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/arome3/code-to-content/quick-twittergit clone --depth 1 https://github.com/arome3/code-to-contentWhat 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 | $0.00011 | $0.00425 |
| Opus 5 | $0.00005 | $0.00212 |
| Sonnet 5 | $0.00002 | $0.00085 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
quick-twitter 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 2d 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.
What it actually says
Quick Twitter Thread Generation
Generate a Twitter/X thread quickly when you already have your topic and know your audience.
Quick Mode: Skips Phase 1 (Project Analysis), Phase 2 (Audience Declaration), and Phase 4 (Optimization).
When to Use Quick Mode
- You already know your audience (assumes intermediate developers)
- Topic is clear and doesn't require codebase analysis
- Time-sensitive content needed fast
- Iterating on existing content
Differentiation (lite): Quick mode skips the interview, but still lead with the WHY (not a feature list) and land one defensible opinion. Want it unmistakably yours — something a competitor couldn't republish? Share the why, a rejected alternative, or a rough draft. See
references/differentiation.md.
Process
-
Load Format Reference Read:
skills/code-to-content/references/formats.md(twitter section) -
Generate Thread Create 5-12 tweets following this structure:
Tweet 1 (Hook): Most important - creates curiosity or challenges assumption Tweets 2-3: Context and problem setup Tweets 4-7: Core insight with examples Tweets 8-10: Solution or takeaway Final Tweet: CTA (link, follow request, engagement question)
-
Quick Validation
- Each tweet < 280 characters
- Hook creates curiosity (not generic)
- Each tweet has standalone value
- CTA at the end
-
Deliver Present with:
- Copy-ready format with
---COPY BELOW---markers - Character count per tweet
- Image suggestions for key tweets
- Best posting time suggestion
- Copy-ready format with
Topic: $ARGUMENTS
Generate the thread now. Skip confirmations and deliver directly.
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.
- 2d ago First seen · 52 lines · 11 tokens per session scan A 887c04754bac
quick-twitter is a command published in the GitHub repository arome3/code-to-content (6 stars, last pushed 2mo ago), licensed MIT. It adds 11 tokens to every session and 425 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 2026-08-31.
Other commands, from other repositories
speckit.verify-tasks
Verify tasks marked [X] in tasks.md are implemented, not phantom completions (marked done but backed by missing or dead code).
archive
Archive merged feature specs into project memory with provenance, sweep discovery, and gated cleanup.
verify
Perform a non-destructive post-implementation verification gate validating the implementation against spec.md, plan.md, tasks.md, and constitution.md.
retrospective
Perform a post-implementation retrospective analysis measuring spec adherence, implementation deviations, and lessons learned.
doctor
Validate project health: templates, agent config, Python runner/helpers, constitution, and feature artifacts.
validate
Verify spec-to-task traceability and check that completed tasks produced expected files.