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 agentmods add skills/epistates/sparx/reviewnpx skills add Epistates/sparX --skill reviewgit 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/review)<a href="https://agentmods.dev/skills/epistates/sparx/review"><img src="https://agentmods.dev/badge/skills/epistates/sparx/review.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.01042 |
| Opus 5 | $0.00020 | $0.00521 |
| Sonnet 5 | $0.00008 | $0.00208 |
| Haiku 4.5 | $0.00004 | $0.00104 |
Grade A, and why
review 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 5d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-Publish Performance Review
Analyze how published posts performed against Phoenix scoring predictions and extract actionable lessons for future content.
Input
The user provides one or more of:
- Post URL(s) — to analyze directly
- Engagement data — impressions, replies, reposts, likes, bookmarks, link clicks
- Analytics screenshot — from X Premium analytics dashboard
- General request — "review my recent posts" or "what's working?"
Process
Step 1 — Gather Performance Data
If the user provides a URL: Use WebFetch to read the post text and any publicly visible metrics. WebFetch can extract the post content, author, and basic engagement signals visible on the page.
For full authenticated metrics (detailed impression counts, bookmark counts, analytics dashboard data), suggest running /analyze [url] which uses chrome browser automation for logged-in access to richer data.
If the user provides raw data, use that directly.
Key metrics to capture:
- Impressions — total eyeballs
- Engagement rate — (all engagements / impressions) × 100
- Reply count — most important engagement metric
- Reposts — distribution amplifier
- Bookmarks — quality signal
- Likes — baseline (least important positive)
- Profile visits — discovery signal
- Follower change — growth impact
- Video views / completion % (if applicable)
- Link clicks (if applicable)
Step 2 — Score Against Phoenix Hierarchy
Map actual performance to algorithm signals:
| Metric | Value | Algorithm Interpretation |
|---|---|---|
| Author reply threads | ? | 75–150× weight — did the author create conversation threads? |
| Replies | ? | 13–27× weight — [assessment] |
| Reposts | ? | ~20× weight — [assessment] |
| Bookmarks | ? | ~10× weight — [assessment] |
| Impressions vs followers | ? | Distribution multiplier — [assessment] |
| Likes | ? | 1× baseline — [assessment] |
| Engagement rate | ? | Overall quality signal — [assessment] |
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.
- 5d ago First seen · 115 lines · 41 tokens per session scan A 4271456cac07
review is a skill published in the GitHub repository Epistates/sparX (3 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,042 once invoked, about $0.0002 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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