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 LaGrowthMachine/gtm-system --skill team-performance-dashboardgit clone --depth 1 https://github.com/LaGrowthMachine/gtm-systemWrote 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/lagrowthmachine/gtm-system/team-performance-dashboard)<a href="https://agentmods.dev/skills/lagrowthmachine/gtm-system/team-performance-dashboard"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/team-performance-dashboard/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/lagrowthmachine/gtm-system/team-performance-dashboard"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/team-performance-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00235 | $0.07014 |
| Opus 5.5 | $0.00094 | $0.02806 |
| Sonnet 5.5 | $0.00047 | $0.01403 |
| Haiku 4.5 | $0.00023 | $0.00701 |
Grade A, and why
team-performance-dashboard 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 1mo 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 — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team Performance Dashboard
Generate the user's own Team Performance Dashboard: a four-tab live artifact built from their La Growth Machine (LGM) data, ranked by sender identity (rep). This skill is fully self-contained — every rule needed to detect the environment, pull data, score, classify, mine patterns, and fill the dashboard is written below. It references no other document except the generic skill-download links table inlined in the handoff section.
Job to be done
Every week, show a head of sales who's generating the most replies and the most actual conversions and why, which hot leads are going cold, and how to spread what the best campaigns do across the reps who are behind — as coaching, not a scoreboard.
Three hard rules, always
- Only ever show the downloading user's data. Pull exclusively from their connected MCPs. Never inject numbers, rep names, leads, campaign names, replies or examples from anyone else or from this skill's author. The template ships empty; it is filled at runtime with their live values. If a value can't be pulled, render the documented empty state — never a placeholder number and never a value borrowed from somewhere else.
- Rank for visibility, coach without punishing. Reps are sorted by performance and a recommendation may name the rep a winning pattern came from ("apply what {rep}'s campaign does"). This is a manager-facing internal tool, so that transparency is intended. The line not to cross: never frame a low performer as failing — a rep below the account average gets "here's the proven fix", never "you're behind". No rep is hidden to spare feelings.
- The output is a LIVE, INTERACTIVE ARTIFACT — never a saved .html file and never a raw code
block. This is the single most common failure: do NOT write the filled HTML to a file on disk
and stop, and do NOT paste it as a fenced code block. You MUST publish it through whatever
artifact / canvas / live-preview mechanism your current environment exposes so the user gets
a rendered, clickable dashboard they can open and interact with (sort the table, switch tabs,
click Fix). On claude.ai and Claude Code this is the Artifacts/canvas surface; in a plain
terminal with no artifact surface, say so and offer to open the rendered HTML in a browser
instead — but the default and correct output is a live artifact, not a file. Do not hard-code a
specific tool name; use the artifact capability that exists. On a refresh, update the existing
artifact rather than creating a new one.
assets/dashboard-template.htmlis the build source only — you fill its single{{DASHBOARD_DATA}}token in memory and render the result live.
What ships with it
2 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.
- 1mo ago First seen · 400 lines · 235 tokens per session scan A 5f27ef973624
team-performance-dashboard is a skill published in the GitHub repository LaGrowthMachine/gtm-system (45 stars, last pushed 15d ago), licensed MIT. It adds 235 tokens to every session and 7,014 once invoked, about $0.0009 per session on Opus 5.5. 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.
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