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 deepline-scoringgit 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/deepline-scoring)<a href="https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-scoring"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-scoring/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/deepline-scoring"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-scoring.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 | $0.00057 | $0.01847 |
| Opus 5.5 | $0.00023 | $0.00739 |
| Sonnet 5.5 | $0.00011 | $0.00369 |
| Haiku 4.5 | $0.00006 | $0.00185 |
Grade A, and why
deepline-scoring 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 today.
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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deepline Scoring
Quick Start
npm install -g deepline
deepline -h
Find evidence for the customer's decision. Use approved rules or a separately evaluated model for scoring. Phrase matches and prevalence ratios alone cannot supply scoring weights.
| Task | Read |
|---|---|
| Find phrases and buyer language | Keyword catalog, buyer-language research |
| Build or audit a score | Scoring delivery |
| Test rules or evaluate outcomes | Testing and evaluation |
| Create an artifact-backed scorecard | Scorecard creation pattern |
| Debug disputed features or routing | Scoring diagnostics |
| Verify technology | Technology evidence |
| Estimate staffing or demand | Capacity evidence |
Read deepline-gtm before collection and deepline-plays before authoring. Verify the workspace, current provider schema and price. Pilot one or two rows and pass the quality gate on the generated outputs before scaling within the approved budget. Keep exports and receipts in a persistent project directory. Reuse collected evidence when rescoring.
What ships with it
28 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.
- .gitignore 19 B
- package.json 114 B
- references/buyer-language-research.md 8.6 KB
- references/capacity-evidence.md 4.4 KB
- references/dedupe.md 1.5 KB
- references/keyword-catalog.md 4.7 KB
- references/pitfalls.md 1.6 KB
- references/proven-signals.md 1.1 KB
- references/quality-gate.md 2.5 KB
- references/report-template.md 5.8 KB
- references/scorecard-creation.md 3.3 KB
- references/scoring-delivery.md 7.4 KB
- references/scoring-diagnostics.md 7.7 KB
- references/scoring-pitfalls.md 2.8 KB
- references/signal-interpretation.md 2.3 KB
- references/step-7-prospects.md 1.6 KB
- references/technology-evidence.md 9.4 KB
- references/testing-and-evaluation.md 6.8 KB
- scripts/analyze_signals_v2.py 26 KB runs code
- scripts/analyze_signals.py 17 KB runs code
- scripts/dedupe_utils_v2.py 16 KB runs code
- scripts/dedupe_utils.py 16 KB runs code
- scripts/find_contacts_v2.py 1.9 KB runs code
- scripts/find_contacts.py 24 KB runs code
- scripts/replay-score.play.ts 2.4 KB runs code
- scripts/scoring_contract.ts 7.5 KB runs code
- scripts/technology_evidence.py 3.8 KB runs code
- skill-metadata.json 3.4 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.
- today Changed · -1 lines edae9477dfd0
- yesterday Changed 017c20c75d8b
- 10d ago Changed 6fa9cf63c1c2
- 14d ago Changed · -3 lines 915b968df66f
- 26d ago First seen · 81 lines · 57 tokens per session scan A 28db38411b7e
deepline-scoring is a skill published in the GitHub repository getaero-io/gtm-eng-skills (68 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,847 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-21.
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