align

A step-by-step planning guide that presents a proposed technical approach in small sections and asks for confirmation before going deeper.

In plain words
What is it for?
Use it to align on software designs, implementation plans, technical choices, and alternatives before writing code.
Why use it?
It reduces misunderstandings by agreeing on the overall design before detailed implementation decisions are made.

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/saski/arnesto/align
Any agent
npx skills add saski/arnesto --skill align
Clone the repo
git clone --depth 1 https://github.com/saski/arnesto

Made for: Claude Code, Codex.

Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 451 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00033 $0.00451
Opus 5 $0.00016 $0.00226
Sonnet 5 $0.00007 $0.00090
Haiku 4.5 $0.00003 $0.00045

Measured 2d ago against content hash 77f8e7d229f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

align 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 2d 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/align/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

STARTER_CHARACTER = 🎯

Core Principle

Propose, don't ask. Think first, then present your thinking for confirmation. The user's job is to course-correct, not to generate the approach.

Flow

Read input → Think internally → Present chunk → Confirm → Next chunk
                                                   ↑            |
                                                   └── Redirect ┘
  • Read the input context
  • Think through the full approach internally
  • Present a rough outline of chunks coming — gives the user the full scope before drilling in
  • Present each chunk progressively, starting from the highest level of abstraction
  • Confirm each chunk with AskUserQuestion before moving to the next
  • Drill down only after the big picture is confirmed

Presenting Chunks

Each chunk is a coherent topic. Present it with:

  • ⭐ Recommended approach with brief rationale
  • ❌ Alternatives considered with why they were rejected
  • ASCII diagram when showing structure or flow

Grouping Decisions

Group related small decisions into a single chunk with ⭐/❌ for each choice within it.

Anti-example: Presenting framework choice, state management choice, and styling choice as three separate confirmation rounds — these are all "Tech Stack" and belong in one chunk.

Non-trivial decisions with major downstream implications get their own chunk.

Chunk Size

Scannable in one read. If it needs scrolling, split it.

Handling Redirects

When the user rejects a chunk, downstream design may change. Don't present pre-computed chunks that depend on the rejected one. Rethink from the redirect point forward.

Anti-patterns

  • Asking open-ended questions instead of proposing (the user invoked this to see YOUR thinking)
  • Presenting the entire design at once (defeats progressive confirmation)
  • Moving to the next chunk without explicit confirmation
  • Presenting trivial decisions one at a time
  • Skipping ASCII diagrams for structural or flow topics
  • Continuing with pre-planned chunks after a redirect without reconsidering

Read the full file on GitHub · 59 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. 2d ago First seen · 59 lines · 33 tokens per session scan A 77f8e7d229f9

Subscribe to this mod's changes

align is a skill published in the GitHub repository saski/arnesto (5 stars, last pushed 7d ago), licensed Unlicense. It adds 33 tokens to every session and 451 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 2026-08-31.

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