last30days

last30days is a skill for Claude Code from tonydzi/second-brain-starter-kit. It costs 91 tokens per session (1,207 once invoked), scanned A, original, MIT.

A trend-monitoring skill that finds what has changed on a topic during the last 30 days using locally collected channel data.

In plain words
What is it for?
Use it before planning or research to identify recent signals, reuse existing topic summaries, and prepare the questions that deeper research should answer.
Why use it?
It gives strategy work a recent evidence base without repeating an already fresh search or using live network requests.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents; mentions Claude Code.

Part of the second-brain-skills plugin — 57 skills shipped together

Good fit Use it before planning or research to identify recent signals, reuse existing topic summaries, and prepare the questions that deeper research should answer.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tonydzi/second-brain-starter-kit/last30days
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add tonydzi/second-brain-starter-kit --skill last30days
Clone the repo
git clone --depth 1 https://github.com/tonydzi/second-brain-starter-kit

Made for: Claude Code.

Or install second-brain-skills, the plugin that ships this one along with the rest of its 57 skills.

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/tonydzi/second-brain-starter-kit/last30days.svg)](https://agentmods.dev/skills/tonydzi/second-brain-starter-kit/last30days)
Your own site
<a href="https://agentmods.dev/skills/tonydzi/second-brain-starter-kit/last30days"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/last30days.svg" alt="Measured on agentmods" height="20"></a>
Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,207 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 $0.00091 $0.01207
Opus 5 $0.00046 $0.00603
Sonnet 5 $0.00018 $0.00241
Haiku 4.5 $0.00009 $0.00121

Measured 7d ago against content hash 6b5abb1423eb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, 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 7d 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.

skills/last30days/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/last30days — what's new on topic X over the last 30 days

🧒 End the reply to the operator with a short "In plain words" recap (memory eli5-always).

A fast trend-watch BEFORE strategy: before planning or launching a Deep Research, take a 10-second slice of "what actually moved on this topic in the last 30 days". This is the entry into the GAP phase of the Alpha Protocol (/alfa): recall knows the operator's past, /last30days adds fresh signal from outside → together they outline the hole a DR has to fill.

Three-layer design ([[skill-design-three-layer]]): a thin skill (UX) → a deterministic engine (0 tokens) → an existing store (8 channel databases). The cost ladder [[vault-data-architecture]]: a SQL slice answers cheaply, the LLM only synthesizes the top hits.

Steps

0. RECALL (don't duplicate). Is there already a fresh slice on this topic? Check $IMPORTS_ROOT/alpha\candidates\_last30days-<topicslug>.md plus memory/the vault (/ask <topic>). If the slice is fresh (from today) — reuse it, don't re-run.

1. Deterministic slice (0 tokens, 0 network). Expand the topic into synonyms in both languages you collect in (the model judges what matters): e.g. the topic "sub-agents" → mcp, sub-agent, subagent, agent, orchestr, swarm.

set PYTHONIOENCODING=utf-8
python $IMPORTS_ROOT/watchers\last30days.py --topic "<term1, term2, …>" --days 30 --top 25 --json

→ slices the 8 channel databases (_imports\alpha\<slug>\<slug>.db, refreshed nightly) by window × keys, scores through mine_channel.score, dedups, writes the digest _imports\alpha\candidates\_last30days-<topicslug>.md and prints the top as JSON. It does not re-scrape — the databases are updated by the nightly watch_run.py. Need a guarantee of freshness right now → add --refresh (goes to the network on the subscription session). Empty result → widen the synonyms / raise --days.

2. (optional) Outside freshness — WebSearch. If the topic reaches beyond the operator's Telegram channels (market/releases/competitors) — 1-2 WebSearch queries over the same keys, windowed to the last 30 days. It complements the channel slice, it does not replace it. Skip it for narrowly internal topics.

Read the full file on GitHub · 56 lines

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. 7d ago First seen · 56 lines · 91 tokens per session scan A 6b5abb1423eb

Subscribe to this mod's changes

last30days is a skill published in the GitHub repository tonydzi/second-brain-starter-kit (5 stars, last pushed 8d ago), licensed MIT. It adds 91 tokens to every session and 1,207 once invoked, about $0.0005 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.

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