# content-instruction-budget Check if instruction count in a file exceeds LLM instruction budget (~150) | | | |---|---| | **Severity** | warning (auto) | | **Autofix** | - | | **Since** | v0.7.0 | | **Category** | [Content Intelligence](content-intelligence.md) | ## Why Research on instruction-following shows compliance degrades as the number of simultaneous instructions grows. Beyond roughly 150 imperative instructions in a single file, the model begins silently dropping or deprioritizing rules. Staying within budget ensures every instruction actually influences behavior. ## Examples **Bad:** A CLAUDE.md with 200+ imperative lines covering every edge case. **Good:** A CLAUDE.md with ~80 focused instructions, with rarely-needed rules moved to `.claude/rules/` files that load only when relevant. ## How to fix Merge duplicate instructions, remove tautologies (things the model does by default), and move context-specific rules into scoped rule files (`.claude/rules/`) so they only load when relevant. A coding agent can consolidate instructions automatically. ## Configuration ```yaml rules: content-instruction-budget: enabled: auto # true | false | auto severity: warning ``` ## Research Basis **Warns when the count of imperative instructions in a single file exceeds ~150.** This rule counts **discrete directives** (lines starting with imperative verbs like "use", "always", "never", "ensure"), not raw tokens. The threshold is based on research showing that LLM instruction-following success degrades as a function of instruction *count*, independent of token length. The "Curse of Instructions" paper (ICLR 2025) demonstrated that the probability of following all N instructions equals (individual success rate)^N — exponential decay. GPT-4o achieved only 15% success at just 10 simultaneous instructions. The IFScale benchmark (2025) extended this to 500 instructions and found that **primacy bias becomes dominant at 150–200 instructions**: models begin selectively attending to earlier instructions and ignoring later ones. The ~150 threshold is where most models cross from "degraded but functional" to "selectively ignoring instructions." See [Instruction Budget vs. Context Budget](../research.md#instruction-budget-vs-context-budget) for how this differs from the `context-budget` rule. **References:** - [Curse of Instructions: Large Language Models Cannot Follow Multiple Instructions at Once](https://openreview.net/forum?id=R6q67CDBCH) (ICLR 2025) — Success rate = p^N; exponential decay with instruction count - Jaroslawicz et al., [How Many Instructions Can LLMs Follow at Once?](https://arxiv.org/abs/2507.11538) (arXiv:2507.11538, Jul 2025) — IFScale benchmark up to 500 instructions; primacy bias strongest at 150–200 - Levy, Jacoby & Goldberg, [Same Task, More Tokens](https://arxiv.org/abs/2402.14848) — Reasoning degrades at ~3,000 tokens; 150 instructions ≈ 1,500 tokens, leaving headroom *Run `skillsaw explain content-instruction-budget` to see this documentation and the rule's effective configuration in your terminal.*