cognia-next: Skill for Codex

.agents/skills/map-requirement-flow/SKILL.md

map-requirement-flow is a skill for Codex from MaxQian888/cognia-next. It costs 169 tokens per session (2,324 once invoked), scanned A, original, AGPL-3.0.

A method for mapping a request into a complete step-by-step requirement flow. It traces who acts, what triggers the process, what must already be true, which paths and exceptions can occur, and how success can be observed.

In plain words
What is it for?
Use it to analyze product features, workflows, and processes across pages, agents, tools, and backend systems.
Why use it?
It exposes missing requirements and unclear ownership before implementation begins. This helps teams handle normal cases, branches, failures, and dependencies consistently.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents).

This is MaxQian888/cognia-next's own configuration. It tells Codex how to work on cognia-next 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 cognia-next configures →

Reuse

Borrowing it

Nothing to install: this file belongs to MaxQian888/cognia-next. 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/MaxQian888/cognia-next/dev/.agents/skills/map-requirement-flow/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/MaxQian888/cognia-next

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 map-requirement-flow

README.md
[![agentmods](https://agentmods.dev/badge/skills/maxqian888/cognia-next/map-requirement-flow/github.svg)](https://agentmods.dev/skills/maxqian888/cognia-next/map-requirement-flow)
Your own site
<a href="https://agentmods.dev/skills/maxqian888/cognia-next/map-requirement-flow"><img src="https://agentmods.dev/badge/skills/maxqian888/cognia-next/map-requirement-flow/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 map-requirement-flow

Your own site · 80×15
<a href="https://agentmods.dev/skills/maxqian888/cognia-next/map-requirement-flow"><img src="https://agentmods.dev/badge/skills/maxqian888/cognia-next/map-requirement-flow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 169 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,324 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 unknown 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 $0.00169 $0.02324
Opus 5 $0.00084 $0.01162
Sonnet 5 $0.00034 $0.00465
Haiku 4.5 $0.00017 $0.00232

Measured 10d ago against content hash 9df0252ecda3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

map-requirement-flow 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 10d 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.

.agents/skills/map-requirement-flow/SKILL.md · 224 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Files

What ships with it

4 files 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.

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. 10d ago First seen · 224 lines · 169 tokens per session scan A 9df0252ecda3

Subscribe to this mod's changes

map-requirement-flow is a skill published in the GitHub repository MaxQian888/cognia-next (53 stars, last pushed 2d ago), licensed AGPL-3.0. It adds 169 tokens to every session and 2,324 once invoked, about $0.0008 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

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

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