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 buildfastwithai/gen-ai-experiments --skill x-algo-tweet-writergit clone --depth 1 https://github.com/buildfastwithai/gen-ai-experimentsWrote 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/buildfastwithai/gen-ai-experiments/x-algo-tweet-writer)<a href="https://agentmods.dev/skills/buildfastwithai/gen-ai-experiments/x-algo-tweet-writer"><img src="https://agentmods.dev/badge/skills/buildfastwithai/gen-ai-experiments/x-algo-tweet-writer/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/buildfastwithai/gen-ai-experiments/x-algo-tweet-writer"><img src="https://agentmods.dev/badge/skills/buildfastwithai/gen-ai-experiments/x-algo-tweet-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00246 | $0.04023 |
| Opus 5 | $0.00123 | $0.02011 |
| Sonnet 5 | $0.00049 | $0.00805 |
| Haiku 4.5 | $0.00025 | $0.00402 |
Grade A, and why
x-algo-tweet-writer 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 12d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
X Algorithm Tweet Writer
Purpose
Write tweets that are engineered for the X "For You" algorithm — not "tweets that sound good" but tweets that actually win against the 22 signals the model predicts. Every craft decision (hook, length, format, timing, link/hashtag/quote choice) maps to a documented mechanism in the xAI source code dump from May 2026.
The full source-code analysis is in references/x-algo-insights.md — read it when you need a mechanism's exact citation, when the user asks "why," or when you hit a case this SKILL.md doesn't cover.
The mental model in one paragraph
The algorithm scores each tweet as a weighted sum of 22 probabilities. 17 are positive (favorite, reply, retweet, dwell, cont_dwell_time, click_dwell_time, photo_expand, click, profile_click, vqv, share, share_via_dm, share_via_copy_link, quote, quoted_click, quoted_vqv, follow_author). 5 are negative and subtract (not_dwelled, not_interested, block_author, mute_author, report). Negative signals weigh orders of magnitude more than positive ones. Above that scoring layer, three gates decide whether your post enters broad discovery at all: (1) the min-traction gate in the first ~30 minutes — without early engagement the post never enters Grok's Banger Initial Screen and never gets a quality multimodal embedding, so it's invisible out-of-network; (2) the 80-hour age cap — after ~3 days the model treats the post as "very old" and stops surfacing it; (3) the Author Diversity Decay — your 2nd, 3rd, 4th post in the same feed gets exponentially demoted. Everything else is a corollary.
Workflow (follow in order)
Step 1: Clarify the goal
Before writing, identify which of these the user wants. Don't ask if it's obvious from context, but make sure you've classified it internally:
| Goal | Optimize for | Format implications |
|---|---|---|
| Reach / virality | quote_score, retweet_score, follow_author, dwell | Original post, contrarian hook, citable phrasing, post at audience peak time |
| Engagement / conversation | reply_score, cont_dwell_time | Question or polarizing claim at end, substantive body, reply hook |
| Follower growth | follow_author_score, profile_click_score | Strong POV, unique angle, "who is this person?" energy |
| Reply-jacking a large account | Reply Ranker 0-3 score | Substantive reply, adds info or wit, not generic |
| Quote-tweeting a viral | quote_score, quoted_click, quoted_vqv | Real take added, quoted post must be "Safe" (not MediumRisk) |
| Thread | dwell across multiple tweets | Single banger first tweet — only one tweet per thread survives DedupConversationFilter |
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.
- 12d ago First seen · 240 lines · 246 tokens per session scan A cec0dbdd587b
x-algo-tweet-writer is a skill published in the GitHub repository buildfastwithai/gen-ai-experiments (763 stars, last pushed today), licensed MIT. It adds 246 tokens to every session and 4,023 once invoked, about $0.0012 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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