AccessiWeather: Skill for Codex

.codex/skills/autoresearch/SKILL.md

autoresearch is a skill for Codex from Orinks/AccessiWeather. It costs 17 tokens per session (595 once invoked), scanned A, original, MIT.

A stateful research loop that keeps working until explicit validation evidence is saved. It supports either a validation script or a prompt-and-architect review.

In plain words
What is it for?
Use it for repeated research or improvement tasks that need a stored completion record and a clearly chosen way to validate the result.
Why use it?
It prevents a task from being treated as finished merely because the agent says it is done or stops making progress.

Skill for Codex

Written for Codex: installed under .codex/. Also seen: $skill-name invocation.

This is Orinks/AccessiWeather's own configuration. It tells Codex how to work on AccessiWeather 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 AccessiWeather configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Orinks/AccessiWeather. 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/Orinks/AccessiWeather/main/.codex/skills/autoresearch/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Orinks/AccessiWeather

Made for: 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 autoresearch

README.md
[![agentmods](https://agentmods.dev/badge/skills/orinks/accessiweather/autoresearch.svg)](https://agentmods.dev/skills/orinks/accessiweather/autoresearch)
Your own site
<a href="https://agentmods.dev/skills/orinks/accessiweather/autoresearch"><img src="https://agentmods.dev/badge/skills/orinks/accessiweather/autoresearch.svg" alt="Measured on agentmods" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 595 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00017 $0.00595
Opus 5 $0.00009 $0.00298
Sonnet 5 $0.00003 $0.00119
Haiku 4.5 $0.00002 $0.00060

Measured 8d ago against content hash a6036b5efa10, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

autoresearch 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 8d 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.

.codex/skills/autoresearch/SKILL.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Autoresearch

Autoresearch is the skill-first replacement for the deprecated omx autoresearch command. It keeps the useful measured-research loop, but it now runs as a native-hook stateful workflow instead of a direct CLI or tmux launch surface.

Use when

  • You want a Ralph-ish persistent research loop
  • The task should keep nudging until explicit validation evidence exists
  • You want init-time choice between script validation and prompt+architect validation

Do not use when

  • You want the old omx autoresearch command surface (hard-deprecated)
  • You want detached tmux or split-pane launch parity
  • You have not decided the validation regime yet

Core contract

  1. Init chooses validation mode. Pick exactly one:
    • mission-validator-script
    • prompt-architect-artifact
  2. Persist mode state in .omx/state/.../autoresearch-state.json including:
    • validation_mode
    • completion_artifact_path
    • mission_validator_command or validator_prompt
    • optional output_artifact_path
  3. Completion is artifact-gated. The loop does not stop because the model says “done”, because a stop hook fired once, or because several turns were no-ops.
  4. Direct CLI launch is gone. Use $deep-interview --autoresearch for intake and $autoresearch for execution.

Completion artifact contract

mission-validator-script

The completion artifact must exist and record a passing validator result, for example:

{
  "status": "passed",
  "passed": true,
  "summary": "metric improved beyond baseline"
}

prompt-architect-artifact

The completion artifact must include both an architect approval verdict and an output artifact path, for example:

{
  "validator_prompt": "Review the research output against the mission.",
  "architect_review": { "verdict": "approved" },
  "output_artifact_path": ".omx/specs/autoresearch-demo/report.md"
}
  1. Run $deep-interview --autoresearch to clarify mission + evaluator.
  2. Materialize .omx/specs/autoresearch-{slug}/mission.md, sandbox.md, and result.json.
  3. Start $autoresearch with the chosen validation mode stored in mode state.
  4. Let stop-hook / auto-nudge continue until the completion artifact satisfies the chosen validation mode.
  5. Finish only after the validator artifact is complete.

Read the full file on GitHub · 69 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. 8d ago First seen · 69 lines · 17 tokens per session scan A a6036b5efa10

Subscribe to this mod's changes

autoresearch is a skill published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 595 once invoked, about $0.0001 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.

Related

Other skills, from other repositories

systematic-debugging

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

obra/superpowers · 21 tokens

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

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

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens