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 Frontal-so/outbound-skills --skill multi-signalgit clone --depth 1 https://github.com/Frontal-so/outbound-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/frontal-so/outbound-skills/multi-signal)<a href="https://agentmods.dev/skills/frontal-so/outbound-skills/multi-signal"><img src="https://agentmods.dev/badge/skills/frontal-so/outbound-skills/multi-signal/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/frontal-so/outbound-skills/multi-signal"><img src="https://agentmods.dev/badge/skills/frontal-so/outbound-skills/multi-signal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00079 | $0.01713 |
| Opus 5 | $0.00039 | $0.00856 |
| Sonnet 5 | $0.00016 | $0.00343 |
| Haiku 4.5 | $0.00008 | $0.00171 |
Grade A, and why
multi-signal 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Signal Stacking and Scoring
Multi-signal stacking is the highest-performing outbound strategy: 3+ signals = 35-40% reply rate vs 6-8% cold. This sub-skill covers the scoring framework, recency multipliers, action thresholds, response SLAs, and compound scoring logic.
Reference Files
- Read
{SKILL_BASE}/resources/signal-scoring.mdfor the complete scoring framework (weights, recency, thresholds, SLAs, plays) - Read
{SKILL_BASE}/resources/examples/signal-campaigns/gtm-plays.mdfor 11 executable GTM plays and multi-channel coordination - Read
{SKILL_BASE}/resources/signal-detection-tools.mdfor 30-trigger quick reference with detection tools, timing windows, Clay credit costs, signal freshness rules (when signals expire), reliability tiers, and signal sources by data party (1st/2nd/3rd)
Performance Benchmarks
| Approach | Reply Rate | Contract Value |
|---|---|---|
| Cold outreach (no signal) | 6-8% | Baseline |
| Single signal-based | 18-22% | 2-3x baseline |
| Multi-signal stacked (3+) | 35-40% | 3-4x baseline |
| Signal + ABM multi-touch | 36% meeting rate | Highest |
Signal Scoring Framework
Tier 1 - Hot Signals (50-100 points)
| Signal | Points |
|---|---|
| Demo/pricing request | 100 |
| 3+ pricing page visits in 7 days | 80 |
| Champion job change to target account | 75 |
| Multiple stakeholders from same account | 70 |
| Product trial signup | 65 |
| G2 comparison with competitors | 60 |
| 5+ website visits in 2 weeks | 50 |
Tier 2 - Warm Signals (20-49 points)
| Signal | Points |
|---|---|
| Series A/B/C funding | 45 |
| Relevant job posting | 40 |
| Bombora topic surge (score 70+) | 40 |
| Case study download | 35 |
| LinkedIn engagement with your content | 30 |
| Webinar attendance | 25 |
| 3+ blog post visits | 20 |
Tier 3 - Cool Signals (5-19 points)
| Signal | Points |
|---|---|
| Company news (expansion) | 15 |
| Single website visit | 10 |
| Industry report download | 10 |
| Email open (no click) | 5 |
| Social follow (no engagement) | 5 |
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 · 129 lines · 79 tokens per session scan A 667ff5c86c1a
multi-signal is a skill published in the GitHub repository Frontal-so/outbound-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,713 once invoked, about $0.0004 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 2026-08-31.
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