bundled-skills/research-prompt/SKILL.md
Turn vague research needs into one precise deep-research prompt with context and output criteria.
npx skillsauth add FrancoStino/opencode-skills-antigravity research-promptInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Goal: turn a vague research need into ONE self-contained paragraph that a researcher with zero prior knowledge of the project can act on with zero back-and-forth.
[For a reader with zero prior knowledge: in 1–2 plain-English sentences, what the project/product is, why it exists, and the current situation.] Research [TOPIC + key identifying facts] to answer one question: [THE QUESTION] — for [DECISION / END USE]. Find: (1) …; (2) …; (3) …; (4) …. [Constraints: include X, avoid Y.] Prefer primary sources; treat forums/social as weak signal only; if sources conflict, separate fact from inference and flag what needs verification. Don't stop at the first plausible answer: corroborate each key claim with multiple independent primary sources where they exist (and say so explicitly where they don't), continuing until every numbered question is covered to that bar. Before finishing, do a self-critique pass — list gaps, contradictions, and any single-source claims, then run another round of searches to close them, repeating until clean. For each point, give the source link, the specific claim, and a one-line "why it matters". No marketing fluff — verifiable, citable facts only. Output everything into a single detailed markdown file.
To run the finished prompt with an AI researcher, execute it via DeepAPI POST /v1/research/deep — follow the deep-research skill.
davidondrej/skills; verify local paths, tools, credentials, and agent features before acting.tools
Authorized security assessment of LLM applications and AI agents: prompt injection, tool abuse, RAG exposure, memory poisoning, system-prompt extraction, and agent-compliance engineering per OWASP LLM/ASI Top 10.
development
Builds two parameterized UI modes—流光溢彩白 (iridescent white) and 五彩斑斓黑 (colorful black)—with OKLCH, WebGL/CSS fallback, vision gating, screenshot QA, and total/per-color intensity reports. Use when a UI request names either mode or needs measured color parameters.
tools
Delegate coding tasks to the Kimi Code CLI (`kimi`) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
development
Front-end JavaScript reverse engineering: locate signature chains, analyze encrypted request parameters, sample runtime behavior, and reproduce logic locally in Node for evidence-based output.