last30days

last30days is a skill for Claude Code, Codex from charlesdove977/goviralbro. It costs 43 tokens per session (5,068 once invoked), scanned A, original, MIT.

A research workflow for finding and summarizing discussions from the past 30 days across Reddit, X, YouTube, and the web.

In plain words
What is it for?
It helps investigate recent news, recommendations, general topics, and prompting practices, then produce prompts that can be copied into another AI tool.
Why use it?
It addresses the problem of relying on outdated or incomplete information when researching current topics and recommendations.

Skill for Claude CodeCodex

Written for Claude Code and Codex: allowed-tools in frontmatter, but also agents/openai.yaml present. Also seen: names the AskUserQuestion tool; mentions Claude Code; mentions Codex.

Runs only inside a plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else, and the catalogue could not identify which plugin ships it.

Good fit It helps investigate recent news, recommendations, general topics, and prompting practices, then produce prompts that can be copied into another AI tool.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for last30days

README.md
[![agentmods](https://agentmods.dev/badge/skills/charlesdove977/goviralbro/last30days/github.svg)](https://agentmods.dev/skills/charlesdove977/goviralbro/last30days)
Your own site
<a href="https://agentmods.dev/skills/charlesdove977/goviralbro/last30days"><img src="https://agentmods.dev/badge/skills/charlesdove977/goviralbro/last30days/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.

agentmods 80×15 button for last30days

Your own site · 80×15
<a href="https://agentmods.dev/skills/charlesdove977/goviralbro/last30days"><img src="https://agentmods.dev/badge/skills/charlesdove977/goviralbro/last30days.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,068 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.05068
Opus 5 $0.00022 $0.02534
Sonnet 5 $0.00009 $0.01014
Haiku 4.5 $0.00004 $0.00507

Measured 12d ago against content hash 0ccfac68d398, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

last30days 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.

The scan reads SKILL.md. This mod also ships 47 executable files (scripts/briefing.py, scripts/last30days.py, scripts/lib/__init__.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

skills/last30days/SKILL.md · 502 lines

How it starts

The opening of the file, as written. The whole thing — 502 lines — stays where its author put it; the contents beside it link to each section on GitHub.

last30days v2.1: Research Any Topic from the Last 30 Days

Research ANY topic across Reddit, X, YouTube, and the web. Surface what people are actually discussing, recommending, and debating right now.

CRITICAL: Parse User Intent

Before doing anything, parse the user's input for:

  1. TOPIC: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
  2. TARGET TOOL (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
  3. QUERY TYPE: What kind of research they want:
    • PROMPTING - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
    • RECOMMENDATIONS - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
    • NEWS - "what's happening with X", "X news", "latest on X" → User wants current events/updates
    • GENERAL - anything else → User wants broad understanding of the topic

Common patterns:

  • [topic] for [tool] → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
  • [topic] prompts for [tool] → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
  • Just [topic] → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
  • "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
  • "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS

IMPORTANT: Do NOT ask about target tool before research.

  • If tool is specified in the query, use it
  • If tool is NOT specified, run research first, then ask AFTER showing results

Store these variables:

  • TOPIC = [extracted topic]
  • TARGET_TOOL = [extracted tool, or "unknown" if not specified]
  • QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]

DISPLAY your parsing to the user. Before running any tools, output:

I'll research {TOPIC} across Reddit, X, and the web to find what's been discussed in the last 30 days.

Parsed intent:
- TOPIC = {TOPIC}
- TARGET_TOOL = {TARGET_TOOL or "unknown"}
- QUERY_TYPE = {QUERY_TYPE}

Research typically takes 2-8 minutes (niche topics take longer). Starting now.

Read the full file on GitHub · 502 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

  1. 12d ago First seen · 502 lines · 43 tokens per session scan A 0ccfac68d398

Subscribe to this mod's changes

last30days is a skill published in the GitHub repository charlesdove977/goviralbro (264 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 5,068 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens