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Rewrite 548 skill descriptions to the activation rubric

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Each rewritten description now states both what the skill does (concrete
capability, named tools/artifacts) and an explicit when-to-use trigger,
improving agent discovery/activation. Grounded in each skill's own body;
changes confined to the `description` field only (bodies and all other
frontmatter untouched). Produced by a gated audit->rewrite->recheck loop
(548 -> 0 flagged) with a sampled anti-invention check (0 ungrounded).

Schema: 817/817 pass. Framework-ID gate: 0 defects.
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Mahipal
2026-08-02 09:32:13 -07:00
parent 04a207702e
commit 2fb6a9faff
548 changed files with 2189 additions and 1915 deletions
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skills/detecting-data-and-model-poisoning/SKILL.md
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---
name: detecting-data-and-model-poisoning
description: Identify poisoned training data and backdoored models across the ML pipeline.
description: Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).
domain: cybersecurity
subdomain: ai-security
tags: