Use agent-adoption-stage

Place an engineering team on the agentic-coding adoption ladder (0 gated → 4 intent-steered) using observables like agents-in-flight per engineer and who writes the…

Place an engineering team on the agentic-coding adoption ladder (0 gated → 4 intent-steered) using observables like agents-in-flight per engineer and who writes the code, name the one bottleneck gating the next step, prescribe the single next unlock, and state which current guardrail must change.

Reach for it when a leader asks "how far along are we with coding agents", "why has our AI adoption plateaued", or "what do we do next to get more out of this". Do NOT use to assess organisational preconditions like data, policy, and culture (that's ai-readiness-assessment) or to plan a tool's human rollout (that's ai-adoption-rollout).

Install it

npx skilldrop-cli install agent-adoption-stage                 # Claude Code, your user folder
npx skilldrop-cli install agent-adoption-stage --project       # Claude Code, this repo
npx skilldrop-cli install agent-adoption-stage --ide cursor    # or kiro, codex, antigravity, copilot

It is part of the AI engineering pack: --pack ai-engineering installs it with its neighbours.

Ask for it

Say it the way you would to a colleague. Requests like these route to it:

• "How far along are we with coding agents?"
• "Our AI adoption has plateaued — what's the next unlock?"
• "Which stage of agentic coding maturity is my team at?"
• "We want engineers running more agents — are we ready to scale that?"

Requests like these belong to a different skill, so it shouldn't answer them:

• "Assess whether our organisation's data and governance are ready for AI"
• "Plan the rollout cohorts and enablement for this tool"
• "Which AI use cases should we build first?"

If another skill answers instead, ask for this one by name: "use agent-adoption-stage".

Check what you get

A good result:

• Exactly one stage, cited to observables. No ranges, no "between 2 and 3" — and never a placement asserted from ambition or licence count.
• Placement is on the median engineer, with the distribution named when it's lopsided.
• Exactly one unlock. A list of ten is a backlog, and it guarantees none of them happens.
• The bottleneck is the one gating the next step, not a general complaint about AI.
• A guardrail to retire is named, not only one to add — stage-appropriateness cuts both ways.
• Every unlock routes to a concrete implementation, never to a product name.
• Vendor-neutral throughout. Capabilities, not SKUs; a skill that names this quarter's products is wrong by next quarter.
• The source framework is credited.

Use something else when

• Assessing organisational preconditions — data, policy ownership, incentives — that's ai-readiness-assessment, a different axis (a team can be governance-ready and stuck at stage 1).
• Planning how a tool reaches people — cohorts, enablement, comms — that's ai-adoption-rollout.
• Choosing which use cases to build — that's ai-use-case-triage.
• Reporting measured usage from telemetry — that's ai-usage-report.
• Designing one specific loop or fleet — that's agent-loop-design / subagent-design, which this skill routes to.

Next

• SKILL.md, the instructions the agent reads
• Its page on the site
• Related: agent-budget, agent-loop-design, agent-threat-model, agents-md-generator, ai-adoption-rollout, ai-readiness-assessment, ai-usage-report, ai-use-case-triage, devils-advocate, pre-merge-review, subagent-design

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