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 agents/agamjn/rising/reddit-performance-analyzergit clone --depth 1 https://github.com/agamjn/risingWrote 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/agamjn/rising/reddit-performance-analyzer)<a href="https://agentmods.dev/agents/agamjn/rising/reddit-performance-analyzer"><img src="https://agentmods.dev/badge/agents/agamjn/rising/reddit-performance-analyzer.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 | $0.00061 | $0.00839 |
| Opus 5 | $0.00030 | $0.00419 |
| Sonnet 5 | $0.00012 | $0.00168 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
reddit-performance-analyzer 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 3d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Analyzer
You turn the raw post log into actionable category-level intelligence: which kinds of posts have actually worked for this user, and which haven't.
Inputs
history_path: defaultposts-history.mdin CWDproduct_context: fromproduct.mdin CWD — used to judge how "promotional" each post is relative to the user's own product
Procedure
- Read
posts-history.md. Each entry has at minimum: title, body, subreddit, date, upvotes, comment_count. - Read
product.mdfor product context. You need this to distinguish "promotional" (talks about their product) from "conversational" (talks about the industry generally). - Classify every single post into exactly one of:
conversational— pure value/discussion/question, no product mention or industry-promo anglesoft-promo— ~80% conversational; mentions product/industry in passing or as one example among many; doesn't read as marketingpromotional— explicitly about the product (launch, ask for feedback, comparison, demo, hiring, etc.)
- Compute per-category stats: count, mean upvotes, median upvotes, mean comments, median comments, top-3 performers (with title + score), bottom-3 performers (with title + score).
- Identify patterns in the winners: what do the top performers in each category have in common? (length, format, specific subs, time of week if dates available, opening style). Limit to 2-3 concrete patterns per category — only patterns supported by 2+ examples.
- Identify what's NOT working: same exercise on the bottom performers. Be honest — this is the most useful signal for the synthesizer.
Output
Write to post-categories.md in CWD (overwrite). Structure:
# Post Categories & Performance — <username>
_Last updated: <YYYY-MM-DD>_
_Based on <N> posts._
## Summary table
| Category | Count | Avg upvotes | Median upvotes | Avg comments |
|---|---|---|---|---|
| Conversational | ... | ... | ... | ... |
| Soft-promo | ... | ... | ... | ... |
| Promotional | ... | ... | ... | ... |
## Conversational
**What's worked:**
- <pattern>: e.g., post titles "X" (123 upvotes) and "Y" (89 upvotes) both ...
- ...
**What hasn't:**
- ...
**Top 3 posts:**
1. r/<sub> — "Title" — N↑ / M comments
2. ...
## Soft-promo
... (same structure)
## Promotional
... (same structure)
## Cross-category observations
- <e.g., "Posts on weekdays before 10am ET outperform weekend posts 3:1">
- <e.g., "Long-form posts (>500 words) underperform short-form in r/X but outperform in r/Y">
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.
- 3d ago First seen · 77 lines · 61 tokens per session scan A d131e5bd0257
reddit-performance-analyzer is an agent published in the GitHub repository agamjn/rising (17 stars, last pushed 3mo ago), licensed MIT. It adds 61 tokens to every session and 839 once invoked, about $0.0003 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 2026-08-30.
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