Borrowing it
Nothing to install: this file belongs to mtarcure/claude-vibe-squad. 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/mtarcure/claude-vibe-squad/main/.agents/skills/virality-analysis/SKILL.mdgit clone --depth 1 https://github.com/mtarcure/claude-vibe-squadWrote 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/skills/mtarcure/claude-vibe-squad/virality-analysis)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/virality-analysis"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/virality-analysis/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/skills/mtarcure/claude-vibe-squad/virality-analysis"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/virality-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.00395 |
| Opus 5 | $0.00028 | $0.00198 |
| Sonnet 5 | $0.00011 | $0.00079 |
| Haiku 4.5 | $0.00006 | $0.00040 |
Grade A, and why
virality-analysis 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 10d 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
Virality Analysis
Analyze a platform-specific sharing hypothesis — hook, retention, shareability, and loop — and turn it into an evidence-bounded experiment rather than a causal story.
Required experiment record
For each hypothesis, record the platform, source, observation window, cohort definition, metric names and formulas, alternative hypotheses, current confidence, intervention/test, stopping rule, and result status. Also state whether the test needs paid distribution, credentials, or private audience/analytics data; those requirements stay behind their separate operator, budget, and privacy gates.
Steps
- Pin the platform, evidence source, observation window, cohort, and metric definitions before interpreting signals.
- Identify a candidate share driver: the emotion or utility that might make a person pass it on.
- Analyze hook and retention structure against the platform mechanics observed in the same window.
- Map the sharing/growth loop and state at least one alternative explanation for every proposed mechanism.
- Assign confidence from observed signals, never fabricated views, retention, or engagement rates.
- Define a testable intervention and stopping rule; mark gated or unavailable analytics as unmeasured, not inferred.
- Update the experiment record with the outcome and retain alternatives that the result did not distinguish.
Acceptance
- Every recommendation is tied to one platform-specific experiment record and mechanism.
- Source/window/cohort and metric formulas are explicit; alternatives and confidence are recorded.
- A test and stopping rule exist before results are interpreted.
- No causal claim exceeds the experiment, no metric is fabricated, and paid/credential/private-data needs remain gated.
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.
- 10d ago First seen · 33 lines · 56 tokens per session scan A b33f91889dc2
virality-analysis is a skill published in the GitHub repository mtarcure/claude-vibe-squad (122 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 395 once invoked, about $0.0003 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 skills, from other repositories
contributor-onboarding
Help a new contributor get productive on this checkout - inspect sync state against main, build, run the repository's exact verification gate, and produce a local what's-new digest. Never fetches, pulls, or modifies a dirty tree on its own. Explicit-only.
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.
gh-file-issue
Use when filing a new Codewhale GitHub issue: turn a bug or idea into a well-formed, actionable issue with repro, acceptance criteria, labels, and milestone.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
gh-treasure-hunt
Hunt the issue/PR queue for highest value-over-risk wins: clean focused community PRs, already-implemented issues to close, safe quick-fixes.
recording
Capture screen recordings and screenshots on any registered computer (macOS, Windows, Linux, HarmonyOS) and manage the recording library.