Use when the user requests integration testing, feature validation, or test plan execution
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Restate content as a flat list of atomic facts in the style of av/facts fact sheets. Use when the user says "just facts", "facts only", "as facts", "give me the facts", or replies with a bare "facts" / "facts?" to a previous message — then the scope is that message's content.
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See how to installReview the instructions the agent will receive before you install.
Restate content as facts, in the style of av/facts fact sheets (github.com/av/facts). The output is a flat list of atomic claims: short enough to read in full, complete enough that nothing in scope is lost.
One fact per line, - prefixed. Group with # headings only when there are 2+ distinct domains; skip headings for short outputs. No intro, no outro, no transitions, no "in summary". The fact list IS the message.
# billing
- MRR is $48.2k, up 6% MoM [stripe, 2026-08-25]
- 3 enterprise deals in contract stage
- Brain Restore Centers closed at $500/mo
# risks
- churn concentrated in workspaces without custom skills
- VIK-2058 unassigned until 07:20Z, now with Ivan
- vendor claims fix ships Friday (unverified). Never upgrade a guess to a fact.[source, YYYY-MM-DD] for volatile data.- Viktor recommends X because Y).- not covered in source: X.No reviews yet. Tell others what you thought.
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Use when the user requests integration testing, feature validation, or test plan execution
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Use when the user wants to systematically fix AI code slop — duplicated logic, over-engineering, silent error swallowing, convention drift, cargo-cult patterns, and other LLM-introduced architectural decay — over a specified duration
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Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.
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Runs autonomous keep/discard experiments on a codebase to optimize a single metric for a fixed duration, in the style of karpathy/autoresearch. Use when the user says "autoresearch" (optionally with a focus, e.g. "autoresearch the optimizer"), asks to run experiments on a repo overnight, to hill-climb or optimize a metric autonomously, or points at a repo with a karpathy-style program.md.
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Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.
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Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.
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