claudecodex

bootloops-protocols

The BootLoops protocol layer: seven disciplines for trustworthy quantitative work with agents — acceptance gates, integer-relation discipline, synthetic-truth controls, provenance bookkeeping, timing discipline, sources-only reading, tool stewardship.

28 on GitHubby Matthew D. Schwartzv1.0.0CC-BY-4.0 AND MITupdated 1 month agoproductivity
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Plugin bootloops-protocols from marketplace bootloops in BootLoops-ai/skills. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (7)

  • acceptance-gate

    The standard a computed result must meet before it may be called done. Use whenever an agent claims an integral is solved, a fit has closed, a formula is identified, or any quantitative answer is ready — the claim is not done until it passes this gate.

  • constant-recognition

    Discipline for integer-relation searches (PSLQ, LLL) that turn high-precision digits into exact constants. Use whenever recognizing a numerical value as a closed form, fitting boundary constants, or reporting that no closed form exists.

  • independence-bookkeeping

    Provenance rules that keep checks independent. Use when setting up any comparison, oracle, or reference value — and whenever tempted to certify a result with machinery that helped produce it.

  • planted-truth

    Synthetic-truth controls for analysis pipelines. Use before running any statistical fit, solver, audit, or search on real data — the pipeline must first prove it can recover a known planted answer and catch a deliberately corrupted input.

  • reading-contract

    Sources-only discipline for any instrument or agent that reads documents and asserts facts from them — literature audits, record replays, data extraction, citation of prior work.

  • timing-discipline

    Compute-planning rules. Use before launching any calculation whose runtime is not already measured — and whenever an agent proposes a long run, an overnight grind, or a bigger machine as the way forward.

  • tool-stewardship

    How the toolkit is consulted, extended, and documented. Use before writing any new code — and after, when a genuinely new instrument has earned its place.

View source on GitHub bootloops.ai

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The BootLoops research skills: heavier machinery for specific tasks — proving with agents (adversary-first pipelines), pre-circulation referee audit, the literature review behind a novelty claim, bibliography verification, and the scientific-prose linter.

5 skills

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