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.
npx agentmods add instructions/dixalex/decision-linter/claude-mdgit clone --depth 1 https://github.com/dixalex/decision-linterWrote 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/instructions/dixalex/decision-linter/claude-md)<a href="https://agentmods.dev/instructions/dixalex/decision-linter/claude-md"><img src="https://agentmods.dev/badge/instructions/dixalex/decision-linter/claude-md.svg" alt="Measured on agentmods" 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 | $0.00258 | $0.00258 |
| Opus 5 | $0.00129 | $0.00129 |
| Sonnet 5 | $0.00052 | $0.00052 |
| Haiku 4.5 | $0.00026 | $0.00026 |
Grade A, and why
decision-linter CLAUDE.md 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 5d 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.
What it actually says
Decision Linter — AI Skill for Pre-Commitment Judgment Checks
What This Is
A Claude Code skill that runs a structured judgment check before consequential decisions. Scores the decision environment as Kind (trust gut) or Wicked (impose structure), runs consider-the-opposite debiasing, surfaces blind spots, and outputs a paste-ready judgment memo.
Based on: Kahneman-Klein (conditions for intuitive expertise), Hogarth (kind vs wicked environments), Tetlock (superforecasting), Morewedge (debiasing interventions).
Installation
/plugin marketplace add dixalex/decision-linter
Or place skills/decision-linter/ in your .claude/skills/ folder.
Dependencies
None. This skill uses only Claude's reasoning — no external tools, MCP servers, or plugins required.
Usage
Invoke with /decision-linter or naturally: "check my assumptions", "lint this decision", "sanity check this proposal".
The skill also auto-triggers when it detects you're about to commit to a high-stakes, low-reversibility direction.
Output
Produces a paste-ready judgment memo (under 300 words) suitable for PRs, Slack, proposals, or decision logs.
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.
- 5d ago First seen · 30 lines · 258 tokens per session scan A 0cf3ee7906da
decision-linter CLAUDE.md is an instructions file published in the GitHub repository dixalex/decision-linter (2 stars, last pushed 4mo ago), licensed MIT. It adds 258 tokens to every session, about $0.0013 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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