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.
npx skills add Epistates/sparX --skill optimizegit clone --depth 1 https://github.com/Epistates/sparXWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/epistates/sparx/optimize)<a href="https://agentmods.dev/skills/epistates/sparx/optimize"><img src="https://agentmods.dev/badge/skills/epistates/sparx/optimize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/epistates/sparx/optimize"><img src="https://agentmods.dev/badge/skills/epistates/sparx/optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.01518 |
| Opus 5.5 | $0.00015 | $0.00607 |
| Sonnet 5.5 | $0.00007 | $0.00304 |
| Haiku 4.5 | $0.00004 | $0.00152 |
Grade A, and why
optimize 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Signal | Score (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
| Risk | Score (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.? |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 141 lines · 37 tokens per session scan A 83679a29ea9b
optimize is a skill published in the GitHub repository Epistates/sparX (3 stars, last pushed 6mo ago), licensed MIT. It adds 37 tokens to every session and 1,518 once invoked, about $0.0001 per session on Opus 5.5. 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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