claude

agent-step-toolkit

Build agent-step LangGraph agents — a ReAct agent fronted by one batching tool over a deterministic flow-controller runner (vendored and versioned; agent-step 3.0.1). Bootstrap a project (runner library + streaming CLI + graph/state/prompt scaffold + the required local sandbox service), then add domain tools declaratively: each action declares its params, prereqs and verdict ROWS — summary, doctrine, state write, effects, one authority per outcome — and its executor just names the verdict. The engine owns the machinery and the model-facing language it enforces: composed action descriptions, per-gate reply contracts, the spliceable turn-protocol prompt fragment, and engine-rendered read-backs and standing asks the model speaks verbatim. Mechanisms are configuration, not prompt prose — confirmation propose/execute gates with attempt budgets and stored-byte repetition, OTP and double-entry match gates, multi-turn flows, paginated reads, escalation ladders that count the prompt's "once" rules and escalate themselves, capture-bounce bounds, an engine-enforced abort policy, typed handoff routes, and a state-resolved handback envelope with orchestrator/specialized agent roles plus off-topic delegation. Graph state is one Zod schema (reducers + invoke-boundary validation, channel-completeness enforced at construction). Also: caller-capture param primitives, the twelve measured host-architecture design principles, porting an existing project onto agent-step, Kafka run-tracing observability via the companion kafka-observability plugin, the three-layer test methodology (runner unit / sandbox / prompt-input), and a channel-middleware contract audit.

3 on GitHubby Charalampos Kyfonidisv0.26.1MITupdated 2 months agodevelopment
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Plugin agent-step-toolkit from marketplace ckifonidis-marketplace in ckifonidis/claude-marketplace. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (4)

  • audit-middleware-contract-compliance

    Audit a channel middleware implementation — the proxy service that invokes a generated agent's LangGraph API, forwards reply tokens to the client channel, and routes handoffs — against the agent-step wire contract. Walks the middleware's source against the contract's adherence checklist (invoke shap

  • create-tool

    Bootstraps a new agent-step LangGraph project OR adds a tool to an existing one. For bootstrap, scaffolds the full project (package.json, tsconfig, graph.ts, state.ts, prompt.ts, CLI, Dockerfile, build_and_push.sh, the agent-step library) and establishes the required root sandbox/ service (reused fr

  • pull-library

    Upgrade THIS project's vendored agent-step runner library to the newest version shipped by the agent-step-toolkit plugin. Run inside a downstream project (one that bootstrapped from the toolkit and vendored src/agent-step/) after the plugin has been updated. Compares the project's src/agent-step/VER

  • test-agent-step

    The testing methodology for an agent-step action — three layers, three questions. Runner unit tests prove the flow controller's mechanics (no backend, no LLM). Sandbox/tool tests prove the runner + executors behave correctly given hand-crafted step batches against a real local sandbox (no LLM). Prom

View source on GitHub

More from ckifonidis/claude-marketplace

All 3 plugins →

Run and debug LangGraph.js conversations: /run-langgraph-conversation executes a single- or multi-turn test conversation against the local dev server — finding the running instance that serves the project on any port before starting one — and captures the thread, with an opt-in raw SSE streaming capture (token/handoff/custom event order); /follow-langgraph-conversation investigates any thread end-to-end: locates the instance that OWNS the thread, inventories the available monitoring sources (dev-server checkpoint history, LangSmith traces incl. EU/self-hosted endpoints, process/container logs), reads handoff signals and step-batch tool envelopes, and produces root-cause analysis. Discovers ports, owning instances, graph ids, and LangSmith credentials/endpoint from the project's own config and the live environment (langgraph.json, dev script, docker-compose, .env, running processes/containers).

2 skills

3

LangSmith-parity observability over Kafka for any LangGraph.js / LangChain.js agent: /add-kafka-observability installs or upgrades a vendored src/observability/ library that attaches a BaseTracer subclass globally through two redundant paths — the configure hook plus the configure-slot LangSmith's own tracer occupies, so it attaches even on platform runtimes that sever async-context ancestry (one startup() call, zero per-node instrumentation) — and publishes EVERY traced run — graph invocation, LangGraph nodes, LLM calls with full prompts/outputs/token usage, tool runs, errors, trace hierarchy, plus opt-in backend HTTP call runs via the traceBackendCall helper — as start/end events to a Kafka topic (standard envelope keyed by event id, thread_id correlation, zod-validated, secret-redacted, 512KB-truncated) over a bounded non-blocking fire-and-forget producer (librdkafka, acks=all, idempotent, bounded shutdown). By default EVERY run is emitted (full LangSmith parity); apps that have payload-verified which of their runs duplicate root/llm/tool content can opt into run filtering (KAFKA_RUN_FILTER_MODE=allow|deny + run_type:name globs, the name side also matching metadata.langgraph_node; the root run always survives — it carries the full invocation input/final state and is the turn-boundary marker for timeline consumers). Disabled by default (KAFKA_ENABLED); runs alongside LangSmith for parallel validation so LangSmith can be unplugged with no code change. Conversation content (prompts, completions, graph state, tool params) is masked by a policy the APPLICATION supplies — startup({ contentMask }) plus digit/deep-walk primitives that compose with a domain masker the project already owns, covering both numeral runs and digits SPOKEN as words (Greek + English packs, inflection/accent-tolerant — how PINs and OTPs actually arrive on a voice channel); the library ships the SEAM, not the policy (which digits are a tax id and which an order reference is a fact about the app's

1 skill

3

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