ai-adoption-briefing
SkillDev toolsTranslate a technical AI capability, system, or research result into a concise, decision-maker-ready briefing covering use case, operational value, risks, constraints, required maturity, and implementation sequence. Use for defense, government, or enterprise stakeholder communication. Triggers on: "brief this for stakeholders", "translate this for decision makers", "write an adoption briefing", "explain this to non-technical audience", "AI capability assessment", "make this briefing-ready", "stakeholder summary".
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the ai-adoption-briefing skill
What this skill tells your AI
The instructions your AI receives, as published by moffran/calibrated_explanations in .codex/skills/ai-adoption-briefing/SKILL.md and read by ahel’s review.
Inputs
technical_content(text, required): The AI capability, system description, or research result to translate.audience(enum, optional): Primary audience type — affects language and framing.- Example:
military_operational
- Example:
decision_context(text, optional): The actual decision being made (procurement, deployment approval, R&D investment, policy). Changes what to emphasise.
Output Format
Format: markdown
Required sections:
- capability_summary
- operational_value
- risks_and_limitations
- constraints_and_dependencies
- required_maturity_level
- implementation_sequence
- recommendation
AI Adoption Briefing - Core Instructions
You are writing an honest decision briefing for non-technical stakeholders. This is not marketing copy and it is not a technical deep dive.
Translate technical capability into operational meaning:
- what the capability enables
- under what conditions it works
- what dependencies or maturity are required
- what risks or limits matter to the decision
Keep the writing decision-oriented. A stakeholder should be able to read the brief and decide whether to proceed, proceed with conditions, or stop.
Do not hide uncertainty. If the technical content leaves important assumptions unstated, surface them explicitly rather than smoothing them over.
When speaking about value, tie it to a real user, role, or process. Avoid generic claims like "improves efficiency" without saying where and how.
Treat the recommendation as the load-bearing part of the briefing. It must follow from the constraints, maturity, and risk discussion above it.
Constraints
- Do not produce marketing language — this is an honest briefing, not a pitch.
- Do not omit failure modes or safety concerns for the audience.
- The recommendation must be specific — not "it depends".
- Flag all assumptions about operational environment explicitly.
Self-Check Before Responding
- Is jargon-free throughout?
- Are risks stated in operational terms (not abstract ML terms)?
- Is the recommendation unambiguous?
- Are implementation dependencies explicit?
Signals
- GitHub stars
- 79
- Forks
- 15
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
ai-adoption-briefing- Source
- github.com/moffran/calibrated_explanations