ai productivity
by Roy Yuen
High-speed intake for shaping vague prompts, triaging complex tasks, and compressing context for efficient execution.
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THE AGENSI STORE
19 skills found
by Roy Yuen
High-speed intake for shaping vague prompts, triaging complex tasks, and compressing context for efficient execution.
by Roy Yuen
Generate high-fidelity, structured handoff packets for seamless multi-agent collaboration and session persistence.
by loreto
Evaluates AI coding agent platforms across five structural dimensions that determine real-world performance independently of model quality, so teams select on architectural fit rather than benchmark scores.
by Julian
A 5-gate pre-flight audit to ensure your AI agent has the context, scope, and safety boundaries needed to code successfully.
by Roy Yuen
A high-precision pre-coding gate that detects conflicting requirements and prevents agents from making wrong assumptions.
by Sir Benjamin
Eliminate context drift and enhance depth with a multi-layered active reasoning framework for agents.
Lint a prompt template for the issues that cause injection and flaky output. Flags untrusted variables interpolated straight into the instructions (the injection surface), placeholders that are never provided or never used, contradictory instructions, a missing output-format spec where the result is parsed, unbounded context interpolation, and leftover placeholders. It detects problems; it does not write prompts.
by LocoLoboZ
A structured governance auditor to optimize AI project instructions, clean up context, and manage workspace health.
by S0lidByte
Master orchestrator that routes engineering tasks to 18 specialized AI skills with persistent project context.
Write and review the docs AI agents actually read — AGENTS.md for your repo and llms.txt for your site. Drafts them from scratch or audits existing ones for completeness, clarity, and wasted context, with a PASS or REVISE verdict.
A retrieval architect that diagnoses why RAG returns confident-but-wrong answers, picks the right context architecture (RAG vs knowledge graph vs structured/temporal retrieval) instead of defaulting to vector search, and designs the institutional-memory schema embeddings throw away.
by Shandra
Creates phased modernization plans for transforming monolithic applications into modular monoliths, service-oriented systems, or microservice-ready architectures without risky rewrites.
Turn long, messy threads into structured context packs for seamless session handoffs.
Diagnose and fix context-window problems in your agent setup. Inventories everything that loads — memory files, skills, MCP tool definitions, hooks — estimates where the budget goes, maps your symptoms to causes, and prescribes concrete trims, restructures, and session habits, rewriting the files where that's the fix.
Design the right team of specialized AI subagents for your project and get the definition files written properly — precise delegation triggers, focused system prompts, least-privilege tools, and the right model tier for each job. Includes a template, orchestration patterns, and a complete sample team.
by Timoranjes
Teaches AI coding agents the discipline of context engineering — designing what information the agent sees, when, and in what format — to dramatically reduce token waste, prevent session amnesia, and
by Shogun Labs
Systematic framework for engineering AI agent context at the right level of abstraction
by Shogun Labs
Battle-tested prompting patterns to eliminate LLM output drift. Sandwich structure, few-shot examples, history limits, retry, and token caps — 6 composable layers for production-grade agent reliability.
A deep reference library of production agent patterns — orchestration, context, tool design, failure and recovery, oversight, and evaluation. Every pattern states when it applies, when it's the wrong answer, what it costs, and the failure it prevents. Seven reference files, not a checklist.