pi-anthropic-auth: Skill for Claude Code

.pi/skills/improvement-discovery/SKILL.md

improvement-discovery is a skill for Claude Code, Codex from gotgenes/pi-anthropic-auth. It costs 47 tokens per session (4,963 once invoked), scanned A, original, MIT.

A planning guide for finding and ranking structural improvements in a software package. It uses a catalogue of common code smells and an ordered analysis process.

In plain words
What is it for?
Use it to plan a refactoring round, inspect the package architecture, identify coupling or unused subsystems, and choose which improvement to tackle first.
Why use it?
It gives improvement work a clear starting point and helps distinguish root design problems from symptoms found by automatic checks.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; positional $N argument.

This is gotgenes/pi-anthropic-auth's own configuration. It tells Claude Code and Codex how to work on pi-anthropic-auth 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 pi-anthropic-auth configures →

Reuse

Borrowing it

Nothing to install: this file belongs to gotgenes/pi-anthropic-auth. 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/gotgenes/pi-anthropic-auth/main/.pi/skills/improvement-discovery/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/gotgenes/pi-anthropic-auth

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 improvement-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/gotgenes/pi-anthropic-auth/improvement-discovery/github.svg)](https://agentmods.dev/skills/gotgenes/pi-anthropic-auth/improvement-discovery)
Your own site
<a href="https://agentmods.dev/skills/gotgenes/pi-anthropic-auth/improvement-discovery"><img src="https://agentmods.dev/badge/skills/gotgenes/pi-anthropic-auth/improvement-discovery/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 improvement-discovery

Your own site · 80×15
<a href="https://agentmods.dev/skills/gotgenes/pi-anthropic-auth/improvement-discovery"><img src="https://agentmods.dev/badge/skills/gotgenes/pi-anthropic-auth/improvement-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,963 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 $0.00047 $0.04963
Opus 5 $0.00023 $0.02482
Sonnet 5 $0.00009 $0.00993
Haiku 4.5 $0.00005 $0.00496

Measured 10d ago against content hash 425499e9afd0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

improvement-discovery 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.

.pi/skills/improvement-discovery/SKILL.md · 286 lines

How it starts

The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Improvement Discovery

Use this skill when planning the next round of structural improvements for this package. It codifies the patterns, smell categories, and analysis workflow that have proven effective across many phases of refactoring.

Analysis workflow

Follow this order — each step builds context for the next. Lead with the cause hypothesis, not the tool: fallow finds symptoms by construction (it is syntactic), so running it first frames the whole analysis around symptoms.

1. Read the architecture document and form a cause hypothesis

Load docs/architecture.md for the current domain model, health metrics table, and dependency bag inventory. Check which bags/hotspots have already been addressed vs. remain open. Before touching any tool, write down a cause hypothesis — the first-principles structural problem the next phase should dissolve (structural fusion, a coupling/boundary flaw, a dead subsystem). The later steps corroborate, refine, or refute it. The prior phase's plan carries candidates beyond any explicit "leading candidate" line: a ⚠️ metric miss in its health-metrics table and any in-step "deferred" remark are implicit candidates, and each metric miss gets an explicit disposition in the new roadmap (re-target / accept with rationale / supersede) — never a silent drop. A cause-level finding must trace to a named target concept in the architecture doc's first-principles section (the pattern, from another Pi package: pi-permission-system's "The authority model"). When no such section exists, writing one — naming the organizing concept and recording resolved design directions — is itself a phase deliverable, not an emergent artifact: settled-in-writing directions are what make the next phase's plan cheap.

2. Sweep open issues

Run gh issue list --state open and cross-check it against the architecture doc's claims about which issues remain open — doc/tracker drift otherwise causes re-planning filed work or missing a parked candidate. An open issue that already names a cause-level finding is a pre-discovered candidate — adopt it as a phase step under its existing number rather than re-deriving it. Track repeat deferrals: an issue swept as out-of-scope across multiple consecutive phases gets an explicit decision this phase — schedule it into the phase, or recommend closing it as not-planned — never a silent re-defer. Structural phases must not starve feature and bug work indefinitely.

Read the full file on GitHub · 286 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. 10d ago First seen · 286 lines · 47 tokens per session scan A 425499e9afd0

Subscribe to this mod's changes

improvement-discovery is a skill published in the GitHub repository gotgenes/pi-anthropic-auth (242 stars, last pushed 5d ago), licensed MIT. It adds 47 tokens to every session and 4,963 once invoked, about $0.0002 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.

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