Archon is a workflow engine for AI coding agents that turns development processes into YAML-defined sequences with phases, validation gates, and artifacts. Developers use it to run repeatable processes such as planning, implementation, testing, code review, and pull-request creation across projects. The catalogue entries provide commands, agents, skills, hooks, instructions, and settings for working with Archon.
Borrowing it
Nothing to install: this file belongs to coleam00/Archon. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/coleam00/Archon/dev/.claude/skills/docker-extend/SKILL.mdgit clone --depth 1 https://github.com/coleam00/ArchonWrote 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/coleam00/archon/docker-extend)<a href="https://agentmods.dev/skills/coleam00/archon/docker-extend"><img src="https://agentmods.dev/badge/skills/coleam00/archon/docker-extend/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/coleam00/archon/docker-extend"><img src="https://agentmods.dev/badge/skills/coleam00/archon/docker-extend.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 3 findings, up to low
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 →
- low Tool Misuse · line 82 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- low Tool Misuse · line 82 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- low Tool Misuse · line 82 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
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 | $0.00118 | $0.01066 |
| Opus 5.5 | $0.00047 | $0.00426 |
| Sonnet 5.5 | $0.00024 | $0.00213 |
| Haiku 4.5 | $0.00012 | $0.00107 |
Grade C, and why
docker-extend scanned grade C with 1 finding 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 1mo 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
&& rm -rf /var/lib/apt/lists/* How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docker Extend
Sets up personal Docker tool customization using the project's gitignored override pattern.
Your Dockerfile.user and docker-compose.override.yml are yours — never committed to git.
Step 1: Detect Context
Determine if the user is in dev (source build) or deploy (GHCR) mode:
!if [ -f docker-compose.yml ] && grep -q 'build: \.' docker-compose.yml 2>/dev/null; then echo "DEV_MODE: Building from source (root docker-compose.yml)"; elif [ -f deploy/docker-compose.yml ]; then echo "DEPLOY_MODE: Using GHCR image (deploy/docker-compose.yml)"; else echo "UNKNOWN_MODE: No docker-compose.yml found in root or deploy/"; fi
Decision:
DEV_MODE→ work withDockerfile.user+docker-compose.override.yml(repo root)DEPLOY_MODE→ work withdeploy/Dockerfile.user+deploy/docker-compose.override.ymlUNKNOWN_MODE→ ask the user which directory theirdocker-compose.ymllives in before proceeding
Step 2: Check Current State
DEV_MODE — check root files:
!echo "=== Dockerfile.user ===" && (cat Dockerfile.user 2>/dev/null || echo "(not found — will create from example)") && echo "" && echo "=== docker-compose.override.yml ===" && (cat docker-compose.override.yml 2>/dev/null || echo "(not found — will create from example)")
DEPLOY_MODE — check deploy/ files:
!echo "=== deploy/Dockerfile.user ===" && (cat deploy/Dockerfile.user 2>/dev/null || echo "(not found — will create from example)") && echo "" && echo "=== deploy/docker-compose.override.yml ===" && (cat deploy/docker-compose.override.yml 2>/dev/null || echo "(not found — will create from example)")
If both files already exist: Skip to Step 4 (add tools). If files are missing: Proceed to Step 3 (copy from examples). If example files are missing too: Tell the user the project hasn't added the example files yet and to check with the maintainers or the docs.
Step 3: Copy Example Files
Run only the copy commands for whichever files are missing. Do NOT overwrite existing files.
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.
- 1mo ago First seen · 108 lines · 118 tokens per session scan C a3836506c0d4
docker-extend is a skill published in the GitHub repository coleam00/Archon (23,752 stars, last pushed today), licensed MIT. It adds 118 tokens to every session and 1,066 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-02.
Other skills, from other repositories
system-prompts
Write system prompts, tool docs, and agent definitions. Project tag conventions + RFC 2119 keywords + dense compression. Use when authoring or editing any prompt the model reads.
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…
semantic-compression
Re-encode verbose prose into a dense telegraphic register — punctuation as connectives, label frames, verbless assertions — without losing normativity or precision. Use when compressing system prompts, tool/function descriptions, skill bodies, or agent instructions; reducing token count or context bloat; making…
hatch3r-cli-toolbox
Category-indexed reference for 34 specialist CLI tools beyond the always-on five (ripgrep, jq, gh, fd, fzf). Use to pick the right tool for HTTP clients, ai-chat, structural-search, sed-style edits, data ops, browser automation, container ops, and more.
hatch3r-containerize
Containerizes an application through a repeatable workflow — multi-stage Dockerfile, non-root hardening, minimal base image, .dockerignore, healthcheck, local docker-compose, Kubernetes manifest basics, and an image-scan gate. Use when adding or hardening container artifacts.
jfrog-devops
Manage JFrog Artifactory repositories, artifacts, Docker registry, build info, ML model registry (JFrog ML / AI Catalog), and Xray security scanning for DevOps and MLOps workflows. Use when user asks about JFrog, Artifactory, Xray, Curation, Frogbot, JFrog ML, AI Catalog, artifact management, "deploy artifact", Docker…