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 gtmagents/gtm-agents --skill case-studiesgit clone --depth 1 https://github.com/gtmagents/gtm-agentsWrote 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/gtmagents/gtm-agents/case-studies)<a href="https://agentmods.dev/skills/gtmagents/gtm-agents/case-studies"><img src="https://agentmods.dev/badge/skills/gtmagents/gtm-agents/case-studies/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/gtmagents/gtm-agents/case-studies"><img src="https://agentmods.dev/badge/skills/gtmagents/gtm-agents/case-studies.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 | $0.00023 | $0.00338 |
| Opus 5 | $0.00012 | $0.00169 |
| Sonnet 5 | $0.00005 | $0.00068 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
case-studies 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.
What it actually says
Case Studies Skill
When to Use
- Sales needs tailored proof points for a segment or persona.
- Marketing is launching a campaign that requires social proof.
- Customer success wants standardized win-story format.
Framework
- Story Framework (CHIC) – Challenge → Hypothesis → Implementation → Change (results).
- Data Gathering – collect metrics (time saved, revenue lift), qualitative quotes, before/after workflows.
- Narrative Structure – exec summary, customer background, stakes, solution, outcomes, CTA.
- Format Variations – PDF, one-pager, slide deck, video script, micro social posts.
- Enablement Package – key stats table, objection responses, industry tags.
Templates
- Narrative brief outlining each CHIC section with quote/data placeholders.
- Multi-format layout kit (PDF, one-pager, slide deck) sharing headline + proof modules.
- Enablement bundle: stats table, objection matrix, industry filter tags.
Tips
- Secure approvals early—share outlines with customers/legal before writing.
- Lead with outcomes; move background into sidebars to keep attention.
- Build a modular proof library so stats auto-populate future case studies.
- Produce derivative social tiles or short videos alongside the long-form asset.
Checklist
- At least 2 hard metrics and 1 qualitative quote.
- Visual timeline or architecture diagram.
- CTA customized per campaign (demo, workshop, download).
- Approvals from customer + legal.
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 · 38 lines · 23 tokens per session scan A 0b4671c88610
case-studies is a skill published in the GitHub repository gtmagents/gtm-agents (399 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 338 once invoked, about $0.0001 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 2026-09-03.
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