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 agentmods add rules/sordi-ai/skill-everything/dockergit clone --depth 1 https://github.com/sordi-ai/skill-everythingWhat 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 | $0.00033 | $0.00975 |
| Opus 5 | $0.00016 | $0.00487 |
| Sonnet 5 | $0.00007 | $0.00195 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
docker 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 2d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sub-Skill: Docker / Container Conventions
Purpose: Prevent common container mistakes — busted layer caches, bloated images, insecure defaults, and compose misconfigurations — before they reach CI or production.
Rules
Layer Ordering & Cache
- Dependency layer first. Always install dependencies in a separate layer before copying application source, so a source change does not invalidate the package cache. Reference: ERR-2026-020
- Copy only what's needed early. Before copying the full source tree, copy only the dependency manifest files (e.g.,
requirements.txt,package.json,pyproject.toml) so the install layer is cached independently. - Minimise layer count. Prefer chaining related
RUNcommands with&&and\continuations rather than issuing oneRUNper command; eachRUNcreates a new layer. - Order by change frequency. Always place instructions that change rarely (OS packages, global tools) before instructions that change often (app source, config files).
Multi-Stage Builds
- Use multi-stage for compiled artefacts. Always use a builder stage to compile or bundle, then copy only the final artefact into a minimal runtime stage; never ship build toolchains in the production image.
- Name every stage. Use
AS <name>on everyFROMline so later stages anddocker build --targetcalls are readable and stable. - Pin the runtime base image. Always pin base images to a specific digest or immutable tag (e.g.,
python:3.12.3-slim) in the runtime stage; never uselatestin production Dockerfiles.
Security
- Run as non-root. Always create a dedicated non-root user and switch to it with
USERbefore the finalCMD/ENTRYPOINT; never run application processes asrootinside a container. - Never embed secrets in image layers. Never pass secrets via
ARGorENVin a Dockerfile; use Docker BuildKit--secretmounts or runtime environment injection instead. - Scan images before push. Before pushing any image to a registry, run an image vulnerability scanner (e.g.,
trivy image,docker scout) and fail the pipeline on critical CVEs. - Prefix docker run -v from Git Bash with MSYS_NO_PATHCONV=1. Always prefix
docker run -vcalls issued from Git Bash or MSYS on Windows withMSYS_NO_PATHCONV=1to prevent path mangling. Reference: ERR-2026-013
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.
- 2d ago First seen · 62 lines · 33 tokens per session scan A 76d6bf20fab6
docker is a cursor rule published in the GitHub repository sordi-ai/skill-everything (19 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 975 once invoked, about $0.0002 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 cursor rules, from other repositories
00-stack
Stack, runtimes, and MCP servers for this repository.
20-testing
How to run tests and MCP smoke checks.
30-security
Secrets, gitleaks, age encryption, telemetry redaction.
obsidian-memory
Markdown vault memory protocol (vkm-kit).
10-style
Style conventions and contribution hygiene.
retention-predictor
Skill — retention-predictor. Predicts retention potential via usage frequency, habit formation mechanics, and churn risk factors.