Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/YALC-the-GTM-operating-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Othmane-Khadri/YALC-the-GTM-operating-system/main/.claude/skills/enrich-with-signals/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/YALC-the-GTM-operating-systemWrote 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/othmane-khadri/yalc-the-gtm-operating-system/enrich-with-signals)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/enrich-with-signals"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/enrich-with-signals/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/othmane-khadri/yalc-the-gtm-operating-system/enrich-with-signals"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/enrich-with-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 40 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 41 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00105 | $0.00554 |
| Opus 5 | $0.00053 | $0.00277 |
| Sonnet 5 | $0.00021 | $0.00111 |
| Haiku 4.5 | $0.00011 | $0.00055 |
Grade A, and why
enrich-with-signals 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.
What it actually says
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
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-cacheto force re-fetch. - Companies with no signals get an empty entry (still cached so we don't re-query).
What ships with it
1 file 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.
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 · 63 lines · 105 tokens per session scan A b07189aaf2d8
enrich-with-signals is a skill published in the GitHub repository Othmane-Khadri/YALC-the-GTM-operating-system (301 stars, last pushed 22d ago), licensed MIT. It adds 105 tokens to every session and 554 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 2026-08-30.
Other skills, from other repositories
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
kn-spec
Use when creating a specification document for a feature (SDD workflow).
kn-handoff
Use when a feature crosses repository boundaries and one side must hand work to the other - generates a self-contained frontend-to-backend brief or backend-to-frontend API contract.
kn-flow
Use when orchestrating a full Knowns spec or task wave through planning, implementation, review, integration, and verification, optionally using sub-agents when scopes are parallel-safe.
kn-research
Use when you need to understand existing code, find patterns, search project knowledge, investigate current external facts, or explore a large codebase before implementation.
kn-debug
Use when debugging errors, test failures, build issues, or blocked tasks — structured triage to fix to learn.