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-bloggit 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 | $0.00010 | $0.00424 |
| Opus 5 | $0.00005 | $0.00212 |
| Sonnet 5 | $0.00002 | $0.00085 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
quick-blog 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 Blog Post Generation
Generate a blog post quickly when you already have your topic defined.
Quick Mode: Skips Phase 1 (Deep Project Analysis) and Phase 4 (Optimization). Still requires audience declaration.
When to Use Quick Mode
- Topic is already well-defined
- You don't need codebase mining for insights
- Time-sensitive content
- You know what you want to write about
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
-
Declare Audience (Required) Ask user: Who is this for?
- Beginner (grade ≤ 8, jargon ≤ 2%)
- Intermediate (grade ≤ 12, jargon ≤ 4%)
- Expert (grade ≤ 16, jargon ≤ 8%)
-
Load Format Reference Read:
skills/code-to-content/references/formats.md(blog section) -
Generate Content Structure: Hook → Problem → Insight → Solution → Takeaway
Hook Rules:
- Start with surprising result or compelling problem
- NEVER "In this article, I will..."
- Create immediate curiosity
-
Quick Validation
- Hook creates curiosity
- Clear problem → solution arc
- Matches declared audience complexity
- Actionable takeaway at end
-
Deliver Present with:
- 3 title options
- Meta description (150-160 chars)
- Complete content
- Suggested header image concept
Topic: $ARGUMENTS
Proceed with audience declaration, then generate 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 · 57 lines · 10 tokens per session scan A dfccc8ce1d1b
quick-blog is a command published in the GitHub repository arome3/code-to-content (6 stars, last pushed 2mo ago), licensed MIT. It adds 10 tokens to every session and 424 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.