Performance Optimizer

Performance Optimizer

A post that flopped is a free experiment — but only if you extract one lesson and ship the next test. Optimization is not "make it better everywhere"; it is finding the single weakest link, changing ONE variable, and letting the numbers vote. Content has four links in series — Hook, Body/Retention, CTA/Conversion, Distribution — and the chain breaks at its weakest point. Fixing anything other than the weakest link is motion without progress. Winners are not luck to admire; they are formats to industrialize. Every result routes to one of three verbs: KILL, ITERATE, or SCALE.

1. Post-mortem — isolate the weakest link

1. Pull the funnel: hook rate, retention/avg watch, saves+shares per view, follows-per-view, reach.
2. Find the FIRST metric below the account's median — that is the weakest link. Attribute the outcome to it, not to a vibe.
3. Weak hook rate -> packaging problem (first frame, first line, title, thumbnail). Good hook + retention cliff -> body problem (pacing, payoff, structure). Good retention + low saves/follows -> CTA/value problem. Everything fine + low reach -> timing, niche-fit, or an unlucky test batch (re-test before concluding).
4. State ONE root cause in a sentence. If you can't, you're guessing — get more data.

2. Design single-variable A/B tests

1. Change exactly ONE variable per test so the result is attributable. Multi-variable "improvements" teach nothing.
2. Highest-leverage variables in order: hook line, first frame/thumbnail, first 3 seconds, format/structure, topic angle, CTA, length, posting time.
3. Write the hypothesis as: "If I change [X], then [metric] improves, because [reason]." Keep everything else identical.
4. Run 3-5 posts per variant before judging — a single post is noise; the algorithm's test audience varies wildly.
5. Judge on the diagnostic RATE tied to the change (hook test -> hook rate), not on total views.

3. Double-down, and decide KILL / ITERATE / SCALE

1. Double-down rule: when a post beats your median by ~2-3x, immediately make 3 more in the same format/angle/hook pattern while it's hot. Winners cluster.
2. KILL: below median on hook AND value after 3+ attempts — the concept doesn't land. Stop; free the slots.
3. ITERATE: mixed signals (strong hook, weak body, or vice versa) — keep the strong link, run one test on the weak link.
4. SCALE: clear winner — replicate the pattern, vary only surface topics, and push volume. Turn the one-off into a series/template.
5. Iteration loop: Ship -> read the one weakest link -> change one variable -> re-ship -> compare to median -> route to Kill/Iterate/Scale. Repeat weekly.

Output template

POST-MORTEM
- Result vs median: [win / flop / average] ([metric], [n] vs [median])
- Funnel read: hook [n]% | retention [n]% | saves+shares [n]% | follows/view [n]
- Weakest link: [hook / body / CTA / distribution]
- Root cause (one sentence): [...]

RANKED FIXES (highest leverage first)
1. [fix] — targets [metric]
2. [fix]
3. [fix]

NEXT 3 EXPERIMENTS (one variable each)
1. Change [X] -> hypothesis: [metric] improves because [reason]
2. Change [Y] -> ...
3. Change [Z] -> ...

DECISION: KILL / ITERATE / SCALE — [why]

Platform variants

• Short video: iterate hooks and first-frames fastest; retention curve is the truth serum.
• YouTube long-form: A/B thumbnail+title first (CTR), then intro (30s retention); tests take days, not hours.
• Carousels/LinkedIn: test slide 1 / opening line and the save-worthy payoff; saves and dwell decide.
• X: test the first line and the format (hook+list vs story); reposts and profile clicks judge it.

Rules

• Isolate ONE weakest link before proposing any fix — no shotgun changes.
• One variable per test, always — attributable or worthless.
• Never conclude from a single post; require 3-5 before you kill or scale.
• When something wins, make 3 more immediately — ride the pattern while the algorithm favors it.
• Judge each test on the rate it targets, not on vanity totals.
• Kill decisively. Dead concepts steal the volume your winners need.
• Compare to the account's own median, never to an absolute or to someone else's numbers.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: moses607
• Source: moses607/socialforge
• License: MIT

Install and usage instructions live in the source repository linked above.