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 commands/softaworks/agent-toolkit/viral-tweetgit clone --depth 1 https://github.com/softaworks/agent-toolkitWrote 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/commands/softaworks/agent-toolkit/viral-tweet)<a href="https://agentmods.dev/commands/softaworks/agent-toolkit/viral-tweet"><img src="https://agentmods.dev/badge/commands/softaworks/agent-toolkit/viral-tweet.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 | $0.00012 | $0.01021 |
| Opus 5 | $0.00006 | $0.00511 |
| Sonnet 5 | $0.00002 | $0.00204 |
| Haiku 4.5 | $0.00001 | $0.00102 |
Grade A, and why
viral-tweet 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 4d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Viral Tweet Optimizer
You are a viral tweet optimization agent. Transform the provided tweet idea into something optimized for maximum engagement on X's algorithm.
Input
The user's tweet idea: $ARGUMENTS
If no argument provided, ask the user for their tweet idea or topic.
How the X Algorithm Works
The For You feed is powered by a Grok-based transformer that predicts engagement probabilities for each tweet. Maximize the weighted score:
Final Score = Σ (weight × P(action))
Positive signals (higher weights):
- P(like) — immediate resonance
- P(reply) — conversation triggers
- P(repost) — share-worthy content
- P(quote) — content worth adding to
- P(click) — curiosity hooks
- P(dwell) — stops the scroll
- P(share) — off-platform worthy
- P(follow_author) — "I need more of this"
Negative signals (hurt your score):
- P(not_interested) — boring, irrelevant
- P(block_author) — annoying, spammy
- P(mute_author) — too much, too often
- P(report) — rule-breaking vibes
Optimization Framework
Optimize across these dimensions:
1. Hook Engineering (first 7 words)
- Pattern interrupt: break expectations
- Curiosity gap: open a loop that demands closing
- Specificity: concrete > abstract ("$47M" not "millions")
- Contradiction: challenge assumed beliefs
2. Emotional Resonance
Map to high-arousal emotions that drive action:
- Awe ("this changes everything")
- Anger (righteous, not toxic)
- Anxiety (FOMO, urgency)
- Surprise (unexpected reveals)
- Validation ("finally someone said it")
Avoid low-arousal states: sadness, contentment, boredom
3. Reply Maximization
Build in reply triggers:
- Hot takes that demand response
- Questions (real or rhetorical)
- Intentional incompleteness ("but there's a catch...")
- Ranking/listing that people want to argue with
- Polarizing framing on non-toxic topics
4. Repost Psychology
Make it identity-reinforcing:
- "This is the kind of person I am"
- Makes the sharer look smart/informed/funny
- Tribal signaling without being exclusionary
- Quotable standalone value
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.
- 4d ago First seen · 138 lines · 12 tokens per session scan A 55a658f951f2
viral-tweet is a command published in the GitHub repository softaworks/agent-toolkit (2,422 stars, last pushed 6mo ago), licensed MIT. It adds 12 tokens to every session and 1,021 once invoked, about $0.0001 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.
Other commands, from other repositories
add-agent
引导新增一个 Agent 适配器。用法 /add-agent.
speckit.archive
Archive a feature specification into main project memory after merge, resolving gaps and conflicts.
speckit.opsmill.retrospect
Run a session retrospective that surfaces context-management gaps and routes them to approved follow-up actions.
create-issue
Transform feature descriptions, bug reports, or improvement ideas into well-structured GitHub issues.
pr
Handle the full workflow from current branch state to an open, CI-monitored pull request.
mach12:issue-review
Read a GitHub issue and all comments, review the implementation plan, and present findings.