timeboxed-iterating
Use when the user specifies a task and a duration, and the work should be done iteratively by subagents over that time period
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when the user specifies a task and a duration, and the work should be done iteratively by subagents over that time period
Bulletproof agent operating protocol. 15 failure-prevention rules distilled from 120+ real sessions and 10 agent definitions. Covers fabrication, constraint tracking, verification, scoping, retry discipline, and communication. Load before any task to prevent the most common agent failure modes.
Self-improving prompt optimization using the Karpathy autoresearch pattern. Scans any repo, suggests optimization targets, auto-defines binary eval metrics, and runs an autonomous generate-eval-score-mutate loop to improve prompts over time. Use when the user asks to optimize, improve, or run autoresearch on anything in their codebase.
Analyze manual edits made to an AI-generated blog article and update the write-article skill with the learnings. Use after manually correcting an AI-drafted article to improve future article generation. Triggers: refine skill, learn from edits, update skill from article, improve article skill, analyze my edits.
Use CortexDB for local-first AI memory, vector search, RAG, knowledge graphs, SPARQL/RDFS/SHACL, corpus-to-graph workflows, and MCP/tool calling. Use when working with CortexDB, embeddings, memory, RAG, GraphRAG, knowledge graph, RDF, SPARQL, SHACL, memoryflow, graphflow, or MCP tools.
Give a Python agent (such as Hermes Agent by Nous Research) durable, local-first memory plus a queryable SPARQL knowledge graph, backed by CortexDB through its gRPC sidecar and the cortexdb-client PyPI package. Use when a Python agent needs to remember facts about a user across turns/sessions, recall them by meaning, store entities and relations, or answer multi-hop questions — and when the user mentions CortexDB, agent memory, long-term memory, RAG, knowledge graph, Hermes, or "remember this".
Give a Node.js agent (such as OpenClaw) durable, local-first memory plus a queryable SPARQL knowledge graph, backed by CortexDB through its gRPC sidecar and the cortexdb-client npm package. Use when a Node agent needs to remember facts about a user across turns/sessions, recall them by meaning, store entities and relations, or answer multi-hop questions — and when the user mentions CortexDB, agent memory, long-term memory, RAG, knowledge graph, OpenClaw, or "remember this".
Find and install skills from skills.sh when a capability gap is detected
Maintain canonical AGENTS.md (open spec). Trigger: 'agents.md', 'update AGENTS.md', or auto-fired by ijfw-team after agent generation.