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/predictleads-dashboard/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/predictleads-dashboard)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/predictleads-dashboard"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/predictleads-dashboard/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/predictleads-dashboard"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/predictleads-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Rogue Agent · line 38 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00069 | $0.01008 |
| Opus 5 | $0.00034 | $0.00504 |
| Sonnet 5 | $0.00014 | $0.00202 |
| Haiku 4.5 | $0.00007 | $0.00101 |
Grade A, and why
predictleads-dashboard 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PredictLeads Dashboard (HTML viz)
Generates a single self-contained HTML page from cached signals in ~/.gtm-os/gtm-os.db. Cards per company with signal-count badges, top-signal callout, expandable detail (recent jobs, news, funding, tech stack, similar companies). Filter by vertical, sort by signal density or recency. Auto dark/light. Zero API calls.
When to use
- After running
prospect-discovery-pipelineto scan all 10 finalists in one view - After bulk-enriching a campaign result set (
signals:enrich --result-set) for a visual sanity check before outreach - Sharing signal context with a non-technical teammate (open the HTML, no CLI knowledge needed)
Don't use when: you only have signals for 1–2 companies (just use signals:show); signals haven't been pulled yet (run signals:fetch first).
How to invoke
The dashboard is built by a small Python script. Pass a list of domains and an optional list of pre-built lead cards (name + title + LinkedIn URL).
Inputs the skill needs
- List of domains (must already be in
company_signalstable) - Optional per-domain lead metadata:
{ company, vertical, geo, lead_name, lead_title, linkedin }
Build steps
- Read the lead metadata into a Python dict (see existing template at
~/Desktop/predictleads-dashboard.htmlfor shape). - Query SQLite for each domain:
SELECT signal_type, COUNT(*)for badge counts- Top 8 jobs by
event_date DESC - Top 8 news by
event_date DESC - Top 5 financing events
- Top 12 technologies
- Top 10 similar_companies sorted by
payload.score
- Render the HTML template (see
Implementationbelow) with embedded JSON. - Write to
~/Desktop/predictleads-dashboard-{client_or_topic}-{date}.htmland open it.
Implementation
A Python generator script lives at scripts/predictleads-dashboard.py (when committed). It reads from ~/.gtm-os/gtm-os.db, accepts a JSON config of leads, and emits a self-contained HTML file.
If the script is missing, model the new one on the prior run captured at ~/Desktop/predictleads-dashboard.html (Apr 30 2026). Key visual elements to keep:
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 · 74 lines · 69 tokens per session scan A b983393d57f7
predictleads-dashboard 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 69 tokens to every session and 1,008 once invoked, about $0.0003 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-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-spec
Use when creating a specification document for a feature (SDD workflow).
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-debug
Use when debugging errors, test failures, build issues, or blocked tasks — structured triage to fix to learn.
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.