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Discover and install skills, docs, and rules to enhance your AI agent's capabilities.

AllSkillsDocsRules
NameContainsScore

self-evolving-single-agent

agentlas-ai/Agentlas-OS

Use when generating a single installable agent that should keep learning, track sources, refresh research, propose repairs, or improve itself over time without becoming a multi-agent team.

Skills

agentlas-ai/Agentlas-OS

Use when creating Codex, Claude Code, Gemini CLI, Cursor, or AGENTS.md runtime adapters from one canonical agent core. Use whenever a generated repo needs multiple AI runtimes without duplicating instructions.

Skills

agentlas-ai/Agentlas-OS

Use when packaging an Agentlas agent repo for public GitHub release, Codex plugin submission, Claude adapter distribution, or one-line terminal installation.

Skills

agentlas-ai/Agentlas-OS

Use when preserving product intent, acceptance criteria, decision memory, open loops, roadmap context, or the product-manager continuity layer of an agent team.

Skills

agentlas-ai/Agentlas-OS

Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

Skills

agentlas-ai/Agentlas-OS

Use when adding Memory Events, Memory Tickets, memory-map.json, vault-references.json, PM Soul memory ownership, or Memory Curator routing to an agent repo.

Skills

agentlas-ai/Agentlas-OS

Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.

Skills

agentlas-ai/Agentlas-OS

Use when verifying that a generated agent package can be installed, discovered by runtimes, and checked without private dependencies.

Skills

agentlas-ai/Agentlas-OS

Use when the user types /hep-storm, says @Hephaestus storm <goal>, or asks to drive a goal to verified completion through a force-robust Stormbreaker loop. Stormbreaker routes the goal to real Agentlas specialists, materializes a dependency-ordered pipeline fabric, and runs each work packet as a verifier-first hardened loop that does not stall, run away, or claim false success. Use it for loop-worthy work — apps, sites, agents, automations, debugging, multi-step research, data/report generation. Trivial questions are answered directly, not stormed.

Skills

agentlas-ai/Agentlas-OS

Use when the user types $hephaestus-network or /hep-network, mentions @Hephaestus, or asks Agentlas to staff a durable goal from registered Local, owner Cloud, and public Hub agents or teams. The active host LLM staffs each turn; the exact roster remains goal-bound until explicit completion.

Skills

agentlas-ai/Agentlas-OS

Use when the user types /hep-cloud or asks to staff from THEIR OWN Agentlas cloud packages only. Cloud is one exact source scope; Network means Local + owner Cloud + public Hub.

Skills

agentlas-ai/Agentlas-OS

Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.

Skills

agentlas-ai/Agentlas-OS

Use when converting, repairing, or packaging an existing local or external agent/team into Agentlas architecture for local install, Agentlas import, Codex plugin use, Claude adapter use, or open-source release.

Skills

agentlas-ai/Agentlas-OS

Use when creating a single Agentlas agent, creating a multi-agent team, or packaging an existing local/external agent into Agentlas architecture. Make sure to use this for /meta-agent requests.

Skills

agentlas-ai/Agentlas-OS

Use when adding or auditing local runtime behavior that turns a project folder into an Agentlas-aware workspace with .agentlas memory and sitemap files.

Skills

agentlas-ai/Agentlas-OS

Use when designing a new multi-agent team, visible agents folder, role boundaries, handoff flow, PM Soul, Memory Curator, Policy Gate, or evaluation role. Use for agent-team repo creation even when the user only says they want a meta-agent or agent operating system.

Skills

nagisanzenin/engram

Clear due memory reviews with free recall — the two-minute habit that makes learning permanent. Use when reviews are due, or the user wants to review, practice, or "do my engram reviews".

Skills

nagisanzenin/engram

Learning telemetry, strategy, and schedule — retention stats, calibration, grader audit, n-of-1 experiments, HTML dashboard. Use for "how am I doing", weekly check-ins, strategy questions, auditing the grader, or adjusting how Engram teaches.

Skills

cataclysmbn/Cataclysm-BN

Write or review Cataclysm-BN tests that avoid flakes, use deterministic fixtures, and clean up global state.

Skills

cataclysmbn/Cataclysm-BN

Finish release follow-up after the changelog exists. Update `data/XDG/org.cataclysmbn.CataclysmBN.metainfo.xml` and send the matching Flathub PR with `gh pr create --web`.

Skills

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