The parts of marketing that have a factual core a model gets wrong: authentication records, platform limits, named legal obligations, and the order operations have to happen in.
Marketing is the hardest category on this site to write honestly, because most of it has no ground truth. A strong model already writes a decent hook, and a skill that tells it to write a better one is a skill that does nothing. Two rounds of measurement on this project say the same thing every time: where the assistant working alone is already near the top of the rubric, nothing moves.
So the skills here are built on the parts of marketing that are checkable. Authentication records either align or they do not. A post either fits inside the character limit before truncation or it does not. A statute either names an obligation or it does not. A budget floor is a number. Where a factual core exists, that is where the skill lives, and the softer material around it is either supporting or absent.
Cold email and LinkedIn are here rather than in hubs of their own, and the reason is the data. We checked. As a search term, a Claude skill for cold email returns about 10 searches a month in the United States, and every phrasing we tested for LinkedIn returned nothing measurable at all. Both are real jobs that people genuinely do, so the skills exist and are as detailed as anything else on this site. Neither earns a category hub, and inventing one would be building a page for a query nobody types.
One skill here has been measured, and it is the one with the hardest facts in it. The email deliverability audit went from a median of 16 of 22 to 19, and from a worst case of 13 to 18, at p=0.0079. The gap sits almost entirely in two findings the run without it never makes: a 1024-bit signing key published with a testing flag beside it, and a click-through domain with no relationship to the sending domain. It also invents one problem in five runs that is not there. Both numbers are on its page. Nothing else in this category has been run yet, and every one of those pages says so before it says anything else.
What we will not do here. Several sources for this material are one named person's paid framework, and a few are that person's voice. We have taken the transferable method, credited frameworks where they belong to somebody, and left the impersonation out. Attributing an idea is fair. Simulating a living person is not, and it is not something you should want inside a file you install.
Run through the harness against a control. The numbers, the p value and the cost of using it are on each page.
Written, reviewed and free to take. No run behind them, so no claim about what they do to an output. Each page says so at the top.
Measured means the skill was given a realistic task on real material, then the identical task was run again with the skill removed, five runs each way. Each output was graded alone, against a rubric written by someone who had never seen the skill, by a session that was not told the other arm existed. Whether it passed was decided by a rule written down before any run executed. Those pages carry the worst case, the median, the p value, and what the skill costs you as well as what it buys.
Not yet measured means exactly that. It is written, it has been read, it is free to take, and we have run no experiment on it, so we make no claim about what it does to an output. It is not a skill that failed. Skills that failed are not published at all, in either state, and their numbers are in the results table on the hub.
Measuring one skill properly costs roughly twenty model sessions. We are working down the queue and moving skills from the second group into the first. Back to all skills.