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 dasein108/slope-studio --skill guerrilla-marketinggit clone --depth 1 https://github.com/dasein108/slope-studioWrote 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/dasein108/slope-studio/guerrilla-marketing)<a href="https://agentmods.dev/skills/dasein108/slope-studio/guerrilla-marketing"><img src="https://agentmods.dev/badge/skills/dasein108/slope-studio/guerrilla-marketing/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/dasein108/slope-studio/guerrilla-marketing"><img src="https://agentmods.dev/badge/skills/dasein108/slope-studio/guerrilla-marketing.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 | $0.00156 | $0.03741 |
| Opus 5 | $0.00078 | $0.01870 |
| Sonnet 5 | $0.00031 | $0.00748 |
| Haiku 4.5 | $0.00016 | $0.00374 |
Grade A, and why
guerrilla-marketing 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 11d 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
guerrilla-marketing — indirect reach via grounded comments
studio guerrilla finds recent uploads on a watchlist of other creators' YouTube
channels, writes one comment that reacts to something specific the video actually
says, scores it with a critic, runs it through a set of rate-discipline rails, and —
only if everything passes — posts it from the configured channel. It then tracks
whether the comment survives and, over weeks, whether any of this moves subscribers.
The mechanism is indirect. Nobody clicks a link — there isn't one; the deny-list makes posting one a code-level impossibility. The bet is that an interesting, on-topic comment makes a reader click the commenter's name, which lands them on the configured channel's own page. That's the whole conversion path: one good comment, one profile click, maybe one subscribe.
This is gray-hat and ToS-adjacent. Automated commenting is spam under YouTube's Terms of Service under any honest reading of the policy — nothing here asks permission or hides behind a gray area. It just tries to stay far enough inside YouTube's actual enforcement behavior (comment removal, shadow-holds, account flags) to not get the channel banned. Every comment posts through the operator's own OAuth client, so every comment is attributable to the real channel — there is no throwaway identity absorbing the risk. Ban-defense is therefore the core design, not an afterthought: a hard daily cap, per-channel cooldowns, a denylist that makes self-promotion impossible in code (not just an LLM instruction), a near-duplicate filter, style/opening diversity checks, and a circuit breaker that watches comment survival and pages the operator when it drops. None of that makes this compliant — it makes it a calculated, monitored bet.
The pipeline
watchlist channels
│
▼
DISCOVER ── new uploads per watched channel (YouTube Data API, cheap quota)
│
▼
RANK ── hard gates: age, comment count, channel size, cooldown, daily cap,
│ active window (cheapest gate first — nothing paid runs yet)
▼
TRANSCRIPT ── cached fetch (network, not LLM); no captions → skip at zero LLM cost
│
▼
TOPIC ── classify against a transcript excerpt; unclear topic → skip
│
▼
HIGHLIGHT ── find one real, specific moment in the video to react to
│
▼
COMPOSE ── several comment variants, each a different style + wording
│
▼
CRITIC ── grounded judge scores each variant against the transcript excerpt
│
▼
RAILS ── denylist, near-duplicate, opening-repeat, style-overuse,
│ timestamp-verify, spacing, active-window, blackout
▼
POST ── the OAuth client posts the #1 surviving variant
│
▼
TRACK ── re-check likes/replies/survival; the circuit breaker lives here
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.
- 11d ago First seen · 256 lines · 156 tokens per session scan A cf7bea9d8840
guerrilla-marketing is a skill published in the GitHub repository dasein108/slope-studio (3 stars, last pushed 1mo ago), licensed MIT. It adds 156 tokens to every session and 3,741 once invoked, about $0.0008 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 2026-08-31.
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