creatorweeklyauto-generated · unproven

How do I each week, review published content performance from an export and pick the next topics based on what actually worked

the agent that answers this

weekly content performance review. each week, review published content performance from an export and pick the next topics based on what actually worked.

Updated Jul 12

Cost: Free; on your own plan
Runs: weekly; on a schedule
Built from: 3 steps; 1 verified skills
Runs in: Claude or Codex; as you

what you get

a content review: the top and bottom performers, what the winners had in common, and a ranked list of next topics to double down on.

review_donenext_topics_set

example result

illustrative

A finished weekly run delivers: a content review: the top and bottom performers, what the winners had in common, and a ranked list of next topics to double down on. Each run captures: review_done, next_topics_set.

Example output, to show the shape of the deliverable. It has not run on your data. Build the agent to get this on yours.

the steps

3 steps · 1 from verified skills

Step 1tool
read the content performance export
Your model fills this step
Step 2
identify the top and bottom performers and the pattern
Detects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100…
Market Top Detector Skill Purpose Detect the probability of a market top formation using a quantitative 6-component scoring system (0-100). Integrates three proven market top detection methodologies: O'Neil - Distribution Day accumulation…
Full skill: market-top-detector
Step 3decision
recommend the next topics to double down on
Decision step

keep in mind

reads a performance export you provide; it does not log into your analytics or CMS.

Run this agent in Implexa, on your own Claude or Codex, free

Get the Implexa app (or connect your Claude Code / Codex), then say build the weekly content performance review agent and approve the schedule. It runs as you, on your real data, on the subscription you already pay for, and gets sharper each run. Your agent's memory is yours and travels with you across Claude, Codex, and whatever comes next. About 5 minutes to your first real run.

Run this agent in Implexa →

common questions

How do I each week, review published content performance from an export and pick the next topics based on what actually worked?

The weekly content performance review agent. a content review: the top and bottom performers, what the winners had in common, and a ranked list of next topics to double down on.

Is the weekly content performance review agent free?

Yes. It runs on the Claude or Codex subscription you already pay for, so there is no extra AI bill and no per-run charge. You can build and run unlimited agents on the free plan.

How often does the weekly content performance review agent run?

It is built to run weekly, on a schedule you set when you build it. You can change the cadence or pause it any time, and it runs unattended once it is on.

What does the weekly content performance review agent need to run?

Just Implexa installed in your Claude or Codex. There are no fragile integrations to wire up, and Implexa never touches your accounts, passwords, or the contents of your work.

Does the weekly content performance review agent use my data? Is it private?

It runs as you, on your own machine, on your real data. The model runs inside your own Claude or Codex, so Implexa never sees your data, accounts, or credentials. Your agent's memory is yours and travels with you across Claude, Codex, and whatever comes next.

How do I build the weekly content performance review agent?

Install Implexa into your Claude or Codex, then say "build the weekly content performance review agent" and approve the schedule. Implexa assembles the 3 steps (1 from verified skills) and it runs on its own. About 5 minutes to your first real run.

Can I change what the weekly content performance review agent does?

Yes. Tell it what to change in plain language and it revises its steps; the next scheduled run uses the change, with no re-scheduling. Every change is versioned, and a run can even propose its own improvements.