why

why is a skill for Claude Code, Codex from Sma1lboy/rove. It costs 111 tokens per session (4,897 once invoked), scanned A, a copy of why, MIT.

A research method for explaining why code or a design decision took its current shape by examining records such as commits, design documents, issues, and team discussions.

In plain words
What is it for?
It helps investigate design rationale, regressions, unusual constants, and questions about why a system behaves or was built in a particular way.
Why use it?
It replaces guesswork about design intent with evidence about constraints, rejected alternatives, edge cases, and historical changes.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/sma1lboy/rove/why
Any agent
npx skills add Sma1lboy/rove --skill why
Clone the repo
git clone --depth 1 https://github.com/Sma1lboy/rove

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 why

README.md
[![agentmods](https://agentmods.dev/badge/skills/sma1lboy/rove/why.svg)](https://agentmods.dev/skills/sma1lboy/rove/why)
Your own site
<a href="https://agentmods.dev/skills/sma1lboy/rove/why"><img src="https://agentmods.dev/badge/skills/sma1lboy/rove/why.svg" alt="Measured on agentmods" height="20"></a>
Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,897 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 84% copy Near-identical to another mod 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 $0.00111 $0.04897
Opus 5 $0.00056 $0.02448
Sonnet 5 $0.00022 $0.00979
Haiku 4.5 $0.00011 $0.00490

Measured 4d ago against content hash 4bf879746281, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

why 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 4d 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.

Origin

This is a copy

84% identical to why — 31 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/pstack/skills/why/SKILL.md · 247 lines

How it starts

The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Why

Investigate the motivation and intent behind code. Why was it built this way? What edge cases were considered? What product, business, or operational constraints shaped the design? What alternatives were rejected, and why?

Companion to the how skill. how answers what the code does and how it works. why answers what forces led to its shape.

How this skill works

Historical context spreads across seven evidence categories: source control history, issue or ticket tracking, long-form documents, real-time team chat, infrastructure observability, error or exception tracking, and product analytics warehouses. You cannot predict from the question alone which one holds the answer, so the skill enumerates available MCPs at run time, maps each to a category, queries all seven in parallel, then synthesizes with explicit confidence calibration. Null results from searched categories are first-class evidence about how the decision was made; report them alongside positive findings. The default is coverage, not minimalism.

Operating Posture

Operate as a careful, cautious, precise investigator. Think like a detective piecing together a historical case from fragmentary records. When the record is thin, say so.

Concretely:

  • Evidence before narrative. Collect the pieces first, then see what story they support. Never pick a story and recruit the evidence that fits it.
  • Precision over polish. Prefer the exact quote and citation over a smooth paraphrase. A reader should be able to follow any claim back to its source and verify it in under a minute.
  • Consider what you haven't seen. The evidence you find is a sample, not the whole truth. Before concluding, ask what you would expect to see if an alternative explanation were true, and whether you looked for it.
  • Name the gaps. If a thread goes cold, a source isn't searchable, or a question has no answer, document the gap. Don't paper it over with an authoritative-sounding guess.
  • Hedge on purpose. When evidence is indirect, your language should signal it ("appears to", "likely", "suggests"). Confidence-matching phrasing is a feature of the output, not a stylistic choice the synthesizer may override.
  • No shortcut by code-reading. The code tells you what it does, rarely why it exists. Resist inferring intent from code shape.

Read the full file on GitHub · 247 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. 4d ago First seen · 247 lines · 111 tokens per session scan A 4bf879746281

Subscribe to this mod's changes

why is a skill published in the GitHub repository Sma1lboy/rove (115 stars, last pushed yesterday), licensed MIT. It adds 111 tokens to every session and 4,897 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to why, differing in 31 lines, and is treated as a copy.

Related

Other skills, from other repositories