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.
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.
[](https://agentmods.dev/skills/charlesdove977/goviralbro/last30days)<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.
<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>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 | $0.00043 | $0.05068 |
| Opus 5 | $0.00022 | $0.02534 |
| Sonnet 5 | $0.00009 | $0.01014 |
| Haiku 4.5 | $0.00004 | $0.00507 |
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.
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- last30days — 89% identical, 110 lines differ
- last30days — 86% identical, 207 lines differ
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:
- TOPIC: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
- TARGET TOOL (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
- 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.
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.
- agents/openai.yaml 448 B
- CHANGELOG.md 3.6 KB
- fixtures/models_openai_sample.json 745 B
- fixtures/models_xai_sample.json 385 B
- fixtures/openai_sample.json 2.2 KB
- fixtures/reddit_thread_sample.json 3.8 KB
- fixtures/xai_sample.json 2.5 KB
- README.md 53 KB
- scripts/briefing.py 8.0 KB runs code
- scripts/last30days.py 42 KB runs code
- scripts/lib/__init__.py 29 B runs code
- scripts/lib/bird_x.py 14 KB runs code
- scripts/lib/brave_search.py 5.9 KB runs code
- scripts/lib/cache.py 4.7 KB runs code
- scripts/lib/dates.py 3.2 KB runs code
- scripts/lib/dedupe.py 3.5 KB runs code
- scripts/lib/entity_extract.py 4.1 KB runs code
- scripts/lib/env.py 8.5 KB runs code
- scripts/lib/http.py 5.6 KB runs code
- scripts/lib/models.py 4.6 KB runs code
- scripts/lib/normalize.py 5.9 KB runs code
- scripts/lib/openai_reddit.py 12 KB runs code
- scripts/lib/openrouter_search.py 6.5 KB runs code
- scripts/lib/parallel_search.py 3.9 KB runs code
- scripts/lib/reddit_enrich.py 7.5 KB runs code
- scripts/lib/render.py 17 KB runs code
- scripts/lib/schema.py 13 KB runs code
- scripts/lib/score.py 11 KB runs code
- scripts/lib/ui.py 19 KB runs code
- scripts/lib/vendor/bird-search/bird-search.mjs 3.7 KB runs code
- scripts/lib/vendor/bird-search/lib/cookies.js 6.1 KB runs code
- scripts/lib/vendor/bird-search/lib/features.json 523 B
- scripts/lib/vendor/bird-search/lib/paginate-cursor.js 1.2 KB runs code
- scripts/lib/vendor/bird-search/lib/query-ids.json 815 B
- scripts/lib/vendor/bird-search/lib/runtime-features.js 4.9 KB runs code
- scripts/lib/vendor/bird-search/lib/runtime-query-ids.js 9.2 KB runs code
- scripts/lib/vendor/bird-search/lib/twitter-client-base.js 4.7 KB runs code
- scripts/lib/vendor/bird-search/lib/twitter-client-constants.js 2.5 KB runs code
- scripts/lib/vendor/bird-search/lib/twitter-client-features.js 18 KB runs code
- scripts/lib/vendor/bird-search/lib/twitter-client-search.js 7.1 KB runs code
- scripts/lib/vendor/bird-search/lib/twitter-client-types.js 59 B runs code
- scripts/lib/vendor/bird-search/lib/twitter-client-utils.js 19 KB runs code
- scripts/lib/vendor/bird-search/LICENSE 1.0 KB
- scripts/lib/vendor/bird-search/package.json 331 B
- scripts/lib/websearch.py 11 KB runs code
- scripts/lib/xai_x.py 6.5 KB runs code
- scripts/lib/youtube_yt.py 11 KB runs code
- scripts/store.py 20 KB runs code
- scripts/sync.sh 1.2 KB runs code
- scripts/watchlist.py 9.1 KB runs code
- SPEC.md 3.1 KB
- tests/__init__.py 19 B runs code
- tests/test_cache.py 1.9 KB runs code
- tests/test_dates.py 3.6 KB runs code
- tests/test_dedupe.py 3.8 KB runs code
- tests/test_models.py 4.1 KB runs code
- tests/test_normalize.py 4.1 KB runs code
- tests/test_openai_reddit.py 2.7 KB runs code
- tests/test_render.py 3.3 KB runs code
- tests/test_score.py 5.2 KB runs code
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.
- 12d ago First seen · 502 lines · 43 tokens per session scan A 0ccfac68d398
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.
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