agentmods.dev

last30days

last30days is a skill for Claude Code from aleksbuss/orchestra. It costs 50 tokens per session (6,598 once invoked), scanned A, original, MIT.

A research skill that investigates what people have discussed during the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, and the web.

In plain words
What is it for?
Use it to research a topic, find current recommendations, check recent news, or create ready-to-copy prompts for another AI tool.
Why use it?
It gathers recent discussions, recommendations, news, and debates instead of relying on older knowledge.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. 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.

not rated 2repo yesterday A scan 50 tokens original MIT

Good fit Use it to research a topic, find current recommendations, check recent news, or create ready-to-copy prompts for another AI tool.

Compare 6 skills from other repositories ↓
View source ↗ aleksbuss/orchestra aleksejs-portfolio.pages.dev
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.

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/aleksbuss/orchestra/last30days/github.svg)](https://agentmods.dev/skills/aleksbuss/orchestra/last30days)
Your own site
<a href="https://agentmods.dev/skills/aleksbuss/orchestra/last30days"><img src="https://agentmods.dev/badge/skills/aleksbuss/orchestra/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/aleksbuss/orchestra/last30days"><img src="https://agentmods.dev/badge/skills/aleksbuss/orchestra/last30days.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,598 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 $0.00050 $0.06598
Opus 5 $0.00025 $0.03299
Sonnet 5 $0.00010 $0.01320
Haiku 4.5 $0.00005 $0.00660

Measured 9d ago against content hash db47e980fdee, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 9d ago.

The scan reads SKILL.md. This mod also ships 47 executable files (scripts/briefing.py, scripts/evaluate-synthesis.py, scripts/generate-synthesis-inputs.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.

bundled-skills/last30days/SKILL.md · 593 lines

How it starts

The opening of the file, as written. The whole thing — 593 lines — stays where its author put it; the contents beside it link to each section on GitHub.

last30days v2.5: Research Any Topic from the Last 30 Days

Research ANY topic across Reddit, X, YouTube, Hacker News, Polymarket, and the web. Surface what people are actually discussing, recommending, betting on, 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 · 593 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. 9d ago First seen · 593 lines · 50 tokens per session scan A db47e980fdee

Subscribe to this mod's changes

last30days is a skill published in the GitHub repository aleksbuss/orchestra (2 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 6,598 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 2026-08-31.

LLMResearchai-agentsai-workspacellm-orchestrationmixture-of-agentsmulti-agent-systems
Related

Other skills, from other repositories

data-analysis

A careful workflow for working with table-like files such as CSV, Excel, TSV, JSON, and Parquet. It requires inspecting the data before writing small analysis steps, then checking each result.

opencmit/alphora · 94 tokens

deep-research

Use this skill for any task that requires in-depth research, investigation, or comprehensive report generation on a topic. This includes: producing industry analysis, market research, technology surveys, competitive intelligence, trend reports, or any deliverable that synthesizes information from multiple sources into…

opencmit/alphora · 159 tokens

ama-memory

Use AMA memory in OpenClaw to recall prior context, capture turns, inspect stored state, end sessions, or delete a user's memory when asked.

Sherlockwz/AMA · 33 tokens

cg-work-memory

Use when a local agent needs to submit a concise public work-memory report for its own completed local task through cg report work-memory.

Intelligent-Internet/CommonGround · 31 tokens

comm-agent-skill

Communication specialist for the OpenClaw multi-agent system. Use this skill when the task involves: drafting or sending emails, posting to Discord or other chat platforms, scheduling calendar events, sending notifications, or any outbound communication on behalf of the user. Always drafts before sending and requires…

parijatmukherjee/openclaw-hawkins · 74 tokens

data-agent-skill

Data processing and analysis specialist for the OpenClaw multi-agent system. Use this skill when the task involves: parsing CSV/JSON/Excel files, data cleaning and transformation, SQL queries, statistical analysis, generating charts or visualizations, aggregating data from multiple sources, or extracting insights from…

parijatmukherjee/openclaw-hawkins · 88 tokens