# Signal Scan

> Signal scan

- Skill: `matteotitta/signal-scan` (Agent Skill)
- Install (CLI): `npx skillmds@latest add matteotitta/signal-scan`
- Raw SKILL.md: https://api.skillmd.com/api/skills/matteotitta/signal-scan/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: matteotitta (https://skillmd.com/u/matteotitta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/matteotitta/signal-scan

---


# Signal scan

Answer "what happened with **{topic}** in the last **N** days?" for any person, company, product, technology, or market term. The brief is ranked by what people actually engaged with — upvotes, likes, views, GitHub stars, prediction-market odds — not by SEO. Knowledge type: `temporal-signal-brief` (per `.claude/rules/ontology.md`); maturity: emergent (each run is fresh, time-bound — briefs are not locked).

Stolen MCP-native from [`mvanhorn/last30days-skill`](https://github.com/mvanhorn/last30days-skill) (MIT). The method is the steal — entity-resolution-before-search, peer expansion, story clustering, engagement ranking. The 1,036-line Python engine is **not**: every surface maps to an MCP we already run (Exa, Firecrawl, GitHub, youtube-transcript, Apify). Provenance + verdict table: `.claude/discovery/0626-last30days-skill-steal-analysis.md`.

## When to run

Invoke for: `what happened with {topic} in the last 30 days`, `recent activity on {company}`, `what's new with {technology}`, `signal scan on {market}`, pre-call prospect prep, weekly newsletter sourcing, competitor recency checks.

Do **NOT** invoke for:

- Internal decision history / "what did we decide" → `/think` (searches our own decision logs).
- "What did we discuss in past sessions" → `/recall` (session DB).
- A deep, static, 13-dimension competitor dossier → `/competitor-research`. (This skill is shallow + recent + topic-agnostic; it feeds that skill's "Recent changes" header.)
- A curated newsletter from links you already collected → `/gtme-pulse` (this skill *sources* the raw signal that feeds it).

**Brain-first (mandatory):** before any external call, run the `.claude/rules/brain-first-lookup.md` ladder — `/recall {topic}` + grep client folders. A locked client doc may already hold what a scan would rediscover. Annotate the brief if you went external after a brain miss.

## Inputs + flags

**Required:** `topic` — the thing to scan (person / company / product / technology / market term). If ambiguous (e.g. "Pivot", "Base", "Bolt"), confirm the disambiguator before running.

**Flags:**

| Flag | Default | Effect |
|------|---------|--------|
| `--days N` | 30 | Lookback window. `--days 14` for fast-moving, `--days 90` for slower markets. |
| `--compare "A vs B"` | off | Per-entity parallel pipelines, merged into a head-to-head brief. |
| `--emit=html` | markdown | Self-contained shareable HTML file instead of markdown. |
| `--x` | off | Add X/Twitter via Apify actor (**credit-gated**). |
| `--tiktok` | off | Add TikTok via Apify actor (**credit-gated**; thin for most B2B topics). |
| `--instagram` | off | Add Instagram via Apify actor (**credit-gated**; thin for most B2B topics). |
| `--markets` | off | Add Polymarket / Kalshi odds via Firecrawl (free; relevant only for forward-looking topics). |

## The pipeline

Seven steps. Full detail → the premium reference. Surface→tool mapping → the premium reference.

1. **Brain-first check.** `/recall {topic}` + grep client folders (per `brain-first-lookup.md`). Use what's already known; only go external for the gap.
2. **Resolve entities before searching.** Find the topic's X/LinkedIn handle, GitHub user/org/repo, the subreddits where its category is discussed, and its domain — *before* any keyword search. This is the step that turns a keyword dump into signal. Kills collisions (searching "42" → Jackie Robinson jerseys; "Pivot" → gymnastics).
3. **Expand to peer communities.** If the topic is a product in a known category, add the cross-product communities where practitioners actually compare tools (not just the brand's own mentions). Annotate the brief with the peer set used.
4. **Generate a query plan.** *You* (the model) write the plan — intent / freshness mode / cluster mode / 1–4 subqueries with per-surface weights. Schema → the premium reference. No engine plans this; Claude does.
5. **Parallel fan-out, date-filtered.** Run the resolved surfaces concurrently, each bounded to the `--days` window. Probe one surface before fanning out (per `goal-driven-loops.md`). Free discovery before metered extraction (per `crawl-cost-discipline.md`). Gate every paid Apify call (per `apify-credits.md`).
6. **Cluster + rank.** Merge the same story across surfaces into one item (HN + Reddit + newsletter on the same launch = one clustered signal, not three). Rank by engagement (upvotes / likes / views / stars / odds) with recency decay.
7. **Synthesize a cited brief.** Narrative prose, inline citations with access dates, per the premium reference. Default-on surfaces: Reddit, HN, news/funding, GitHub, G2/Trustpilot/Product Hunt, YouTube. Flag-gated: X/TikTok/IG/markets.

## Credit gate

X / TikTok / Instagram run through Apify actors and are **off by default**. When a flag turns one on, follow `.claude/rules/apify-credits.md`: `fetch-actor-details` (free) first, estimate cost, gate before `call-actor` (soft <$5, hard ≥$5). Deep-Reddit via Apify is also credit-gated; the default Reddit surface uses free Exa site-filtered search. `--markets` (Firecrawl) and all default-on surfaces incur no Apify spend. Probe one item before any batch fan-out.

## Quality gate (binary, before declaring done)

- Brain-first ladder run before any external call; brief annotated if it went external after a miss.
- Entities resolved before searching (handle / repo / subreddits named in the brief, or explicitly "none found").
- Peer set named when the topic is a product in a known category.
- Every claim has an inline citation + access date; no invented engagement numbers (mark `[Not available]`).
- Date window respected — nothing older than `--days N` in the brief (or flagged as background context).
- Story-level dedup applied — no single story appearing 3× from 3 surfaces.
- Coverage footer lists which surfaces ran, were thin, or were skipped — no silent truncation.
- Apify flags: cost estimated + gated before any paid call.

## Composition

| Rule | Role |
|------|------|
| `brain-first-lookup.md` | Step 1 — check the brain before external. |
| `exa-protocol.md` | Tool selection + citation standard for the default surfaces. |
| `crawl-cost-discipline.md` | Free discovery (spider_links / sitemap) before metered extraction. |
| `apify-credits.md` | Gate every paid Apify call (X/TikTok/IG/deep-Reddit). |
| `goal-driven-loops.md` | Probe-one-before-fan-out on the parallel surfaces. |
| `outbound-research-hygiene.md` | When a signal feeds outbound copy — dated, ≤12mo, current-company-only. |
| `output-tenets.md` · `output-simplicity.md` · `doc-output-structure.md` · `ai-speak-anti-patterns.md` | Brief voice + structure + source placement. |