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-linkedingit 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 | $0.00430 |
| Opus 5 | $0.00005 | $0.00215 |
| Sonnet 5 | $0.00002 | $0.00086 |
| Haiku 4.5 | $0.00001 | $0.00043 |
Grade A, and why
quick-linkedin 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 LinkedIn Post Generation
Generate a LinkedIn post 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(linkedin section) -
Generate Post Structure:
Lines 1-2 (Hook): Visible before "see more" - must create curiosity Lines 3-6: Personal story with specific details Bullet Points: 3 actionable takeaways Final Line: Engagement question
Target: 800-1300 characters
-
Quick Validation
- Hook in first 2 lines creates curiosity
- Specific details (numbers, names, dates)
- Clear takeaways as bullet points
- Engagement question at end
- 800-1300 characters
- No external links in body (algorithm penalty)
-
Deliver Present with:
- Copy-ready format
- Character count
- 5 relevant hashtags
- Best posting time suggestion
Topic: $ARGUMENTS
Generate the post 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 · 55 lines · 11 tokens per session scan A 54779c6990e1
quick-linkedin 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 430 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
marketing
Command "marketing" from Infrasity-Labs/dev-gtm-claude-skills, covering seo & discoverability, content & copy, demand gen & growth, lifecycle & retention and research & competitive.
dev-gtm
Parse the arguments: $ARGUMENTS.
linkedin-growth-config
Adjust linkedin-growth account settings — daily invite limit, schedule time, pending threshold, pause/resume — or edit the ICP qualification prompt.
speckit.verify-tasks
Verify tasks marked [X] in tasks.md are implemented, not phantom completions (marked done but backed by missing or dead code).
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.