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 zime-ai/zime-gtm-skills --skill adoption-leaderboardgit clone --depth 1 https://github.com/zime-ai/zime-gtm-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/zime-ai/zime-gtm-skills/adoption-leaderboard)<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/adoption-leaderboard"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/adoption-leaderboard/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/zime-ai/zime-gtm-skills/adoption-leaderboard"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/adoption-leaderboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 59 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00096 | $0.01954 |
| Opus 5 | $0.00048 | $0.00977 |
| Sonnet 5 | $0.00019 | $0.00391 |
| Haiku 4.5 | $0.00010 | $0.00195 |
Grade A, and why
adoption-leaderboard 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Behavior Adoption Leaderboard
You are a coaching-evidence analyst. Score a set of recent sales calls
against five fixed behavior checklists (references/behavior-checklists.md)
and rank reps by adoption, lowest first — the reps most in need of coaching
lead the report, not trail it.
Run this end to end in one pass. Don't stop to ask which calls to include, who's internal, or how to interpret an ambiguous call — apply the default rule in the relevant step below, decide it yourself, and note the assumption once. The user can correct any assumption after seeing the leaderboard; that's a quick re-run, not a precondition for the first one.
When to use this
- Prepping a 1:1 or team coaching session from call evidence instead of manager impression.
- Running a team calibration: which behaviors is the team actually landing, and which reps most need attention.
- Checking whether a specific behavior (e.g. rapport-building, surfacing renewal risk) is landing consistently across a book of calls, not just on the calls a manager happened to listen to.
If .agents/gtm-context.md (or .claude/gtm-context.md) exists, read it
first and don't ask for anything it already answers.
Step 1: Choose an input source
Two modes. Neither is the "real" one — use whichever the user has.
Connector mode — if this conversation has tools that can (a) list or search meetings/calls and (b) return call transcripts, use them. Match by capability, not by brand or vendor: any pair of list-calls + get-transcript tools works, whatever the source is called. If several are connected, prefer the one with organization-wide coverage and speaker emails on calls; say which one you picked and why. Verify the choice with one cheap call: list a single recent meeting before proceeding.
Local mode — if no such tools are present, or the user points at a
directory instead, read transcript files directly:
.txt/.vtt/.json/.md, same formats every other skill in this repo
accepts. Speaker labels carry attribution when the source provides them;
where they don't (generic "Speaker 1" labels), infer rep vs. external
participant from context and state the inference once — see Step 3's edge
handling.
What ships with it
9 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.
- assets/call-01-jordan-meridian.txt 914 B
- assets/call-02-jordan-lakeview.txt 915 B
- assets/call-03-amara-northbridge.txt 1.4 KB
- assets/call-04-amara-crestpoint.txt 836 B
- assets/call-05-diego-solvantfin.txt 1.9 KB
- assets/call-06-sam-brightwell.txt 797 B
- evals/evals.json 4.8 KB
- references/behavior-checklists.md 4.4 KB
- references/scoring.md 2.7 KB
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 · 169 lines · 96 tokens per session scan A eb66bc4200ff
adoption-leaderboard is a skill published in the GitHub repository zime-ai/zime-gtm-skills (14 stars, last pushed 17d ago), licensed MIT. It adds 96 tokens to every session and 1,954 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
go-to-market-playbook
A reusable Go-to-Market strategy template for both B2B and B2C launches. Covers positioning, messaging, ICP definition, channel selection, and competitive analysis frameworks. By @WeiYipei.
category-point-of-view
Create a differentiated B2B category point of view that leads with the customer problem, defines the market shift, names the category or strategic frame, and turns it into content, distribution, and measurement guidance.
b2b-pmm-orchestrator
Route vague B2B product marketing requests to the right PMM skill, sequence multiple skills into intelligent GTM workflows, and keep the agent focused on the smallest useful artifact that moves the business forward.
ai-pmm-reviewer
Review AI-generated B2B marketing and PMM drafts for strategic sharpness, customer truth, positioning quality, plain English, and AI tells; diagnose gaps and rewrite only where judgment is clear.
customer-story-engine
Capture true customer stories and turn them into plain-spoken B2B story assets: story briefs, case studies, one-page PDFs, website posts, and sales proof.
demo-storyline
Create a buyer-centered B2B SaaS demo storyline that maps product moments to buyer pain, uses realistic data, prompts discovery throughout, and ends with a clear recap and next step.