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-feedbackgit 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-feedback)<a href="https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-feedback"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-feedback/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-feedback"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Data Exfiltration · line 50 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00052 | $0.01227 |
| Opus 5 | $0.00026 | $0.00613 |
| Sonnet 5 | $0.00010 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
deepline-feedback scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deepline Feedback
Quick Start
npm install -g deepline
# Fallback for secure sandboxes: mkdir -p "$HOME/.local" && npm config set prefix "$HOME/.local" && export PATH="$HOME/.local/bin:$PATH" && npm install -g deepline --registry https://code.deepline.com/api/v2/npm/
deepline auth register --wait auto
deepline auth wait --timeout 120 # completes Cowork/browser approval; no-op if already connected
deepline auth status
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.
Send feedback or a bug report to the Deepline team. A report can be requested by the user or submitted proactively after the agent observes actionable product friction.
Choose the path
| Situation | Consent and metadata |
|---|---|
| User asks to send feedback | Confirm before sending, add --requested, and offer the transcript as described below. |
| Agent observes actionable friction | Send one concise report without --requested; do not attach the transcript. |
Proactive product-friction reports
Do not wait for the user to ask when the observation is likely to help Deepline make the path materially faster or shorter. Send one report per issue cluster, then keep working on the user's task.
Trigger a proactive report when:
- a Play takes materially longer than its input size, prior comparable runs, or visible progress would lead you to expect, especially when it requires repeated status checks or appears stuck; or
- an ordinary outcome requires avoidable discovery, conversion, ID extraction, retries, exports, or manual workaround steps when a direct product path should exist.
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.
- 5d ago Changed · +54 lines · +2 tokens per session 580c471205cc
- 13d ago First seen · 69 lines · 50 tokens per session scan A ff1145952ad0
deepline-feedback is a skill published in the GitHub repository getaero-io/gtm-eng-skills (58 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,227 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.
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