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.
git clone --depth 1 https://github.com/classicchins/compounding-marketingWrote 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/classicchins/compounding-marketing/cm-social)<a href="https://agentmods.dev/commands/classicchins/compounding-marketing/cm-social"><img src="https://agentmods.dev/badge/commands/classicchins/compounding-marketing/cm-social.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 5.1 | $0.00000 | $0.02939 |
| Opus 5 | $0.00000 | $0.01470 |
| Sonnet 5 | $0.00000 | $0.00588 |
| Haiku 4.5 | $0.00000 | $0.00294 |
Grade A, and why
cm-social 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 8d 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 — 459 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cm:social — Social Media Campaign Planning
Plan and execute a complete social media campaign across platforms with clear goals, content calendar, and engagement tactics.
What It Does
Guides you through social campaign planning from goal-setting to execution, with platform-specific strategies and a 30-day content calendar template.
Process
Step 1: Define Campaign Goal
Choose ONE primary goal:
Awareness → Reach new audiences, build brand visibility
- Metrics: Impressions, reach, follower growth
- Platforms: Twitter (virality), LinkedIn (B2B reach), Instagram (visual)
Engagement → Build community, start conversations
- Metrics: Likes, comments, shares, reply rate
- Platforms: Twitter (conversations), LinkedIn (thought leadership), Reddit (niche communities)
Conversion → Drive signups, demos, downloads
- Metrics: Link clicks, conversions, cost per acquisition
- Platforms: LinkedIn (B2B), Facebook/Instagram (paid ads), Twitter (DMs)
Write your goal:
"Increase [metric] by [X%] in [timeframe] via [platforms]"
Example:
"Increase demo bookings by 30% in 30 days via LinkedIn + Twitter organic content"
Step 2: Platform Selection
Match platform to goal + audience:
LinkedIn (B2B Decision-Makers)
Best for:
- Thought leadership
- B2B SaaS positioning
- Recruitment / employer branding
- CEO / executive visibility
Content that works:
- Personal stories (career lessons, founder journey)
- Industry insights (hot takes, trend analysis)
- How-to guides (tactical value)
- Company milestones (funding, launches, hiring)
Frequency: 3-5 posts/week (consistency > volume)
Engagement tactics:
- Comment on other posts (first-comment strategy)
- Tag relevant people (not spammy)
- Use carousels (higher engagement than single images)
- Native video > external links (LinkedIn prioritizes native)
Twitter/X (Developers, Tech Enthusiasts, Early Adopters)
Best for:
- Real-time updates
- Product launches (tech audience)
- Viral reach (retweets > likes)
- Customer support / community
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.
- 8d ago First seen · 459 lines · 0 tokens per session scan A 2078ecb3e3e3
cm-social is a command published in the GitHub repository classicchins/compounding-marketing (8 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,939 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 2026-08-31.
Other commands, from other repositories
checklist
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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.