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 modest-curator478/claude-skills --skill gtm-ai-adoption-rolloutgit clone --depth 1 https://github.com/modest-curator478/claude-skillsWrote 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/modest-curator478/claude-skills/gtm-ai-adoption-rollout)<a href="https://agentmods.dev/skills/modest-curator478/claude-skills/gtm-ai-adoption-rollout"><img src="https://agentmods.dev/badge/skills/modest-curator478/claude-skills/gtm-ai-adoption-rollout/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/modest-curator478/claude-skills/gtm-ai-adoption-rollout"><img src="https://agentmods.dev/badge/skills/modest-curator478/claude-skills/gtm-ai-adoption-rollout.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 | $0.00203 | $0.02730 |
| Opus 5 | $0.00102 | $0.01365 |
| Sonnet 5 | $0.00041 | $0.00546 |
| Haiku 4.5 | $0.00020 | $0.00273 |
Grade A, and why
gtm-ai-adoption-rollout 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 12d 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.
This is a copy
100% identical to gtm-ai-adoption-rollout — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GTM AI Adoption Rollout: Getting Sales and Marketing Teams to Actually Use New Tools
This skill addresses the gap between buying a tool and the team actually using it. Most rollouts fail not because the tool is wrong but because the adoption plan treats it as a training problem rather than a behavior change problem. These are different problems with different solutions.
HOW TO SET UP THIS SKILL
This skill works out of the box. For more specific output, provide:
- The tool or tools being rolled out
- The team size and composition (reps, SDRs, marketers, managers)
- Whether this is leadership-mandated or a bottoms-up initiative
- What the team is currently using and being asked to change from
- What has already been tried and failed
- Your timeline and any hard deadlines
Why GTM Tool Rollouts Fail
Before building a rollout plan, diagnose which failure mode applies. Most failed rollouts trace to one of five root causes.
Failure mode 1: The tool solves a problem leadership has, not one the team has
Leadership buys a tool to get pipeline visibility or forecast accuracy. The rep sees it as more data entry with no personal benefit. Adoption fails because the incentive structure is inverted. The tool asks reps to give something (their time, their process) in exchange for something they do not personally receive (a dashboard the VP uses).
Fix: Identify one thing the tool does that makes the rep's job easier, not just the manager's job more visible. Lead the rollout with that use case. If no such use case exists, the tool has an adoption problem that no training program will solve.
Failure mode 2: Training was a one-time event, not a reinforcement system
The team attended a two-hour kickoff session, got a login, and was left to figure it out. Within two weeks, 80% reverted to the old workflow.
Fix: Adoption is not a training event. It is a 90-day behavior change program. The kickoff session gets people started. The first 30 days builds the habit. Days 31-60 reinforces it under real conditions. Days 61-90 standardizes it and removes the workarounds. Each phase requires different interventions.
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.
- 12d ago First seen · 307 lines · 203 tokens per session scan A 5d0b84a9f1e7
gtm-ai-adoption-rollout is a skill published in the GitHub repository modest-curator478/claude-skills (4 stars, last pushed yesterday), licensed MIT. It adds 203 tokens to every session and 2,730 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gtm-ai-adoption-rollout, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
TEMPLATE
../../../agents/personas/TEMPLATE.md.
code-review
Pre-commit code review using code-reviewer agent for bug detection, security analysis, and quality assurance. Manual invocation only. Use before git commits to catch issues early. Blocks commits on REQUESTCHANGES verdict.
debug
Systematic debugging using error-detective and debugger agents instead of manual investigation. Triggers automatically on: error, bug, broken, failing, exception, stack trace, test failure. Orchestrates RCA-first approach with parallel agent investigation.
deep-context
Build deep codebase understanding using Capsule context, progressive-reader, and specialist agents instead of overwhelming main context. Triggers on: don't have context, understand codebase, learn about, need background. Implements progressive context building.
workflow
Systematic task orchestration for complex multi-step tasks. Triggers automatically when detecting: complex task, multi-step work, coordinate, orchestrate, break down. Guides through Understand → Strategy → Plan → Execute → Verify phases for comprehensive systematic approach.
task-router
Decision matrix for choosing optimal approach to any task. Helps Claude decide when to delegate to sub-agents vs. working directly. Use when starting a new task or unsure of best approach.