yolo-jail: Skill for Claude Code

.pi/skills/context-first-design/SKILL.md

context-first-design is a skill for Claude Code, Codex from mschulkind-oss/yolo-jail. It costs 33 tokens per session (2,667 once invoked), scanned A, original, Apache-2.0.

A read-only workflow for giving design advice from a small set of cited evidence. It keeps the owner responsible for the question, scope, and final design decision.

In plain words
What is it for?
Use it to frame a design question, collect or reuse a dated evidence packet, compare options, and record an evidence-based opinion with clear limits.
Why use it?
It prevents an advisory opinion from being mistaken for an approved design or an instruction to change code, settings, or access. It also avoids broad repository exploration when the needed evidence is already defined.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

This is mschulkind-oss/yolo-jail's own configuration. It tells Claude Code and Codex how to work on yolo-jail itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything yolo-jail configures →

Reuse

Borrowing it

Nothing to install: this file belongs to mschulkind-oss/yolo-jail. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/mschulkind-oss/yolo-jail/main/.pi/skills/context-first-design/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/mschulkind-oss/yolo-jail

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for context-first-design

README.md
[![agentmods](https://agentmods.dev/badge/skills/mschulkind-oss/yolo-jail/context-first-design/github.svg)](https://agentmods.dev/skills/mschulkind-oss/yolo-jail/context-first-design)
Your own site
<a href="https://agentmods.dev/skills/mschulkind-oss/yolo-jail/context-first-design"><img src="https://agentmods.dev/badge/skills/mschulkind-oss/yolo-jail/context-first-design/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.

agentmods 80×15 button for context-first-design

Your own site · 80×15
<a href="https://agentmods.dev/skills/mschulkind-oss/yolo-jail/context-first-design"><img src="https://agentmods.dev/badge/skills/mschulkind-oss/yolo-jail/context-first-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,667 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.02667
Opus 5.5 $0.00013 $0.01067
Sonnet 5.5 $0.00007 $0.00533
Haiku 4.5 $0.00003 $0.00267

Measured yesterday against content hash e73ff71e5439, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

context-first-design 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 yesterday.

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.

.pi/skills/context-first-design/SKILL.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context-first design

Use this workflow only for an owner-requested advisory design opinion. The owner keeps the question, scope, model choice, and any design ruling. The result is advice—not an owner decision, accepted design, implementation plan, or permission to change source, schema, agent configuration, global settings, credentials, or tool access.

Procedure

  1. Fix the question and boundary. Use the owner's exact question and constraints. If either is unclear, ask before researching. Do not convert an opinion request into a design ruling or implementation task.
  2. Build or reuse a small evidence packet. Prefer a minimal-context collector profile, with unrelated project/global/skill inheritance disabled where supported; do not edit agent configuration, add tools, or widen permissions to obtain one. Start from named source seams, group targeted reads, and reuse verified evidence. A dated, cited packet may be reused when its source revision and relevant seams are still valid; collection-profile optimization is not a prerequisite. Give a collector a deliberately small soft exploration budget—not a hard tool/evaluation cap—and stop once the packet is grounded; leave narrow gaps explicit. Cite exact repository-relative paths and stable symbols, headings, or line ranges, and identify the source revision. Do not paste whole files or directories unless the owner explicitly asks for review of a named whole document; then include that full document rather than excerpts. Separate observed facts from owner constraints, interpretations, and unknowns. Present options neutrally; do not smuggle in a recommendation. Keep usage-accounting collection in its own task/artifact; never append its accounting notes or complete collector report to the designer packet.
  3. Close only narrow fact gaps. If a required fact is absent, ask a configured low-cost scout one question tied to the packet's named source seam. Request a concise answer, exact citation, and what would falsify it—not a file, directory survey, or broad exploration. Keep the initial designer opinions blind to each other. For a bounded helper dispatch, use async: false, set maxOutput: { bytes: N } with N <= 6000, and set output: false; do not nest helpers or ask them to publish/write anything. maxOutput limits returned text only; it is not a tool-call, token, or evaluation budget. If the fact remains unknown, leave it explicitly unknown; never fill it by inference.
  4. Freeze the packet. Before asking for a comparison, save one compact, design-only packet containing the question, cited evidence, current data flow or contract, owner constraints, neutral options, and unknowns. Inspect the exact text selected for the designer's inline prompt and remove any accounting appendix or unrelated collector output before freezing it. Preserve the exact packet and version. Do not silently change it between model opinions. If a new fact is needed after a model starts, collect it separately, record the continuation and usage, update and freeze a new packet, then give each compared model that same packet and question before treating the opinions as comparable.
  5. Run advisory opinions with a verified boundary. Select the dedicated workspace profile designer-advisory for this workflow; do not use the write-capable document-author profile. That profile pins output: false; keep the launch free of any output-path override and use the native passive agent list or launch preflight to confirm the selected project profile and no output-file path. Its direct tools must be only subagent, subagent_supervisor, and contact_supervisor; systemPromptMode must be replace; project/global context and skill inheritance must be off. A per-call options.tools subset is not proof of restriction: in the audited Astra/Fable pair, the requested subset did not remove capabilities and both effective lists still included write and subagent. Before a paid launch, confirm the profile's tool allowlist, inheritance, fresh context, and absence of an output-file path. At completion, inspect native launch/session evidence and actual effective tool capability audit as well: write, repository read/search, shell, and any unapproved tools must be absent, not merely unused. Retain only explicitly authorized bounded-helper or supervisor tools; their presence does not relax the direct-tool restriction. If the preflight or run audit shows a forbidden tool or cannot establish the boundary, say so plainly and do not claim isolation or a verified pair. Do not treat a prompt, no tool calls, or a per-call subset as proof. Do not widen access to make the run easier, and do not disable writing globally: design-document authors may need write access to publish. Keep the selected task-specific prompt to the frozen packet plus known runtime wrapper material—no accounting appendix, unrelated context, or output-file directive. If a helper is needed for a narrow packet gap, use only an allowed helper and set its synchronous, inline-only, bounded-output parameters as in step 3; do not ask it to edit or publish. If the profile or API cannot enforce the boundary, stop and seek owner direction before launching or retrying. Request advice only, grounded in the packet, with a recommendation, strongest counterargument, trade-offs, assumptions/unknowns, and confidence. No file edits or implementation.
  6. Let the owner assess the first pass when using the default path. Present the opinion, source packet, process observations, and measured usage separately. Wait for the owner to judge process, opinion, and cost before a comparator. This staged review is the default, not a gate when the owner has already explicitly requested a named parallel comparison.
  7. Honor the owner's comparison shape. The default is one opinion, then wait for the owner to assess it and explicitly name any comparator; never launch an expensive comparator by default or on a model's suggestion. If the owner explicitly names multiple models for a comparison, run those named opinions independently in one parallel fanout without a staged permission gate. Initial opinions are blind: give every model the same frozen packet, exact question, requested effort/thinking setting, and output contract; do not expose another model's answer or intermediate result. An explicitly owner-authorized follow-up may instead critique another opinion only when the owner supplies that opinion and asks for critique; mark that task as a follow-up, distinguish the other model's claims from packet/source facts, and keep the critique advisory. Before launch, verify the actual API supports the required fanout and return-only child behavior. Compare the native effective tool lists for all named runs; if any boundary differs or fails, report it and do not call the pair verified. Present opinions side by side, with differing conclusions and uncertainties intact. Run a further comparator only if the owner explicitly asks after assessing the comparison.
  8. Report evidence and ownership. State what is evidenced and unknown, each model's advisory conclusion, requested and resolved effort, and whether the native tool boundary was actually satisfied. The owner—not either model—decides whether to rule or proceed. Do not claim the design is accepted, pilot-tested, or implemented without separate evidence.

Read the full file on GitHub · 72 lines

Changes

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.

  1. yesterday First seen · 72 lines · 33 tokens per session scan A e73ff71e5439

Subscribe to this mod's changes

context-first-design is a skill published in the GitHub repository mschulkind-oss/yolo-jail (3 stars, last pushed today), licensed Apache-2.0. It adds 33 tokens to every session and 2,667 once invoked, about $0.0001 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 2026-10-26.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens