steps

Process

  1. Identify which lab(s) to summarize
  2. Clarify the audience and format
  3. Gather latest data (check SharePoint Research site, recent communications)
  4. Draft summary in appropriate format
  5. Ensure all data points are accurate — flag any numbers that seem outdated
  6. Present for review

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free