Borrowing it
Nothing to install: this file belongs to charlesdove977/goviralbro. 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/charlesdove977/goviralbro/main/.claude/commands/viral-analyze.mdgit clone --depth 1 https://github.com/charlesdove977/goviralbroWrote 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/charlesdove977/goviralbro/viral-analyze)<a href="https://agentmods.dev/commands/charlesdove977/goviralbro/viral-analyze"><img src="https://agentmods.dev/badge/commands/charlesdove977/goviralbro/viral-analyze/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/charlesdove977/goviralbro/viral-analyze"><img src="https://agentmods.dev/badge/commands/charlesdove977/goviralbro/viral-analyze.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 | $0.16446 |
| Opus 5 | $0.00000 | $0.08223 |
| Sonnet 5 | $0.00000 | $0.03289 |
| Haiku 4.5 | $0.00000 | $0.01645 |
Grade A, and why
viral-analyze scanned grade A with 2 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import json, urllib.request, os Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
v = subprocess.check_output(['yt-dlp', '--version']).decode().strip() How it starts
The opening of the file, as written. The whole thing — 1,614 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/viral:analyze — Multi-Platform Analytics Collection
You are the Analytics Collection Engine for the Viral Command system. You gather performance data for published content across all platforms, store it for pattern analysis, and prepare the feedback loop.
Arguments
$ARGUMENTS
Parse for:
--youtube— Analyze YouTube content only--instagram— Analyze Instagram content only--all— Analyze all supported platforms (default if no platform flag — YouTube + Instagram)--content-id [ID]— Analyze a specific script by ID--recent [N]— Analyze last N published pieces (default: 5)--longform— Analyze YouTube longform (5+ min) only; skip Shorts and Instagram--shorts— Analyze YouTube Shorts + Instagram Reels only; skip longform--manual— Non-interactive mode for cron/automation (skips prompts, analyzes all published content, runs full pipeline A-H without pauses)--deep-analysis— Skip straight to Phase G.6 top 10 ranking + transcript/visual analysis (no new analytics collection — uses existing data fromanalytics.jsonl)
Phase A: Initialization & Mode Selection
Step 1: Load Agent Brain
Read data/agent-brain.json and extract:
platforms.posting— which platforms the user posts toplatforms.api_keys_configured— which APIs are set upperformance_patterns.total_content_analyzed— how much data we have
Step 1.2: Analytics Connection Detection
Skip this step if --manual flag is present.
Using the brain data loaded in Step 1, check analytics connection status for each platform in platforms.posting:
YouTube Analytics:
- Connected if:
"youtube_analytics_v2"inapi_keys_configuredAND~/.viral-command/yt-token.jsonexists - Missing if: either condition is false
Instagram Graph API:
- Connected if:
"instagram_graph_api"inapi_keys_configuredANDINSTAGRAM_ACCESS_TOKENset in.env - Missing if: either condition is false
If all relevant connections are present (or platform not in scope): Skip to Step 1.5 silently. No output.
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.
- 11d ago First seen · 1,614 lines · 0 tokens per session scan A 99a7640191fe
viral-analyze is a command published in the GitHub repository charlesdove977/goviralbro (264 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 16,446 tokens. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.