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 octavehq/lfgtm --skill generategit clone --depth 1 https://github.com/octavehq/lfgtmWrote 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/octavehq/lfgtm/generate)<a href="https://agentmods.dev/skills/octavehq/lfgtm/generate"><img src="https://agentmods.dev/badge/skills/octavehq/lfgtm/generate/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/octavehq/lfgtm/generate"><img src="https://agentmods.dev/badge/skills/octavehq/lfgtm/generate.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 | $0.00069 | $0.03321 |
| Opus 5 | $0.00034 | $0.01661 |
| Sonnet 5 | $0.00014 | $0.00664 |
| Haiku 4.5 | $0.00007 | $0.00332 |
Grade A, and why
generate 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 — 416 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/octave:generate - GTM Content Generator
Generate GTM content using your Octave library context. Choose how to generate: run a saved agent for consistency, use Octave's built-in AI, or have Claude draft it directly with Octave context.
Principles
Follow these standards during generation. Read each before producing output.
Content and language:
- Editorial rules — no AI-isms, banned vocabulary, honest analyst tone
- Information principles — lead with conclusions, evidence-backed claims, narrative arc
Presentation:
- Presentation principles — use for any visual output (HTML, dashboards, tables); text follows the editorial rules above
Octave data:
- Octave value — prioritize grounded workspace data over generic AI content
- Octave research toolkit — tool selection (list vs. search) and standard error handling when gathering context for Mode B/C
- Entity model — canonical entity types and oId prefixes referenced throughout (persona, product, Motion, Motion ICP, etc.)
Review:
- For Mode C (Claude Direct), the content is Claude's own draft: run the review from protocol.md before presenting it — for HTML output the protocol is a mandatory gate; for text output run the preflight and the editorial checks. Modes A and B hand generation to a saved agent or Octave's own generation tools, so the protocol's reviewer pass doesn't apply — those outputs still get the Step 4/5 present-and-refine loop below.
Usage
/octave:generate <type> [options] [--mode agent|octave|claude]
Content Types
Email Sequences
/octave:generate email --to "<person>" --about "<topic>" [--persona "<persona>"] [--motion "<motion>"]
Example:
/octave:generate email --to "John Smith, VP Engineering at Acme" --about "reducing deployment time"
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 · 416 lines · 69 tokens per session scan A eae34869f480
generate is a skill published in the GitHub repository octavehq/lfgtm (11 stars, last pushed 19d ago), licensed MIT. It adds 69 tokens to every session and 3,321 once invoked, about $0.0003 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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