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 forsvn-labs/meta-skills --skill plan-campaigngit clone --depth 1 https://github.com/forsvn-labs/meta-skillsWrote 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/forsvn-labs/meta-skills/plan-campaign)<a href="https://agentmods.dev/skills/forsvn-labs/meta-skills/plan-campaign"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/plan-campaign/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/forsvn-labs/meta-skills/plan-campaign"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/plan-campaign.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 | $0.00059 | $0.00572 |
| Opus 5 | $0.00030 | $0.00286 |
| Sonnet 5 | $0.00012 | $0.00114 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
plan-campaign 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan an executable campaign
Create the smallest campaign package that can ship, learn, and compound.
Write the campaign spine
Define:
- Outcome: the observable business change.
- Audience: one primary audience for this campaign.
- Moment: why they care now.
- Promise: the credible change.
- Proof: what makes the promise believable.
- Objection: the main reason they will not act.
- Action: one proportionate next step.
- Measurement: one primary signal and a small diagnostic set.
If the spine cannot fit in a short paragraph, narrow the campaign.
Choose channels by fit
Select the smallest channel set that covers the job:
- Existing demand: search, comparison pages, marketplaces, launch directories.
- Borrowed trust: communities, partners, creators, advocates.
- Direct access: email, outreach, sales-assisted conversations.
- Compounding attention: useful content, social distribution, owned audience.
- Paid acceleration: only when message and conversion path are testable.
Choose from audience habitat, native format, proof, operator capacity, feedback speed, and destination readiness. For a named platform, verify current rules and norms when they affect the plan. Do not recommend a channel because it is fashionable.
Rank bets and budget learning
Force-rank opportunities before scoring them. Keep at most three active bets unless independent capacity exists. Treat budget as a constraint, not a strategy.
Give every experiment:
- hypothesis and mechanism;
- one intentional change;
- audience and channel;
- primary signal and diagnostic guardrails;
- minimum useful observation window;
- keep, revise, or stop rule.
Fund a test at the minimum level that can answer its question, or fund it at zero. Give each allocation a reallocation trigger and destination. Do not hide uncertainty behind precise forecasts.
For lifecycle work, use behavioral triggers, activation events, and suppression conditions. For referrals, earn retention first, bound rewards by economics, and add abuse controls before scaling.
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.
- 11d ago First seen · 76 lines · 59 tokens per session scan A 1aee19763cdf
plan-campaign is a skill published in the GitHub repository forsvn-labs/meta-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 572 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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