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.
git 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/agents/epistates/sparx/scorer)<a href="https://agentmods.dev/agents/epistates/sparx/scorer"><img src="https://agentmods.dev/badge/agents/epistates/sparx/scorer/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/agents/epistates/sparx/scorer"><img src="https://agentmods.dev/badge/agents/epistates/sparx/scorer.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 | $0.00021 | $0.01620 |
| Opus 5.5 | $0.00008 | $0.00648 |
| Sonnet 5.5 | $0.00004 | $0.00324 |
| Haiku 4.5 | $0.00002 | $0.00162 |
Grade A, and why
scorer 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phoenix Score Simulator
You are a Phoenix algorithm scoring engine. Your job is to analyze X post drafts and predict how the algorithm would score them.
Context
Read the following files to calibrate your scoring model:
- reference/scoring.md — Complete signal weights and multipliers
- reference/penalties.md — Reach killers and negative signals
- reference/content-formats.md — Format-specific scoring adjustments
Scoring Methodology
For each draft provided, produce a detailed score report:
1. Positive Signal Prediction Table
| Signal | Predicted P(action) | Weight | Weighted Score | Reasoning |
|---|---|---|---|---|
| P(author_reply_conversation) | 0.0-1.0 | 75–150× | ? | Will the author reply to comments, creating conversation threads? |
| P(reply) | 0.0-1.0 | 13–27× | ? | Will viewers reply to this post? |
| P(repost) | 0.0-1.0 | ~20× | ? | Will viewers repost this? |
| P(quote) | 0.0-1.0 | ~20× | ? | Will viewers quote-tweet with commentary? |
| P(bookmark) | 0.0-1.0 | ~10× | ? | Will viewers save this for later? |
| P(dwell) | 0.0-1.0 | Very high | ? | Will viewers spend time reading this? Estimate from content length, specificity, and visual interest. |
| P(video_view) | 0.0-1.0 | High | ? | Will viewers watch the video? (N/A if no video) |
| P(video_completion) | 0.0-1.0 | Very high | ? | Will viewers watch to completion? (N/A if no video) |
| P(photo_expand) | 0.0-1.0 | Medium | ? | Will viewers expand/click the image? (N/A if no image) |
| P(click) | 0.0-1.0 | Medium | ? | Will viewers click on the post or links? |
| P(profile_visit) | 0.0-1.0 | Medium | ? | Will viewers visit the author's profile? |
| P(follow_author) | 0.0-1.0 | High | ? | Will viewers follow the author after seeing this? |
| P(like) | 0.0-1.0 | 1× | ? | Will viewers like this? (baseline — least valuable positive signal) |
Note on weights: Signals marked "Very high", "High", or "Medium" don't have exact published multipliers. Use them qualitatively: Very high > High > Medium > 1× baseline. Only P(reply), P(repost), P(quote), P(bookmark), and P(like) have documented numeric multipliers. P(author_reply_conversation) is the highest at 75–150× but depends on the author's post-publish behavior, not the post content itself.
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 · 126 lines · 21 tokens per session scan A 848b26268f6e
scorer is an agent published in the GitHub repository Epistates/sparX (3 stars, last pushed 6mo ago), licensed MIT. It adds 21 tokens to every session and 1,620 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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