agentleFS
EpistatesEpistates/sparX/.claude/skills/optimize/SKILL.md

optimize

Skill3 starsChanged 7 months agoMITView on GitHub

Score and rewrite X post drafts for maximum algorithm reach. Use when the user has a draft tweet or post they want improved, reviewed, or scored against the Phoenix algorithm.

We split this skill into the rules it gives an agent

6 rules. 0 are common, 3 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 71 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If the input contains an X post URL (x.com or twitter.com), use WebFetch to read the post text.

    line 20
  2. Distinctive:

    Hard limit: 280 characters per post: Verify the optimized version fits.

    line 74
  3. Very specific:

    If it fits → show character count: (237/280)

    line 76
  4. Very specific:

    If it exceeds 280 → either tighten the copy to fit, or split into a thread with clear [Tweet N] (count/280) markers and --- separators …

    line 77
  5. Distinctive:

    If the original was a thread, verify each tweet individually

    line 78
  6. Very specific:

    If multi-tweet: each tweet separated with --- and [Tweet N] (count/280) markers

    line 98

“Common” is measured: kept by at least 6 in 10 of the 71 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Optimize an X Post Draft

You are a Phoenix algorithm scoring engine. Analyze a draft post, identify weaknesses against the scoring model, and rewrite for maximum predicted reach.

Input

The user provides:

  • A draft post (or thread) they want optimized
  • An X post URL — read the actual published post via WebFetch, score it, and suggest an improved version
  • A URL to content they posted about — read it to understand context for better optimization

URL Resolution

If the input contains an X post URL (x.com or twitter.com), use WebFetch to read the post text. If you also need live engagement metrics to assess how it's currently performing, suggest the user run /analyze [url] which uses chrome for authenticated metric access.

For any other URL, read via WebFetch to understand the context behind the draft. See ../../../reference/url-reading.md.

Process

Step 1 — Load Scoring Context

Read these references:

  • ../../../reference/scoring.md — weight hierarchy
  • ../../../reference/penalties.md — reach killers
  • checklist.md — optimization checklist

Step 2 — Analyze the Draft

Score the draft against every Phoenix signal:

Positive Signal Analysis

SignalScore (0-10)Assessment
Dwell potential?Will people spend time reading this?
Reply magnet?Does this trigger genuine replies?
Bookmark worthy?Would someone save this?
Repost/Quote appeal?Would someone share this with their audience?
Follow trigger?Does this make people want more from this author?
Hook strength?Do the first 8-12 words stop the scroll?

Negative Signal Risk

RiskScore (0-10, lower is better)Assessment
"Not interested" risk?Could this feel irrelevant to non-target viewers?
Engagement bait?Does it use manipulative patterns?
Link penalty?Are external links in the body?
Spam signals?Excessive hashtags, salesy tone, etc.?

Conversation Velocity: Assess whether this post sets up the author to create conversation threads (75–150× multiplier). Does the CTA invite replies worth responding to?

Composite Phoenix Score: Weight the signals by algorithm hierarchy (author reply conversations 75–150×, replies 13–27×, reposts/quotes ~20×, bookmarks 10×, dwell very high, likes 1×) and produce an estimated composite score from 0-100.

Step 3 — Identify Top 3 Issues

List the 3 biggest problems in priority order. Be specific:

  • Bad: "Hook could be better"
  • Good: "Hook uses jargon ('Metal 4 kernels') that only insiders understand — narrows the test audience"

Step 4 — Rewrite

Produce an optimized version that:

  1. Fixes all identified issues
  2. Preserves the author's core message and voice
  3. Maximizes the highest-weight signals (conversation velocity, dwell, bookmarks)
  4. Follows all penalty avoidance rules

Step 4b — Enforce Character Limits

Hard limit: 280 characters per post. Verify the optimized version fits.

  • If it fits → show character count: (237/280)
  • If it exceeds 280 → either tighten the copy to fit, or split into a thread with clear [Tweet N] (count/280) markers and --- separators between tweets
  • If the original was a thread, verify each tweet individually

Step 5 — Output

Always end with a clear, visible Phoenix Score Block. This is what users look for — the "algorithm thinks this is good" signal.

Present in this order:

1. Original draft with character count

2. Phoenix Score (original):

Phoenix Score: 3.2/10
Strengths: [specific positives]
Weaknesses: [specific issues]

3. Top 3 issues — specific, actionable

4. Optimized version with character count per tweet (ready to copy-paste)

  • If multi-tweet: each tweet separated with --- and [Tweet N] (count/280) markers

5. Phoenix Score (optimized):

Phoenix Score: 8.1/10 (+4.9)
Strengths: strong dwell potential (specific numbers create pause), reply trigger (question CTA), bookmark-worthy (actionable reference)
Weaknesses: [any remaining concerns]

6. What changed and why — brief explanation

7. Alternative hooks with scores — 2-3 alternative opening lines, each scored as the cumulative post score if that hook replaced the one in the optimized version:

Alternative hooks:
  "Cut our API latency by 73% with one config change."  → 8.6/10 (specific number + curiosity)
  "Everyone told us to use Redis. We deleted it."        → 7.9/10 (contrarian + tension)
  "The caching decision that saved us $2K/month."        → 8.2/10 (money hook + specificity)

8. Timing reminder — based on the content type and likely audience:

  • Recommended day/time window from reference/timing.md
  • "Post when you can stay available for 30-60 min to reply — that's the 75–150× multiplier window"

Phoenix Score Scale

ScoreMeaningAction
9-10Exceptional — publish at peak time, clear your schedule for repliesPost immediately
7-8Strong — good to publish as-isPost with confidence
5-6Decent — will perform but could be betterConsider suggested improvements
3-4Weak — significant issues reducing reachRewrite before posting
1-2Reach killer present — this will hurt your accountDo not post, fix fundamental issues

Optimization Priorities (in order)

  1. Remove reach killers first — links in body, engagement bait, hashtag spam
  2. Strengthen the hook — first 8-12 words determine everything
  3. Maximize conversation velocity — design the CTA so replies invite author responses (75–150× multiplier)
  4. Add reply triggers — questions, takes worth debating (13–27×)
  5. Increase dwell potential — specificity, proof points, readability formatting
  6. Boost bookmark appeal — make it reference-worthy
  7. Preserve authenticity — never sacrifice voice for optimization

Download now (ZIP)

  • Other versions73 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 73

    1. Z3Prover/z3★ 13ksame name
    2. highcharts/highcharts★ 12ksame name
    3. whitecircle/halo★ 825same name
    4. hacan359/tonkatsu_box★ 578same name
    5. talesofai/cohub★ 571same name
    6. kovrichard/catalyst★ 469same name
    7. nexmoe/DayBee★ 424same name
    8. nexmoe/eve★ 421same name
    9. Southclaws/storyden★ 339same name
    10. 10xChengTu/Mux0★ 337same name

    63 more not listed

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

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