Verify that an answer's claims are actually grounded in the provided sources, flagging fabricated or unsupported statements before you trust the output. Split the answer into sentence-level claims and check each one for lexical support in the source documents. Use after RAG or summarization, before trusting or shipping the result, to catch the sentence the model invented that the sources never said.
Verify that the URLs and citations in an answer actually resolve, catching hallucinated or dead links before you trust or ship them. Extract every link (bare and markdown), check each over the network, and flag the ones that don't exist. Use when an LLM produced links/citations and you need to confirm they're real, not plausible-looking fiction.
Chain several tricks into one pipeline so the output of one flows straight into the next — redact then wash then strip, or wash then score — and run the whole routine in a single call. Use when a task needs more than one trick in sequence (clean-and-check, extract-and-analyze, gate-then-transform) instead of invoking each trick separately. Built on the Unix pipe: every trick is a stdin→stdout program, and combo wires the stages together, forwards each stage's summary, and aborts the moment a gate fails.
Catch overconfident phrasing in an answer and prefer calibrated uncertainty or honest abstention. Flag absolutes (always/never/guaranteed), bare certainty (definitely/obviously), and false authority (trust me / everyone knows) where the evidence is thin. Use when reviewing a draft answer for unwarranted certainty, or as a reflex to fold a weak hand — say "I don't know" — instead of confabulating a confident wrong answer.
Pat text down for secrets and PII before it leaves the building. Scan prompts, context, and pasted snippets for API keys, tokens, private keys, JWTs, and emails — then redact or refuse. Use before sending context/prompts that may contain credentials to an LLM or any third party, before pasting logs into an issue, or before piping output anywhere it'll be stored.
Adversarially interrogate an answer before trusting it — generate probing follow-up questions that attack its weak points across hidden assumptions, missing edge cases, internal contradictions, unsupported claims ("what's the source?"), overconfidence, and "what would change your mind?", then optionally run them against a model to see whether the answer holds up or cracks. Use to stress-test a draft answer or another model's output before you ship or trust it.
Ask one sharp clarifying question before acting when a request is underspecified, instead of guessing. Use when correctness matters more than speed and a wrong assumption would be costly or hard to reverse. Flips the default "always be helpful, always answer now" reflex into "ask first when it matters."
Wash the mechanical fingerprints out of text — zero-width and invisible characters, smart quotes, em/en dashes, the unicode ellipsis, non-breaking and exotic spaces, soft hyphens — so the bytes read like a human typed them. It scrubs typography, it does NOT rewrite prose (that's the model's job) or detect word-level tells (that's `tell`'s). Use before pasting model output where invisible characters or fancy punctuation would give it away, corrupt a diff, or break a downstream parser.