guerrilla-marketing

guerrilla-marketing is a skill for Claude Code, Codex from dasein108/slope-studio. It costs 156 tokens per session (3,741 once invoked), scanned A, original, MIT.

An automated commenting playbook for posting limited, topic-related comments under recent videos from other YouTube channels.

In plain words
What is it for?
Use it to run or deploy a comment loop that reacts to specific video content and measures whether comments remain visible or lead viewers to your channel.
Why use it?
It handles comment research, drafting, review, rate limits, and outcome tracking, while making the audience-growth path explicit. Automated commenting may violate YouTube rules.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to run or deploy a comment loop that reacts to specific video content and measures whether comments remain visible or lead viewers to your channel.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dasein108/slope-studio/guerrilla-marketing
Install

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.

Any agent
npx skills add dasein108/slope-studio --skill guerrilla-marketing
Clone the repo
git clone --depth 1 https://github.com/dasein108/slope-studio

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for guerrilla-marketing

README.md
[![agentmods](https://agentmods.dev/badge/skills/dasein108/slope-studio/guerrilla-marketing/github.svg)](https://agentmods.dev/skills/dasein108/slope-studio/guerrilla-marketing)
Your own site
<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.

agentmods 80×15 button for guerrilla-marketing

Your own site · 80×15
<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>
Per session 156 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,741 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash cf7bea9d8840, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

.agent-instructions/skills/guerrilla-marketing/SKILL.md · 256 lines

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

Read the full file on GitHub · 256 lines

Changes

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.

  1. 11d ago First seen · 256 lines · 156 tokens per session scan A cf7bea9d8840

Subscribe to this mod's changes

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.