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-plays-reviewgit 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-plays-review)<a href="https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-plays-review"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-plays-review/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-plays-review"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-plays-review.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 Memory Poisoning · line 216 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00121 | $0.02710 |
| Opus 5.5 | $0.00048 | $0.01084 |
| Sonnet 5.5 | $0.00024 | $0.00542 |
| Haiku 4.5 | $0.00012 | $0.00271 |
Grade A, and why
deepline-plays-review 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 6d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review and Improve Deepline Plays
Quick Start
npm install -g deepline
deepline -h
CLI resolution
Run deepline when it is available. If the shell reports that command is missing, use <workspace-root>/.deepline/runtime/bin/deepline (or the npm-created .cmd shim on Windows). If neither exists, follow https://code.deepline.com/INSTALL.md to set up Deepline.
Loop: revision → run → assess → decide → next revision. Google Sheets is the
human review surface; the Play revision, completed run, and durable dataset
remain the execution record.
Route the request
| User intent | Start here |
|---|---|
| Put a run in a Sheet | Export the completed dataset for review |
| Address edits, notes, or comments | Read fresh feedback and run one revision |
| Make feedback a standing rule | Record a general expectation |
| Never regress on a corrected case | Add a case-specific expectation or golden case |
| Compare revisions | Evaluate both against one frozen basis |
| Try several improvements | Establish a bounded agent-driven loop |
| Keep improving together across turns | Resume the loop and yield after each candidate |
Stop at planning boundaries. When the user asks for a plan, classification, or proposed evaluation before any calls or edits, write it from the supplied context and stop. Do not inspect live Plays, runs, files, or tool contracts, even through read-only commands: exploration cannot grant missing authority or define a budget, and it can start paid work. Resume discovery only after the user asks to proceed.
To only build, run, or debug a Play, use deepline-plays; for unrelated
spreadsheet manipulation, use the relevant spreadsheet workflow. This skill
begins when a result will be reviewed, measured, or used to change the Play.
What ships with it
2 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.
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.
- 6d ago Changed f586c70f1c4c
- 11d ago Changed · -2 lines 5d471478075f
- 12d ago Changed b6fa02cd1ef0
- 13d ago Changed · -58 lines f52e2f6849d9
- 15d ago Changed ea1e37b5dece
- 16d ago Changed b944d3c747a2
- 20d ago Changed a8463b913397
- 24d ago Changed · +9 lines 0618e5387fdb
- 26d ago Changed 75e35b3a4aec
- 1mo ago First seen · 309 lines · 121 tokens per session scan A 378d6c6d5a31
deepline-plays-review is a skill published in the GitHub repository getaero-io/gtm-eng-skills (71 stars, last pushed yesterday), licensed MIT. It adds 121 tokens to every session and 2,710 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
gooseworks
GooseWorks growth coworker and router for markets, competitors, audiences, creators, campaigns, ads, photos, graphics, video, content, email, SEO, social, leads and contacts. Use for brand facts or copy too, so offers, prices, rules, audience and bestsellers reach the brand's Marketing Brain.
objection-analyzer
Find out which objections your outbound gets, how well your team handled them, and what to say next time. Use whenever the user wants to analyze the objections in their replies, rank the most frequent ones, know how to handle a specific objection, coach reps on their objection answers, build an objection playbook or…
audience-icp-filter
Filter an existing audience or lead list against your ICP and split it into ready-to-sequence segments. Use when someone already has a list of people — an audience in their sales tool, a CSV or CRM export, event or webinar attendees, registrants, a Sales Navigator import, a newsletter or community export — and wants…
sales-nav-search-builder
Generate a precise LinkedIn Sales Navigator search URL from a natural-language ICP description — for sales prospecting, outbound outreach, lead generation, and B2B targeting. Use whenever the user asks for a Sales Nav URL, a LinkedIn search URL, an outbound prospecting query, a search for a persona or role, wants to…
team-performance-dashboard
Generate your Team Performance Dashboard from your La Growth Machine data: a head-of-sales cockpit that ranks each sender (rep) on reply rate, your success event (signups/meetings/deals) and conversion, surfaces hot leads going cold, and turns your best reps' campaigns into a coaching playbook for the rest. Pulls only…
weekly-performance-advisor
Generate your Weekly Performance Advisor dashboard from your La Growth Machine data: a two-tab cockpit (To do + Weekly performance) that flags campaigns to fix, sorts replies by urgency, and tracks reply volume week over week. It pulls only YOUR live LGM data, scores each running campaign against baked 3-zone…