Borrowing it
Nothing to install: this file belongs to panguard-ai/panguard-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/panguard-ai/panguard-ai/main/.claude/commands/content-calendar.mdgit clone --depth 1 https://github.com/panguard-ai/panguard-aiWrote 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/panguard-ai/panguard-ai/content-calendar)<a href="https://agentmods.dev/commands/panguard-ai/panguard-ai/content-calendar"><img src="https://agentmods.dev/badge/commands/panguard-ai/panguard-ai/content-calendar/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/commands/panguard-ai/panguard-ai/content-calendar"><img src="https://agentmods.dev/badge/commands/panguard-ai/panguard-ai/content-calendar.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.00488 |
| Opus 5 | $0.00000 | $0.00244 |
| Sonnet 5 | $0.00000 | $0.00098 |
| Haiku 4.5 | $0.00000 | $0.00049 |
Grade A, and why
content-calendar 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/content-calendar — Generate Weekly Content Calendar
Generate a 1-week or 4-week content calendar for Panguard AI marketing.
Input
- Duration: "1 week" (default) or "4 weeks"
- Optional focus: product launch, feature highlight, community growth, etc.
Calendar Format
For each day, specify: | Day | Platform | Type | Topic | Target Keyword | Status |
Platform Schedule
- Mon: Twitter (product tip) + Blog draft start
- Tue: LinkedIn (thought leadership) + Twitter (threat intel)
- Wed: Twitter (behind the scenes) + Blog publish
- Thu: LinkedIn (industry insight) + Twitter (comparison)
- Fri: Twitter (community) + Blog draft start
- Sat: Blog publish + Reddit/HN if appropriate
- Sun: Rest / plan next week
Content Pillars (rotate)
- Product Education — How to use Scan, Guard, Chat, Trap, Report
- Threat Intelligence — Real threat trends, ATR rule spotlights
- Open Source — ATR contributions, community milestones
- Compliance — TCSA, ISO 27001, SOC 2 automation tips
- Developer Experience — CLI tips, MCP integration, CI/CD setup
- Industry POV — Why SMBs are targets, AI security landscape
SEO Keywords to Target (rotate across blog posts)
- "server security tool for small business"
- "endpoint protection for developers"
- "free server security scan"
- "AI security monitoring CLI"
- "sigma rules explained"
- "YARA rules tutorial"
- "AI agent security threats"
- "open source SIEM alternative"
- "taiwan TCSA compliance"
- "SOC 2 compliance automation"
Output
Return the calendar as a markdown table. Include direct links to use other skills:
- Use
/write-blog <topic>to generate blog posts - Use
/write-twitter <topic>to generate tweets - Use
/write-linkedin <topic>to generate LinkedIn posts
Context
Read packages/website/src/data/blog-posts.ts and packages/website/src/data/changelog-entries.ts to avoid duplicating existing content and to find inspiration from recent product changes.
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 · 58 lines · 0 tokens per session scan A e747806c8870
content-calendar is a command published in the GitHub repository panguard-ai/panguard-ai (63 stars, last pushed 20d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 488 tokens. 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.