performance-optimizer

A method for examining content results and planning the next version by finding the weakest part of its path from attention to action.

In plain words
What is it for?
Review why a post succeeded or failed, choose one variable to test, and decide whether to stop, revise, or repeat the approach.
Why use it?
It replaces vague improvement attempts with a focused change and a measurable test.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/moses607/socialforge/performance-optimizer
Any agent
npx skills add moses607/socialforge --skill performance-optimizer
Clone the repo
git clone --depth 1 https://github.com/moses607/socialforge

Made for: Claude Code, Codex.

Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,112 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00093 $0.01112
Opus 5 $0.00046 $0.00556
Sonnet 5 $0.00019 $0.00222
Haiku 4.5 $0.00009 $0.00111

Measured yesterday against content hash a8841d5a9fb7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

performance-optimizer 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 yesterday.

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.

skills/performance-optimizer/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 68 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. yesterday First seen · 68 lines · 93 tokens per session scan A a8841d5a9fb7

Subscribe to this mod's changes

performance-optimizer is a skill published in the GitHub repository moses607/socialforge (2 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 1,112 once invoked, about $0.0005 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.

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