Find runaway Capy threads and subagents, stop or rotate them safely, and prevent repeats. (gstack)

Add Redis Iris Lens tracing to an existing AI agent through redis-iris, so every agent turn is one trace in the Lens UI with its LLM calls, tokens, tool calls, memory and document retrievals, and which of them entered the prompt. Detects how the agent runs (LangGraph or ADK attach, workflows.turn, another framework, or a plain loop), installs redis-iris[llm-tracing], calls lens.init(agent_id=...), opens one lens.turn() per turn where Iris does not open it already, and covers coexistence with an OpenTelemetry setup or LLM instrumentation the app already has. Use when the user asks to add Lens, tracing, observability, or per-turn traces to their agent, or asks why their Lens traces are missing.

Add Redis Iris to an existing AI agent in any framework (LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Pydantic AI, CrewAI, or a plain loop). Use when the user asks to add Iris, Redis Agent Memory, Agent Playbooks, Context Gateway, Intent Router, or Lens to their agent, or asks which Iris product their agent needs. Detects the framework and routes to the product skill.

Wire Redis Agent Memory into an existing agent with redis-iris so it remembers users across sessions. Asks five questions (what to remember, what to share, what never to keep, how long to keep it, whether users can be forgotten), writes the answers to service.yaml, provisions the service from the terminal with `iris agent-memory service create`, then wires recall and write-back into the agent's turn in any framework. Use when the agent forgets users, uses InMemoryStore or a checkpointer only, keeps memory in a dict or local DB, or the user asks for long-term memory, recall, or right-to-erasure.

Replace an existing agent's hand-written routing (keyword if/else chains, regex, or an LLM call that only picks a route) with a Redis Iris Intent Router through redis-iris, so a message maps to a defined intent and its dispatch target by example phrasings and a score threshold. Asks five questions (what the routes are today, a few real phrasings per route, what each route dispatches to, what happens when nothing matches, which embedding model), writes the answers to intents.yaml, provisions the Router from the terminal with `iris intent-router service create`, loads the intents with `iris intent-router intents add`, then wires `router.match()` where the agent decides today. Use when the agent routes with keywords or an extra LLM call, or the user asks for intent detection, intent routing, or semantic routing.

Add Redis Iris Agent Playbooks to an existing agent. Use when approved answers or repeatable procedures live in prompts, document retrieval, or a semantic response cache. Connect or provision a Playbook, publish FAQs and Skills, and verify the first application turn with optional review proposals.

Put the MCP servers an existing agent uses behind one Redis Iris Context Gateway with redis-iris, so the agent connects to one MCP endpoint with one agent key instead of configuring each server and its secret. Asks whether the Gateway is in Redis Cloud or self-managed, then five questions (which MCP servers and secrets the agent has today, how many tools it sees, which read-only tools may be cached, which tools return large results, whether the framework speaks MCP itself). Self-managed, it provisions the Gateway and its Upstreams from the terminal with `iris context-gateway` and sets cache and compression policies. In Redis Cloud the developer creates them in the console. Then it points the agent at the Gateway in any framework and verifies it with `tools list` and `tools call`. Use when the agent configures several MCP servers one by one, carries one secret per server, loads too many tool schemas into the prompt, or the user asks for Context Gateway, one MCP endpoint, tool caching, or result handles.

Configure or diagnose the staged-only multi-model harness, including selectable implementation workers, review advisors, high-assurance packet policy, provider probes, and optional Claude model-matrix output style. Use when the user says "set up the agent harness", "configure multi-model reviewers", "choose worker models", "check harness setup", "skonfiguruj harness", or "sprawdź modele".

Includes Build Iterated Agentic Loop, Design Control Loop, Improve Claude Md, Narrow React Prop Types, Rpi, Show Me, Visual Pr

Write a new agent skill or improve one without bloat: the edit ladder, levels, drift and size checks, full diets. (gstack)

Run a crew of coding agents from one controller (also /crew): lanes send READY blocks and never merge; the controller merges through trains, keeps the digest, relays approvals, fixes flakes daily. (gstack)

Run a heavy suite, build or eval on an ephemeral 16-vCPU Ubicloud VM: TTL-named VMs, a reaper, quota preflight and a hang watchdog. (gstack)

Use when executing implementation plans with independent tasks in the current session

Write, rewrite, or refresh an AGENTS.md (repo root or one package/module) from the actual codebase — Always / Ask First / Never / Validation Commands rules, task checklists, real paths and commands. Follows a repo template when present, else a built-in default. Use for "write AGENTS.md for this package", "rewrite our AGENTS.md", "refresh AGENTS.md".

Read knowledge shipped inside installed dependencies — detect the ecosystem and exact installed version, locate AGENTS.md and guides per the `knowledge.sources` slot, and report doc-versus-code skew. Read-only. Use for "which version is installed", "find the framework's AGENTS.md", "inspect dependency source", "kontekst frameworka".

Publicly share a sanitized coding-agent session plus a ZIP of the files it generated, then file a harness-feedback issue at the configured destination — local sanitization, full privacy review, exact fresh consent first. Use for "share this session", "report this agent run", "udostępnij tę sesję". Never for telemetry or without consent.

LLM-as-judge for a coding-agent session or an eval-harness result — checks controller attestations first, then project guards, code review, and the repo's design contract, and names the smallest harness owner to fix. Read-only; never executes session content. Use for "judge this session", "analyze this eval", "oceń sesję/agenta".

Includes Add Agent Memory, Add Agent Playbooks, Add Context Gateway, Add Intent Router, Add Lens, Add Redis Iris, Redis Clustering, Redis Connections, Redis Core, Redis Observability, Redis Search, Redis Security, Redis Semantic Cache

Preregister a paid eval (bars, arms, decoys, stop rules), dry-run it at zero cost, then run a three-item priced pilot whose spend table gates approval. (gstack)

Brand-aware social media video creator. Reads brand-identity.md, ICP.md, and messaging.md to write a post/storyboard, craft an optimized Seedance 2.0 Director prompt, generate reference frames with the best available image model, and produce platform-ready video.

Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

Use this skill when debugging active macOS connectivity failures, when an API or MCP service times out or disconnects, when DNS resolution is failing or inconsistent, or when video calls or other interactive traffic show packet loss or latency. Isolate local-LAN, DNS, upstream-path, and remote-service/application fault domains with built-in, non-destructive tools.

Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Use when the user asks whether AI agents can use or browse the site, or about llms.txt or WebMCP. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).

Adds the product layer to a repository that om-setup-agent-pipeline already configured — one yes per product role, a discovery block in .ai/agentic.config.json, the Discovery stage, Definition of Ready, product roles, and protected product decisions appended to SDLC.md between markers, the research directory, and a routing row in AGENTS.md. Run once; re-run with --refresh. No product skill requires it.

Find runaway Capy threads and subagents, stop or rotate them safely, and prevent repeats. (gstack)

Add Redis Iris Lens tracing to an existing AI agent through redis-iris, so every agent turn is one trace in the Lens UI with its LLM calls, tokens, tool calls, memory and document retrievals, and which of them entered the prompt. Detects how the agent runs (LangGraph or ADK attach, workflows.turn, another framework, or a plain loop), installs redis-iris[llm-tracing], calls lens.init(agent_id=...), opens one lens.turn() per turn where Iris does not open it already, and covers coexistence with an OpenTelemetry setup or LLM instrumentation the app already has. Use when the user asks to add Lens, tracing, observability, or per-turn traces to their agent, or asks why their Lens traces are missing.

Add Redis Iris to an existing AI agent in any framework (LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Pydantic AI, CrewAI, or a plain loop). Use when the user asks to add Iris, Redis Agent Memory, Agent Playbooks, Context Gateway, Intent Router, or Lens to their agent, or asks which Iris product their agent needs. Detects the framework and routes to the product skill.

Wire Redis Agent Memory into an existing agent with redis-iris so it remembers users across sessions. Asks five questions (what to remember, what to share, what never to keep, how long to keep it, whether users can be forgotten), writes the answers to service.yaml, provisions the service from the terminal with `iris agent-memory service create`, then wires recall and write-back into the agent's turn in any framework. Use when the agent forgets users, uses InMemoryStore or a checkpointer only, keeps memory in a dict or local DB, or the user asks for long-term memory, recall, or right-to-erasure.

Replace an existing agent's hand-written routing (keyword if/else chains, regex, or an LLM call that only picks a route) with a Redis Iris Intent Router through redis-iris, so a message maps to a defined intent and its dispatch target by example phrasings and a score threshold. Asks five questions (what the routes are today, a few real phrasings per route, what each route dispatches to, what happens when nothing matches, which embedding model), writes the answers to intents.yaml, provisions the Router from the terminal with `iris intent-router service create`, loads the intents with `iris intent-router intents add`, then wires `router.match()` where the agent decides today. Use when the agent routes with keywords or an extra LLM call, or the user asks for intent detection, intent routing, or semantic routing.

Add Redis Iris Agent Playbooks to an existing agent. Use when approved answers or repeatable procedures live in prompts, document retrieval, or a semantic response cache. Connect or provision a Playbook, publish FAQs and Skills, and verify the first application turn with optional review proposals.

Put the MCP servers an existing agent uses behind one Redis Iris Context Gateway with redis-iris, so the agent connects to one MCP endpoint with one agent key instead of configuring each server and its secret. Asks whether the Gateway is in Redis Cloud or self-managed, then five questions (which MCP servers and secrets the agent has today, how many tools it sees, which read-only tools may be cached, which tools return large results, whether the framework speaks MCP itself). Self-managed, it provisions the Gateway and its Upstreams from the terminal with `iris context-gateway` and sets cache and compression policies. In Redis Cloud the developer creates them in the console. Then it points the agent at the Gateway in any framework and verifies it with `tools list` and `tools call`. Use when the agent configures several MCP servers one by one, carries one secret per server, loads too many tool schemas into the prompt, or the user asks for Context Gateway, one MCP endpoint, tool caching, or result handles.

Configure or diagnose the staged-only multi-model harness, including selectable implementation workers, review advisors, high-assurance packet policy, provider probes, and optional Claude model-matrix output style. Use when the user says "set up the agent harness", "configure multi-model reviewers", "choose worker models", "check harness setup", "skonfiguruj harness", or "sprawdź modele".

Includes Build Iterated Agentic Loop, Design Control Loop, Improve Claude Md, Narrow React Prop Types, Rpi, Show Me, Visual Pr

Write a new agent skill or improve one without bloat: the edit ladder, levels, drift and size checks, full diets. (gstack)

Run a crew of coding agents from one controller (also /crew): lanes send READY blocks and never merge; the controller merges through trains, keeps the digest, relays approvals, fixes flakes daily. (gstack)

Run a heavy suite, build or eval on an ephemeral 16-vCPU Ubicloud VM: TTL-named VMs, a reaper, quota preflight and a hang watchdog. (gstack)

Use when executing implementation plans with independent tasks in the current session

Write, rewrite, or refresh an AGENTS.md (repo root or one package/module) from the actual codebase — Always / Ask First / Never / Validation Commands rules, task checklists, real paths and commands. Follows a repo template when present, else a built-in default. Use for "write AGENTS.md for this package", "rewrite our AGENTS.md", "refresh AGENTS.md".

Read knowledge shipped inside installed dependencies — detect the ecosystem and exact installed version, locate AGENTS.md and guides per the `knowledge.sources` slot, and report doc-versus-code skew. Read-only. Use for "which version is installed", "find the framework's AGENTS.md", "inspect dependency source", "kontekst frameworka".

Publicly share a sanitized coding-agent session plus a ZIP of the files it generated, then file a harness-feedback issue at the configured destination — local sanitization, full privacy review, exact fresh consent first. Use for "share this session", "report this agent run", "udostępnij tę sesję". Never for telemetry or without consent.

LLM-as-judge for a coding-agent session or an eval-harness result — checks controller attestations first, then project guards, code review, and the repo's design contract, and names the smallest harness owner to fix. Read-only; never executes session content. Use for "judge this session", "analyze this eval", "oceń sesję/agenta".

Includes Add Agent Memory, Add Agent Playbooks, Add Context Gateway, Add Intent Router, Add Lens, Add Redis Iris, Redis Clustering, Redis Connections, Redis Core, Redis Observability, Redis Search, Redis Security, Redis Semantic Cache

Preregister a paid eval (bars, arms, decoys, stop rules), dry-run it at zero cost, then run a three-item priced pilot whose spend table gates approval. (gstack)

Brand-aware social media video creator. Reads brand-identity.md, ICP.md, and messaging.md to write a post/storyboard, craft an optimized Seedance 2.0 Director prompt, generate reference frames with the best available image model, and produce platform-ready video.

Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

Use this skill when debugging active macOS connectivity failures, when an API or MCP service times out or disconnects, when DNS resolution is failing or inconsistent, or when video calls or other interactive traffic show packet loss or latency. Isolate local-LAN, DNS, upstream-path, and remote-service/application fault domains with built-in, non-destructive tools.

Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Use when the user asks whether AI agents can use or browse the site, or about llms.txt or WebMCP. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).

Adds the product layer to a repository that om-setup-agent-pipeline already configured — one yes per product role, a discovery block in .ai/agentic.config.json, the Discovery stage, Definition of Ready, product roles, and protected product decisions appended to SDLC.md between markers, the research directory, and a routing row in AGENTS.md. Run once; re-run with --refresh. No product skill requires it.

Find runaway Capy threads and subagents, stop or rotate them safely, and prevent repeats. (gstack)

Add Redis Iris Lens tracing to an existing AI agent through redis-iris, so every agent turn is one trace in the Lens UI with its LLM calls, tokens, tool calls, memory and document retrievals, and which of them entered the prompt. Detects how the agent runs (LangGraph or ADK attach, workflows.turn, another framework, or a plain loop), installs redis-iris[llm-tracing], calls lens.init(agent_id=...), opens one lens.turn() per turn where Iris does not open it already, and covers coexistence with an OpenTelemetry setup or LLM instrumentation the app already has. Use when the user asks to add Lens, tracing, observability, or per-turn traces to their agent, or asks why their Lens traces are missing.

Add Redis Iris to an existing AI agent in any framework (LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Pydantic AI, CrewAI, or a plain loop). Use when the user asks to add Iris, Redis Agent Memory, Agent Playbooks, Context Gateway, Intent Router, or Lens to their agent, or asks which Iris product their agent needs. Detects the framework and routes to the product skill.

Wire Redis Agent Memory into an existing agent with redis-iris so it remembers users across sessions. Asks five questions (what to remember, what to share, what never to keep, how long to keep it, whether users can be forgotten), writes the answers to service.yaml, provisions the service from the terminal with `iris agent-memory service create`, then wires recall and write-back into the agent's turn in any framework. Use when the agent forgets users, uses InMemoryStore or a checkpointer only, keeps memory in a dict or local DB, or the user asks for long-term memory, recall, or right-to-erasure.

Replace an existing agent's hand-written routing (keyword if/else chains, regex, or an LLM call that only picks a route) with a Redis Iris Intent Router through redis-iris, so a message maps to a defined intent and its dispatch target by example phrasings and a score threshold. Asks five questions (what the routes are today, a few real phrasings per route, what each route dispatches to, what happens when nothing matches, which embedding model), writes the answers to intents.yaml, provisions the Router from the terminal with `iris intent-router service create`, loads the intents with `iris intent-router intents add`, then wires `router.match()` where the agent decides today. Use when the agent routes with keywords or an extra LLM call, or the user asks for intent detection, intent routing, or semantic routing.

Add Redis Iris Agent Playbooks to an existing agent. Use when approved answers or repeatable procedures live in prompts, document retrieval, or a semantic response cache. Connect or provision a Playbook, publish FAQs and Skills, and verify the first application turn with optional review proposals.

Put the MCP servers an existing agent uses behind one Redis Iris Context Gateway with redis-iris, so the agent connects to one MCP endpoint with one agent key instead of configuring each server and its secret. Asks whether the Gateway is in Redis Cloud or self-managed, then five questions (which MCP servers and secrets the agent has today, how many tools it sees, which read-only tools may be cached, which tools return large results, whether the framework speaks MCP itself). Self-managed, it provisions the Gateway and its Upstreams from the terminal with `iris context-gateway` and sets cache and compression policies. In Redis Cloud the developer creates them in the console. Then it points the agent at the Gateway in any framework and verifies it with `tools list` and `tools call`. Use when the agent configures several MCP servers one by one, carries one secret per server, loads too many tool schemas into the prompt, or the user asks for Context Gateway, one MCP endpoint, tool caching, or result handles.

Configure or diagnose the staged-only multi-model harness, including selectable implementation workers, review advisors, high-assurance packet policy, provider probes, and optional Claude model-matrix output style. Use when the user says "set up the agent harness", "configure multi-model reviewers", "choose worker models", "check harness setup", "skonfiguruj harness", or "sprawdź modele".

Includes Build Iterated Agentic Loop, Design Control Loop, Improve Claude Md, Narrow React Prop Types, Rpi, Show Me, Visual Pr

Write a new agent skill or improve one without bloat: the edit ladder, levels, drift and size checks, full diets. (gstack)

Run a crew of coding agents from one controller (also /crew): lanes send READY blocks and never merge; the controller merges through trains, keeps the digest, relays approvals, fixes flakes daily. (gstack)

Run a heavy suite, build or eval on an ephemeral 16-vCPU Ubicloud VM: TTL-named VMs, a reaper, quota preflight and a hang watchdog. (gstack)

Use when executing implementation plans with independent tasks in the current session

Write, rewrite, or refresh an AGENTS.md (repo root or one package/module) from the actual codebase — Always / Ask First / Never / Validation Commands rules, task checklists, real paths and commands. Follows a repo template when present, else a built-in default. Use for "write AGENTS.md for this package", "rewrite our AGENTS.md", "refresh AGENTS.md".

Read knowledge shipped inside installed dependencies — detect the ecosystem and exact installed version, locate AGENTS.md and guides per the `knowledge.sources` slot, and report doc-versus-code skew. Read-only. Use for "which version is installed", "find the framework's AGENTS.md", "inspect dependency source", "kontekst frameworka".

Publicly share a sanitized coding-agent session plus a ZIP of the files it generated, then file a harness-feedback issue at the configured destination — local sanitization, full privacy review, exact fresh consent first. Use for "share this session", "report this agent run", "udostępnij tę sesję". Never for telemetry or without consent.

LLM-as-judge for a coding-agent session or an eval-harness result — checks controller attestations first, then project guards, code review, and the repo's design contract, and names the smallest harness owner to fix. Read-only; never executes session content. Use for "judge this session", "analyze this eval", "oceń sesję/agenta".

Includes Add Agent Memory, Add Agent Playbooks, Add Context Gateway, Add Intent Router, Add Lens, Add Redis Iris, Redis Clustering, Redis Connections, Redis Core, Redis Observability, Redis Search, Redis Security, Redis Semantic Cache

Preregister a paid eval (bars, arms, decoys, stop rules), dry-run it at zero cost, then run a three-item priced pilot whose spend table gates approval. (gstack)

Brand-aware social media video creator. Reads brand-identity.md, ICP.md, and messaging.md to write a post/storyboard, craft an optimized Seedance 2.0 Director prompt, generate reference frames with the best available image model, and produce platform-ready video.

Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

Use this skill when debugging active macOS connectivity failures, when an API or MCP service times out or disconnects, when DNS resolution is failing or inconsistent, or when video calls or other interactive traffic show packet loss or latency. Isolate local-LAN, DNS, upstream-path, and remote-service/application fault domains with built-in, non-destructive tools.

Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Use when the user asks whether AI agents can use or browse the site, or about llms.txt or WebMCP. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).

Adds the product layer to a repository that om-setup-agent-pipeline already configured — one yes per product role, a discovery block in .ai/agentic.config.json, the Discovery stage, Definition of Ready, product roles, and protected product decisions appended to SDLC.md between markers, the research directory, and a routing row in AGENTS.md. Run once; re-run with --refresh. No product skill requires it.

Find runaway Capy threads and subagents, stop or rotate them safely, and prevent repeats. (gstack)

Add Redis Iris Lens tracing to an existing AI agent through redis-iris, so every agent turn is one trace in the Lens UI with its LLM calls, tokens, tool calls, memory and document retrievals, and which of them entered the prompt. Detects how the agent runs (LangGraph or ADK attach, workflows.turn, another framework, or a plain loop), installs redis-iris[llm-tracing], calls lens.init(agent_id=...), opens one lens.turn() per turn where Iris does not open it already, and covers coexistence with an OpenTelemetry setup or LLM instrumentation the app already has. Use when the user asks to add Lens, tracing, observability, or per-turn traces to their agent, or asks why their Lens traces are missing.

Add Redis Iris to an existing AI agent in any framework (LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Pydantic AI, CrewAI, or a plain loop). Use when the user asks to add Iris, Redis Agent Memory, Agent Playbooks, Context Gateway, Intent Router, or Lens to their agent, or asks which Iris product their agent needs. Detects the framework and routes to the product skill.

Wire Redis Agent Memory into an existing agent with redis-iris so it remembers users across sessions. Asks five questions (what to remember, what to share, what never to keep, how long to keep it, whether users can be forgotten), writes the answers to service.yaml, provisions the service from the terminal with `iris agent-memory service create`, then wires recall and write-back into the agent's turn in any framework. Use when the agent forgets users, uses InMemoryStore or a checkpointer only, keeps memory in a dict or local DB, or the user asks for long-term memory, recall, or right-to-erasure.

Replace an existing agent's hand-written routing (keyword if/else chains, regex, or an LLM call that only picks a route) with a Redis Iris Intent Router through redis-iris, so a message maps to a defined intent and its dispatch target by example phrasings and a score threshold. Asks five questions (what the routes are today, a few real phrasings per route, what each route dispatches to, what happens when nothing matches, which embedding model), writes the answers to intents.yaml, provisions the Router from the terminal with `iris intent-router service create`, loads the intents with `iris intent-router intents add`, then wires `router.match()` where the agent decides today. Use when the agent routes with keywords or an extra LLM call, or the user asks for intent detection, intent routing, or semantic routing.

Add Redis Iris Agent Playbooks to an existing agent. Use when approved answers or repeatable procedures live in prompts, document retrieval, or a semantic response cache. Connect or provision a Playbook, publish FAQs and Skills, and verify the first application turn with optional review proposals.

Put the MCP servers an existing agent uses behind one Redis Iris Context Gateway with redis-iris, so the agent connects to one MCP endpoint with one agent key instead of configuring each server and its secret. Asks whether the Gateway is in Redis Cloud or self-managed, then five questions (which MCP servers and secrets the agent has today, how many tools it sees, which read-only tools may be cached, which tools return large results, whether the framework speaks MCP itself). Self-managed, it provisions the Gateway and its Upstreams from the terminal with `iris context-gateway` and sets cache and compression policies. In Redis Cloud the developer creates them in the console. Then it points the agent at the Gateway in any framework and verifies it with `tools list` and `tools call`. Use when the agent configures several MCP servers one by one, carries one secret per server, loads too many tool schemas into the prompt, or the user asks for Context Gateway, one MCP endpoint, tool caching, or result handles.

Configure or diagnose the staged-only multi-model harness, including selectable implementation workers, review advisors, high-assurance packet policy, provider probes, and optional Claude model-matrix output style. Use when the user says "set up the agent harness", "configure multi-model reviewers", "choose worker models", "check harness setup", "skonfiguruj harness", or "sprawdź modele".

Includes Build Iterated Agentic Loop, Design Control Loop, Improve Claude Md, Narrow React Prop Types, Rpi, Show Me, Visual Pr

Write a new agent skill or improve one without bloat: the edit ladder, levels, drift and size checks, full diets. (gstack)

Run a crew of coding agents from one controller (also /crew): lanes send READY blocks and never merge; the controller merges through trains, keeps the digest, relays approvals, fixes flakes daily. (gstack)

Run a heavy suite, build or eval on an ephemeral 16-vCPU Ubicloud VM: TTL-named VMs, a reaper, quota preflight and a hang watchdog. (gstack)

Use when executing implementation plans with independent tasks in the current session

Write, rewrite, or refresh an AGENTS.md (repo root or one package/module) from the actual codebase — Always / Ask First / Never / Validation Commands rules, task checklists, real paths and commands. Follows a repo template when present, else a built-in default. Use for "write AGENTS.md for this package", "rewrite our AGENTS.md", "refresh AGENTS.md".

Read knowledge shipped inside installed dependencies — detect the ecosystem and exact installed version, locate AGENTS.md and guides per the `knowledge.sources` slot, and report doc-versus-code skew. Read-only. Use for "which version is installed", "find the framework's AGENTS.md", "inspect dependency source", "kontekst frameworka".

Publicly share a sanitized coding-agent session plus a ZIP of the files it generated, then file a harness-feedback issue at the configured destination — local sanitization, full privacy review, exact fresh consent first. Use for "share this session", "report this agent run", "udostępnij tę sesję". Never for telemetry or without consent.

LLM-as-judge for a coding-agent session or an eval-harness result — checks controller attestations first, then project guards, code review, and the repo's design contract, and names the smallest harness owner to fix. Read-only; never executes session content. Use for "judge this session", "analyze this eval", "oceń sesję/agenta".

Includes Add Agent Memory, Add Agent Playbooks, Add Context Gateway, Add Intent Router, Add Lens, Add Redis Iris, Redis Clustering, Redis Connections, Redis Core, Redis Observability, Redis Search, Redis Security, Redis Semantic Cache

Preregister a paid eval (bars, arms, decoys, stop rules), dry-run it at zero cost, then run a three-item priced pilot whose spend table gates approval. (gstack)

Brand-aware social media video creator. Reads brand-identity.md, ICP.md, and messaging.md to write a post/storyboard, craft an optimized Seedance 2.0 Director prompt, generate reference frames with the best available image model, and produce platform-ready video.

Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

Use this skill when debugging active macOS connectivity failures, when an API or MCP service times out or disconnects, when DNS resolution is failing or inconsistent, or when video calls or other interactive traffic show packet loss or latency. Isolate local-LAN, DNS, upstream-path, and remote-service/application fault domains with built-in, non-destructive tools.

Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Use when the user asks whether AI agents can use or browse the site, or about llms.txt or WebMCP. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).

Adds the product layer to a repository that om-setup-agent-pipeline already configured — one yes per product role, a discovery block in .ai/agentic.config.json, the Discovery stage, Definition of Ready, product roles, and protected product decisions appended to SDLC.md between markers, the research directory, and a routing row in AGENTS.md. Run once; re-run with --refresh. No product skill requires it.