Pull PredictLeads buying-intent signals (jobs, news, funding, tech, leadership changes) for a result set of companies and write them back into the local cache.

MITAuto-check: notesDatabases

Install Enrich With Signals

skills CLI
$ npx skills add Othmane-Khadri/YALC-the-GTM-operating-system --skill enrich-with-signals -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Othmane-Khadri/YALC-the-GTM-operating-system enrich-with-signals --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Othmane-Khadri/YALC-the-GTM-operating-system.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/enrich-with-signals .claude/skills/enrich-with-signals && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
enrich-with-signals
GitHub stars
318
Token cost
~560 tokens
SKILL.md length
219 words
Files
2 (incl. references)
Skills in repo
57
Repo updated
First seen
Licence
MIT

At a glance

Pull PredictLeads buying-intent signals (jobs, news, funding, tech, leadership changes) for a result set of companies and write them back into the local cache.

  • Works in 7 steps: Ask which result set → Validate result set exists → Ask which signal types → …
  • The user says enrich these companies with signals
  • SKILL.md covers When This Skill Applies, Workflow and Notes
  • Calls npx

What it does

Enrich With Signals is an agent skill from Othmane-Khadri/YALC-the-GTM-operating-system. Pull PredictLeads buying-intent signals (jobs, news, funding, tech, leadership changes) for a result set of companies and write them back into the local cache. Use when the user says 'enrich these companies with signals', 'add buying signals to this list', 'pull intent data for [domain]', 'check signals for these accounts', or 'fetch jobs and news for these companies'. Side-effecting — calls PredictLeads API and writes to local SQLite + JSON cache.

Its SKILL.md is about 560 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/example-output.md`).

It sits in Databases. It works with SQLite. The repository describes itself as: YALC 1.0, the open-source Clay alternative. MIT, CLI-first, self-hosted, runs in Claude Code. Yalc today is an intelligent orchestration layer that runs pre configured GTM agents…. The licence is MIT.

When your agent uses it

  • The user says enrich these companies with signals
  • Add buying signals to this list
  • Pull intent data for [domain]
  • Check signals for these accounts

Example prompts

  • “enrich these companies with signals”
  • “add buying signals to this list”
  • “pull intent data for [domain]”
  • “/enrich-with-signals”

Requirements

  • Node.js

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Ask which result set
  2. Validate result set exists
  3. Ask which signal types
  4. Shell out
  5. Parse output
  6. Render
  7. Offer follow-ups

What it can do on your machine

Read from SKILL.md and the folder at commit 5686d1f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Enrich With Signals loads about 560 tokens when it runs, and up to ~804 if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 219 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~560
With references · SKILL.md plus every file in references/, read only if the agent opens them
~804

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:40
    cd ~/Desktop/gtm-os && set -a && source .env.local && set +a && \

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Othmane-Khadri/YALC-the-GTM-operating-system at commit 5686d1f, republished under its MIT licence (© Othmane-Khadri). 219 words, ~560 tokens.

Download SKILL.mdSave it as .claude/skills/enrich-with-signals/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
enrich-with-signals
description
Pull PredictLeads buying-intent signals (jobs, news, funding, tech, leadership changes) for a result set of companies and write them back into the local cache. Use when the user says 'enrich these companies with signals', 'add buying signals to this list', 'pull intent data for [domain]', 'check signals for these accounts', or 'fetch jobs and news for these companies'. Side-effecting — calls PredictLeads API and writes to local SQLite + JSON cache.
version
1.0.0

Enrich With Signals

I'll wrap signals:enrich. Take a result set, fan out PredictLeads calls (cached 7 days per domain), and surface signal counts + summary per company.

When This Skill Applies

  • "enrich these companies with signals"
  • "add buying signals to this list"
  • "pull intent data for [domain]"
  • "check signals for these accounts"
  • "fetch jobs and news for these companies"

NOT this skill (use find-lookalikes instead):

  • "find similar companies" — that discovers new prospects.

NOT this skill (use qualify-leads --enrich-signals instead):

  • "qualify these leads with signals" — that's the qualification pipeline with signal enrichment as a gate.

Workflow

Step 0 — Ask which result set

"Which result set should I enrich? Pass the id, or use the most recent."

Step 1 — Validate result set exists
Step 2 — Ask which signal types

"Which signal types? (default: jobs, funding, tech, news; also available: leadership)"

Step 3 — Shell out
bash
cd ~/Desktop/gtm-os && set -a && source .env.local && set +a && \
  npx tsx src/cli/index.ts signals:enrich --result-set <id> --types <types>

Side-effecting → shell-out per benchmark.

Step 4 — Parse output

CLI emits per-company signal counts + cache hit ratio + total credits consumed.

Step 5 — Render

See references/example-output.md.

Step 6 — Offer follow-ups

"Want me to (a) qualify the enriched set via qualify-leads, (b) launch a campaign segmented by signal type?"

Notes

  • ~1 PredictLeads credit per uncached domain per signal type.
  • 7-day cache TTL; pass --no-cache to force re-fetch.
  • Companies with no signals get an empty entry (still cached so we don't re-query).

© Othmane-Khadri, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in .claude/skills/enrich-with-signals of Othmane-Khadri/YALC-the-GTM-operating-system.

  • SKILL.md
  • references/example-output.md

Open the folder on GitHubat commit 5686d1f

Compare with similar skills

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Reactive Sqlite UIfastrepl/anarlog9.5k—~699Automated safety check: PassMIT
Composer Forensicsdxos/dxos526—~3.1kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Enrich With Signals

What does Enrich With Signals do?

Pull PredictLeads buying-intent signals (jobs, news, funding, tech, leadership changes) for a result set of companies and write them back into the local cache. Enrich With Signals is an agent skill from Othmane-Khadri/YALC-the-GTM-operating-system. Pull PredictLeads buying-intent signals (jobs, news, funding, tech, leadership changes) for a result set of companies and write them back into the local cache.

When should I use Enrich With Signals?

Enrich With Signals fits situations like: the user says enrich these companies with signals; add buying signals to this list; pull intent data for [domain]; check signals for these accounts.

How do I install Enrich With Signals in Claude Code?

Run `npx skills add Othmane-Khadri/YALC-the-GTM-operating-system --skill enrich-with-signals -a claude-code`. Or copy the skill folder (.claude/skills/enrich-with-signals in Othmane-Khadri/YALC-the-GTM-operating-system) into .claude/skills/enrich-with-signals in your project. Claude Code loads it when a task matches its description.

How do I install Enrich With Signals in Codex?

Run `npx skills add Othmane-Khadri/YALC-the-GTM-operating-system --skill enrich-with-signals -a codex`. Or copy the skill folder (.claude/skills/enrich-with-signals in Othmane-Khadri/YALC-the-GTM-operating-system) into .agents/skills/enrich-with-signals in your project. Codex loads it when a task matches its description.

Can I use Enrich With Signals in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Othmane-Khadri/YALC-the-GTM-operating-system --skill enrich-with-signals -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/enrich-with-signals, .gemini/skills/enrich-with-signals, .github/skills/enrich-with-signals and .opencode/skills/enrich-with-signals in your project.

What does Enrich With Signals need to run?

Going by SKILL.md and its folder, Enrich With Signals needs the command-line tools its instructions call (npx). Our summary lists: Node.js.

Does Enrich With Signals access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Enrich With Signals safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Enrich With Signals use?

Enrich With Signals is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Enrich With Signals use?

About 560 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 244 tokens, read only when the agent opens those files.

What are the alternatives to Enrich With Signals?

Skills that share tags, products or a category with Enrich With Signals: Iptvnator Sqlite DB Worker (4gray/iptvnator, 7.3k stars), Restore Legacy Sessions (zai-org/ZCode, 7.7k stars), Analyze Nsys Profile (mlc-ai/pith-train, 355 stars) and Reactive Sqlite UI (fastrepl/anarlog, 9.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Enrich With Signals?

Othmane-Khadri (a GitHub user) maintains it in Othmane-Khadri/YALC-the-GTM-operating-system, which has 318 GitHub stars. The repository holds 57 skills in this directory. The repository was last updated on August 20, 2026.

Source: Othmane-Khadri/YALC-the-GTM-operating-system on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.