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/skainguyen1412/social-media-research-skillWrote 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/skainguyen1412/social-media-research-skill/rank)<a href="https://agentmods.dev/commands/skainguyen1412/social-media-research-skill/rank"><img src="https://agentmods.dev/badge/commands/skainguyen1412/social-media-research-skill/rank/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/skainguyen1412/social-media-research-skill/rank"><img src="https://agentmods.dev/badge/commands/skainguyen1412/social-media-research-skill/rank.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 | $0.00009 | $0.00126 |
| Opus 5 | $0.00005 | $0.00063 |
| Sonnet 5 | $0.00002 | $0.00025 |
| Haiku 4.5 | $0.00001 | $0.00013 |
Grade A, and why
rank 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 13d 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
This command produces a ranked analysis. It follows the orchestrated pipeline with rank as the target.
-
Ensure raw data exists
Check if
reddit_data.jsonorx_data.jsonexists. If missing or stale, delegate to thesocial_media_fetchskill to fetch fresh data with deep depth. -
Run analysis
Read the
social_media_rankskill instructions and follow them to generateclassified_rank.json. -
Present results
Display key insights and ranked products from
classified_rank.json.
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.
- 13d ago First seen · 17 lines · 9 tokens per session scan A 99dca430130c
rank is a command published in the GitHub repository skainguyen1412/social-media-research-skill (57 stars, last pushed 6mo ago), licensed MIT. It adds 9 tokens to every session and 126 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
cancel-ralph
Cancel active Ralph Loop.
release
Git geçmişinden hedef kitleye uygun sürüm notları oluştur.
spec-forge
Use when generating software specifications — full chain (Idea→Decompose→Tech Design + Feature Specs) or individual documents.
rclone_config_dump
Dump the config file as JSON.
dd-spec-test
Generate test spec (requires confirmed status).
dd-init
Initialize product documentation structure in current directory.