--- title: "Undominated · Long-Context Distraction Audit: review, setup & limitations · Undominated.ai" canonical: https://undominated.ai/skills/undominated-context-distraction-audit/ description: "Audits retrieval accuracy degradation as irrelevant distractor documents are added to prompts (evaluating signal-to-noise ratio in long contexts)." --- # Undominated · Long-Context Distraction Audit: review, setup & limitations · Undominated.ai > Audits retrieval accuracy degradation as irrelevant distractor documents are added to prompts (evaluating signal-to-noise ratio in long contexts). [← Explore all skills](/skills/) EVIDENCE AND EVALUATION / Undominated.ai # Undominated · Long-Context Distraction Audit Audits retrieval accuracy degradation as irrelevant distractor documents are added to prompts (evaluating signal-to-noise ratio in long contexts). [See setup guidance ↓](#setup)[Original source ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/definition.md) SOURCE REVIEW Reviewed 2026-10-14 Evidence 3 linked sources Publisher Undominated.ai Licence [MIT ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/LICENSE.txt) Revision 6429b5e267c9 [Read what was—and wasn’t—checked ↓](#review) “Audits retrieval accuracy degradation as irrelevant distractor documents are added to prompts (evaluating signal-to-noise ratio in long contexts).” [Undominated.ai · upstream description ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/definition.md) Our analysis follows below. 01 / THE REASONING ## Why this made the selection. - Pairs a bounded workflow with an offline Python checker and explicitly synthetic example inputs. - Created for recurring evidence and comparison failures: missing facts remain unknown and every conclusion keeps its scope. - It names a concrete failure mode: Audits retrieval accuracy degradation as irrelevant distractor documents are added to prompts (evaluating signal-to-noise ratio in long contexts). ### A good fit for - Deploy before loading 100k+ token document bundles into prompt context without prior reranking. - Run the local checker against a supplied file for this stated condition: passes = drop <= max_drop. ### Weigh up before choosing - Distractor documents must share lexical domain similarity with target needles to detect realistic confusion. - Fixture checks establish the documented local behaviour, not performance on an unrestricted set of real tasks. - Public GitHub source availability is not yet verified; the complete download contains the reviewed source. - A new npm install path is not yet verified. Use the complete source download. - Exits `0` on verified pass, `1` on failed assertion, `2` on malformed input. 02 / THE REVIEW RECORD ## What we actually inspected. Source review has boundaries. A clear record is more useful than a “safe” badge. ### Material inspected - skills/undominated-context-distraction-audit/SKILL.md - skills/undominated-context-distraction-audit/scripts/check.py - skills/undominated-context-distraction-audit/examples/synthetic.json ### Our findings - First-party resource authored by Undominated.ai; this is our own verification record, not an independent endorsement. - The download preserves the complete source and supporting files. Its manifest hashes identify the exact bytes. - The checker names its required fields as: required = ["isolated_recall_percent", "distracted_recall_percent", "max_allowed_drop_percent"]. - The pass expression is passes = drop <= max_drop. The process then runs sys.exit(0 if passes else 1). ### Not established by this review - Model compliance with these instructions on arbitrary tasks - Native integration in every agent host - Public GitHub source identity - Remote installation through the standard Skills CLI - The new npm resource CLI installation The review applies to the material and revision named here. A newer upstream release can change its behavior. 03 / PUT IT TO WORK ## Add a skill to your workflow. [Upstream setup instructions ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/definition.md) - Download the complete bundle from https://undominated.ai/resources/skills/undominated-context-distraction-audit/bundle.zip and extract it into a new directory. - Place the extracted directory at .agents/skills/undominated-context-distraction-audit/ or your host's documented skills directory. Preserve SKILL.md, scripts, examples and licence files together. - Run the documented synthetic example from the skill directory with Python 3.10 or later before using your own evidence. ### Before you start - An Agent Skills compatible host - Python 3.10 or later for the optional checker ### Compatibility Agent Skills compatible hosts · Python 3.10+ ### Implementation Python · Markdown ### Local, explicit inputs - read:user-selected-local-file ### Cost model MIT source at no charge. Your agent host or model provider may charge for use; the included offline checks require no paid API. THE COMPLETE REVIEWED DEFINITION ## Read it before you reuse it. Original source bytes, with attribution. Review the host-specific setup notes above. Copy definition ↗ [Download complete resource + licence ↗](/resources/skills/undominated-context-distraction-audit/bundle.zip)[Raw Markdown ↗](/resources/skills/undominated-context-distraction-audit/definition.md) --- name: undominated-context-distraction-audit description: Audits retrieval accuracy degradation as irrelevant distractor documents are added to prompts (evaluating signal-to-noise ratio in long contexts). license: MIT metadata: author: Undominated.ai version: "1.0.0" tags: ["long-context", "distraction", "retrieval", "rag", "needle"] --- # Long-Context Distraction Audit Audits retrieval accuracy degradation as irrelevant distractor documents are added to prompts (evaluating signal-to-noise ratio in long contexts). ## When to Use Deploy before loading 100k+ token document bundles into prompt context without prior reranking. ## Limitations Distractor documents must share lexical domain similarity with target needles to detect realistic confusion. ## Verification Script Run the deterministic stdlib checker: ```bash python3 scripts/check.py examples/synthetic.json ``` Exits `0` on verified pass, `1` on failed assertion, `2` on malformed input. The download contains SKILL.md . Extract the whole bundle; the supporting files are required. Inspect the included MANIFEST.json for file hashes. By **Undominated.ai**. [Exact upstream source ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/definition.md) · [Licence](/resources/skills/undominated-context-distraction-audit/LICENSE.txt) · [Attribution](/resources/skills/undominated-context-distraction-audit/ATTRIBUTION.txt) SHA-256 6429b5e267c9b0d35c945d1e6e7db141579d46dcf3bcd7fcb2af9d2174aa7ade Read the applicable licence MIT License Copyright (c) 2026 Undominated.ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 04 / FOLLOW THE EVIDENCE ## The source trail. Our notes are separate from the original resource. Check upstream before adopting a new version. - [Original Undominated definition ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/definition.md) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-context-distraction-audit/definition.md Supports: Workflow and input requirements, Stated limitations - [MIT licence ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/LICENSE.txt) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-context-distraction-audit/LICENSE.txt Supports: Redistribution terms and attribution - [Complete source bundle ↗](https://undominated.ai/resources/skills/undominated-context-distraction-audit/bundle.zip) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-context-distraction-audit/bundle.zip Supports: Full local source, supporting files and integrity manifest KEEP COMPARING ## Other approaches to consider. Related by category or shared topics. These are alternatives to inspect, not a measured quality order. [### Undominated · Needle-in-a-haystack density audit ↗ Audit long-context retrieval recall across varying context lengths, needle depth placements, and distractor similarity levels.](/skills/undominated-needle-in-haystack-density-audit/)[### Undominated · A prompt is not the catalogue ↗ Refuse a prompt whose scope is the whole catalogue. Retrieval stays top-k. No catalogue is copied.](/skills/undominated-topk-not-dump/)[### Undominated · KV Cache INT4 Outlier Drift Audit ↗ Audits key/value cache INT4 quantization channel scaling to prevent catastrophic attention drift on long sequences.](/skills/undominated-kv-cache-int4-outlier-drift-audit/) [AI Tools ↗](/tools/)[Skills ↗](/skills/)[Agents ↗](/agents/)[MCP Servers ↗](/mcp-servers/)[Workflows ↗](/workflows/) ## Continue your investigation - [Choose a toolkit](/tools/) - [Inspect execution hosts](/agents/) - [Check tool access](/mcp-servers/) - [Check project dependencies](/check-repo/)