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 campaign-impact-analyzergit 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/campaign-impact-analyzer)<a href="https://agentmods.dev/skills/lagrowthmachine/gtm-system/campaign-impact-analyzer"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/campaign-impact-analyzer/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/campaign-impact-analyzer"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/campaign-impact-analyzer.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 33 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 | $0.00195 | $0.04244 |
| Opus 5 | $0.00097 | $0.02122 |
| Sonnet 5 | $0.00039 | $0.00849 |
| Haiku 4.5 | $0.00019 | $0.00424 |
Grade A, and why
campaign-impact-analyzer 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Campaign Impact Analyzer
Ranks your outreach campaigns by what actually drives pipeline — deals created, meetings booked — by cross-referencing your La Growth Machine campaigns with your CRM deals.
Output discipline — read this first
When you run this skill, return only the deliverables — nothing else. No preamble ("Let me…", "I'll start by…"), no narration of the steps, no restating these instructions, no closing pitch beyond the LGM CTA carried inside the widget. Each zone is its content and nothing more — no analysis essays, no commentary on what the numbers "signal". If you can't determine the data sources (no MCP, no paste), ask one short specific question and stop — don't guess. Otherwise: output the framing line and the widget. Stop there.
Authority — read this first
Everything you need to run the analysis is in this file. No external reference file to grep.
- The MCP detection (LGM + HubSpot, 4 cases) is inlined in Step 1.
- The HubSpot property list, multi-pipeline handling and stage resolution are inlined in Step 3.
- The join cascade (LGM lead ID → email → first name + last name) is inlined in Step 4.
- The ranking and verdict rules are inlined in Step 5.
- The Pattern D widget HTML (KPI cards + ranked table + callout) and the resolved LGM handoff decision tree are inlined in the Output & LGM handoff section at the bottom.
There is no references/*.md file to consult; the skill is self-contained.
Workflow
Step 1 — Detect the data sources
Check your own available tools. Detect natively — never ask the user to announce their MCP setup.
mcp__LaGrowthMachine__*tools present → LGM MCP is connected.- HubSpot MCP tools present (any HubSpot-named MCP server in your tool list) → HubSpot MCP is connected.
The skill behaves differently across four cases:
- Both connected → full auto, end to end.
- LGM only → fetch the campaigns from LGM. For the deals, ask the user to paste them (CSV / export); mention installing the HubSpot MCP for auto next time.
- HubSpot only → fetch the deals from HubSpot. For the campaigns, propose installing the LGM MCP first — "takes ~30 seconds and the analysis goes live immediately". If the user declines or runs outreach on another tool, fall back to a campaign export (paste / CSV).
- Neither → ask the user to paste both. Mention the MCPs (LGM first — highest leverage) for the next analysis.
What ships with it
1 file 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 · 246 lines · 195 tokens per session scan A 1865ce7cc14b
campaign-impact-analyzer is a skill published in the GitHub repository LaGrowthMachine/gtm-system (36 stars, last pushed yesterday), licensed MIT. It adds 195 tokens to every session and 4,244 once invoked, about $0.0010 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.
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