Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sananthanarayan/skilldrop --skill agent-adoption-stagegit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sananthanarayan/skilldrop/agent-adoption-stage)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/agent-adoption-stage"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/agent-adoption-stage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/agent-adoption-stage"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/agent-adoption-stage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00144 | $0.02142 |
| Opus 5 | $0.00072 | $0.01071 |
| Sonnet 5 | $0.00029 | $0.00428 |
| Haiku 4.5 | $0.00014 | $0.00214 |
Grade A, and why
agent-adoption-stage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-adoption-stage
Answer one question honestly: which rung is this team actually on, and what is the single next thing that moves them up? Not a scorecard, not a tool list — a placement backed by observables, the bottleneck that gates the next step, and one unlock.
Adapted from Boris Cherny's Steps of AI Adoption (2026-07-16). The stage names and the bottleneck-shift are his; the guardrail changes and the routing here are skilldrop's, and everything is stated as a capability rather than a product so it stays true across tools and across years.
The load-bearing idea: the bottleneck moves. Each stage is limited by something different, so a fix that unlocked the last step does nothing for the next one. Diagnosing the current bottleneck is the whole job; the stage number is just its index.
The ladder
| Stage | Shape | Observable | The bottleneck |
|---|---|---|---|
| 0 · Gated | no real access | tools unapproved, gateways slow, no path to run outputs | approval process — a legacy security posture optimising cost-per-token instead of outcomes |
| 1 · Assisted | you + one agent | ~1 agent, synchronous, you read nearly every change | your attention. Low trust and no self-verification, so you watch instead of moving on |
| 2 · Parallel | you orchestrate | ~5–10 agents, isolated checkouts, agent checks its own work first | review throughput. You're checking six streams instead of writing one |
| 3 · Supervised autonomy | manager of managers | ~100, agent writes nearly all code, agents start agents | trust in the loop, and token efficiency as volume climbs |
| 4 · Intent-steered | steer by intent | ~1000+, loop closed, monitor by exception | finding and automating the work, with the right guardrail per work type |
How to respond
- Place the team on exactly one stage, from observables. Ask for (or extract) three things: how many agents a typical engineer has in flight, who writes most of the code now, and what gets reviewed — every diff, final diffs, or exceptions. Quote the evidence. A range is a refusal — pick the stage the team is operating at, not the best day it ever had. Cap clarifying questions at 2.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 90 lines · 144 tokens per session scan A ec4d3a0cc4c7
agent-adoption-stage is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 25d ago), licensed MIT. It adds 144 tokens to every session and 2,142 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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