Borrowing it
Nothing to install: this file belongs to white1024/agent-nextup. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/white1024/agent-nextup/main/.claude/skills/align-scope/SKILL.mdgit clone --depth 1 https://github.com/white1024/agent-nextupWrote 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.
[](https://agentmods.dev/skills/white1024/agent-nextup/align-scope)<a href="https://agentmods.dev/skills/white1024/agent-nextup/align-scope"><img src="https://agentmods.dev/badge/skills/white1024/agent-nextup/align-scope/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.
<a href="https://agentmods.dev/skills/white1024/agent-nextup/align-scope"><img src="https://agentmods.dev/badge/skills/white1024/agent-nextup/align-scope.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00852 |
| Opus 5.5 | $0.00026 | $0.00341 |
| Sonnet 5.5 | $0.00013 | $0.00170 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
align-scope 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.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
align-scope — settle the scope before building
Goal: when the work starts, there is nothing left that "we will figure out later" — and every assumption behind it is either verified or written down as an assumption.
Use it when the ask is broad ("make the reporting better"), when two readings of the request would lead to materially different work, or when the cost of building the wrong thing is high. Skip it when the next step is obvious — an alignment ritual on a one-line fix is just friction.
1. Separate what you can find out from what only the human knows
Before asking anything, split the unknowns into two piles:
- Findable: the answer exists in the code, the files, the history, or the environment. Look it up yourself and spend the human's attention only on what needs their judgement. When a lookup will take a while, hand it to a sub-agent if your host has them and carry on: only the questions that depend on its answer wait for it.
- Human-only: priorities, trade-offs, what "good" means here, what happens to whoever uses this, what the deadline really is. These are the questions worth asking.
Say what you found before you ask. "The task files already carry a priority field, so ordering is possible today — the question is whether…" is a better question than "how should tasks be ordered?"
2. Ask in rounds: the whole frontier at once
The open decisions form a tree — some can only be asked once others are settled. The frontier is every question whose prerequisites are already answered: the ones you can ask now without guessing at answers you have not heard yet.
Ask the whole frontier in one round. Number each question, put your recommended answer under it with the reason in a line, and word the question so that "yes" accepts your recommendation:
Q1 — <short title>: <the question; the options, if it is a choice>
Recommended: <your answer> — <why, in one line>
Q2 — <short title>: …
Lead the round with the question whose answer changes the most about what gets built. A question that depends on another question in the same round belongs to the next round. Then wait for the answers: each round settles part of the tree and moves the frontier outward, so recompute it and ask the next round.
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.
- 4d ago Changed · +7 lines · +3 tokens per session 203413c251b7
- 1mo ago First seen · 44 lines · 61 tokens per session scan A 453a138b2924
align-scope is a skill published in the GitHub repository white1024/agent-nextup (2 stars, last pushed 5d ago), licensed MIT. It adds 64 tokens to every session and 852 once invoked, about $0.0003 per session on Opus 5.5. 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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