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.
npx skills add getaero-io/gtm-eng-skills --skill niche-signal-discoverygit clone --depth 1 https://github.com/getaero-io/gtm-eng-skillsWrote 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/getaero-io/gtm-eng-skills/niche-signal-discovery)<a href="https://agentmods.dev/skills/getaero-io/gtm-eng-skills/niche-signal-discovery"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/niche-signal-discovery/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/getaero-io/gtm-eng-skills/niche-signal-discovery"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/niche-signal-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00135 | $0.04775 |
| Opus 5 | $0.00068 | $0.02388 |
| Sonnet 5 | $0.00027 | $0.00955 |
| Haiku 4.5 | $0.00014 | $0.00477 |
Grade A, and why
niche-signal-discovery scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Niche Signal Discovery
Quick Start
npm install -g deepline
# Fallback for secure sandboxes: mkdir -p "$HOME/.local" && npm config set prefix "$HOME/.local" && export PATH="$HOME/.local/bin:$PATH" && npm install -g deepline --registry https://code.deepline.com/api/v2/npm/
deepline auth register --wait auto
deepline auth wait --timeout 120 # completes Cowork/browser approval; no-op if already connected
deepline auth status
deepline -h
CLI resolution
Run deepline when it is available. If the shell reports that command is missing, use <workspace-root>/.deepline/runtime/bin/deepline (or the npm-created .cmd shim on Windows). If neither exists, follow https://code.deepline.com/INSTALL.md to set up Deepline.
Discover differential signals between Closed Won and Closed Lost accounts by extracting multi-page website content and job listings, then computing Laplace-smoothed lift scores to identify what distinguishes buyers from non-buyers.
Prerequisites
- Deepline CLI — All enrichment runs through
deepline enrich; route through prebuilt plays and customer-configured provider connections rather than hardcoding provider-specific prospecting tools. - Python 3 stdlib only — no pip dependencies for any shipped script.
- Credits - paid web extraction plus CrustData job search. Run a small sample or
deepline tools describe crustdata_v2_job_search --jsonfor current Deepline-facing pricing before scaling. Step 7 contact discovery is additional. Always get user approval before paid steps.
Deepline-First Principle
Use deepline enrich for all enrichment and deepline tools execute for one-offs. Inspect CSV shape and samples with deepline csv show; inspect run state with the run/play URL or deepline runs get when a run id is available. Reruns are idempotent. Refer to deepline-gtm for command patterns and provider playbooks.
Input requirements
- Won and lost customer domain lists (≥20 won + ≥10 lost for statistical significance)
- Lookalikes can supplement Won if Closed Won < 15. Add a Dataset Caveat to the report.
- Target company context from Step 0 — what they sell, who they sell to, key personas.
What ships with it
13 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.
- references/dedupe.md 4.6 KB
- references/keyword-catalog.md 13 KB
- references/pitfalls.md 4.8 KB
- references/proven-signals.md 3.2 KB
- references/quality-gate.md 3.4 KB
- references/report-template.md 14 KB
- references/scoring-pitfalls.md 4.2 KB
- references/signal-interpretation.md 4.6 KB
- references/step-7-prospects.md 7.8 KB
- scripts/analyze_signals.py 17 KB runs code
- scripts/dedupe_utils.py 16 KB runs code
- scripts/find_contacts.py 24 KB runs code
- skill-metadata.json 1.6 KB
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 · 322 lines · 135 tokens per session scan A bb0c88382b67
niche-signal-discovery is a skill published in the GitHub repository getaero-io/gtm-eng-skills (58 stars, last pushed yesterday), licensed MIT. It adds 135 tokens to every session and 4,775 once invoked, about $0.0007 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.
Other skills, from other repositories
inbound-lead-enrichment
Fills in missing data for inbound leads — researches the company, identifies the person's role and seniority, finds other stakeholders at the company, checks for existing CRM relationships, and updates the lead record. Produces enriched lead data ready for qualification or outreach. Tool-agnostic.
inbound-lead-qualification
Qualifies inbound leads against full ICP criteria — company size, industry, use case fit, role/seniority of the person. Checks CRM and existing customer base for duplicates and existing relationships. Outputs a scored CSV with qualification status, reasoning, and pipeline overlap flags. Tool-agnostic — works with any…
inbound-lead-triage
Triages all inbound leads from a given period — demo requests, free trial signups, content downloads, webinar registrations, chatbot conversations. Classifies by urgency, qualifies against ICP, enriches with context, and produces a prioritized action queue with recommended response for each lead. Tool-agnostic — works…
company-contact-finder
Find decision-makers at a specific company using Apollo, Crustdata, Fiber, and PDL people search via Gooseworks MCP. Given a company name and target titles, returns a list of contacts with name, title, LinkedIn URL, and location.
job-scraper
Search for job postings across LinkedIn and Indeed. Use when users want to find open roles, monitor hiring signals, identify companies hiring for specific positions, or research competitor hiring activity. Returns job title, company, location, salary, description, seniority level, and direct apply URLs. No login or…
apollo-lead-finder
Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Creates Apollo lists. Deduplicates against existing contacts by LinkedIn URL.