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langgraph-agentcore

Production patterns for LangGraph StateGraph workflows deployed on AWS Bedrock AgentCore. Use when building agentic AI workflows with LangGraph, deploying agents to AgentCore Runtime, adding interrupt-based human-in-the-loop review, or checkpointing workflows that run for days. Covers graph design, interrupt() HITL, checkpointing, 3-tier model routing with fallback chains, confidence calibration, Cedar policy enforcement, cost-aware pipeline design, Bedrock Foundation Models and Guardrails, MCP tools via AgentCore Gateway, and observability with LangSmith and CloudWatch. Not for insurance underwriting domain rules — see underwriting.

74

Quality

93%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong, production-oriented body: the ordered workflow checklist embeds real validation gates, code examples teach non-obvious gotchas rather than basics, and detail is properly pushed to three well-structured, verified reference files navigated at section granularity. The two weaker spots are a handful of trimmable editorial sentences and two code blocks that call undefined helper functions without definitions or pointers.

Suggestions

Make the confidence-calibration and cost-tracking examples executable: either define or stub apply_business_rules / run_parallel_extractions / compute_agreement and invoke_model_with_cost_tracking, or point to where their implementations live in references/production-patterns.md.

Trim editorial sentences that restate the obvious payoff ("This dramatically reduces average cost-per-invocation", "Building an agent on AgentCore is a sequence, not a menu") — the checklist order and the cheap-checks-first diagram already convey the reasoning.

The Cedar 'How It Works' ASCII diagram duplicates what the three policy patterns and the Gateway description already show; consider cutting it or folding it into references/agentcore-deployment.md's Policy section.

DimensionReasoningScore

Conciseness

The body consistently assumes Claude's competence ("StateGraph, add_node/add_edge wiring and START/END behave exactly as the LangGraph docs describe. What is worth deciding deliberately is what goes *into* the state") and code comments carry non-obvious gotchas ("from_conn_string is a context manager — it owns the psycopg connection"; "not langgraph.constants — deprecated in v1.0"). A few sentences are editorial padding that could be trimmed ("This dramatically reduces average cost-per-invocation"; "Building an agent on AgentCore is a sequence, not a menu"), placing it at the 4 anchor ("efficient; minor instances of over-explanation") rather than 5 ("every token earns its place").

4 / 5

Actionability

The core patterns are fully executable and copy-paste ready — the interrupt/resume pair, PostgresSaver setup, confidence router, and the ModelTier/RouterConfig/get_model router — but two blocks invoke undefined helpers: estimate_confidence calls apply_business_rules, run_parallel_extractions, and compute_agreement, and cost_aware_node calls invoke_model_with_cost_tracking(...) with no definition or pointer. That is the 4 anchor ("mostly executable guidance; concrete code with minor gaps"), not 5 ("fully executable; copy-paste ready").

4 / 5

Workflow Clarity

The "New Agent Workflow" checklist gives a clearly ordered sequence with explicit validation checkpoints and feedback loops: "Run the interrupt/resume test — assert the graph stops at the gate and completes after Command(resume=...)" and "Deploy the Cedar policies in LOG_ONLY — analyse the DENY decisions before flipping to ENFORCE", ending with a canary deploy. Checkpoints are explicit and the ordering is justified, matching the 5 anchor ("clear sequence with explicit validation steps; feedback loops; checklists") rather than the 4 anchor's "minor validation gaps".

5 / 5

Progressive Disclosure

Three one-level-deep reference files exist and each opens with a Contents list; the body cites them at section granularity ("references/production-patterns.md, *Evidence Traceability*") and the Reference Files section maps every topic to its file and section ("**Runtime** when writing the deployment entrypoint or sizing sessions; **Gateway** when exposing a Lambda or REST API as a tool..."), and all cited sections were verified to exist. evals.md is correctly scoped ("Read when changing the skill, not when using it"). This matches the 5 anchor: clear overview, well-signaled one-level-deep references, easy navigation.

5 / 5

Total

18

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: it states what the skill covers with a comprehensive, specific capability list, gives an explicit "Use when..." clause with natural trigger terms and synonyms, uses third person, and closes with a negative boundary to prevent mis-triggering against a related skill. No fluff or over-claims are present.

DimensionReasoningScore

Specificity

The description lists eleven concrete capabilities ("graph design, interrupt() HITL, checkpointing, 3-tier model routing with fallback chains, confidence calibration, Cedar policy enforcement, cost-aware pipeline design, Bedrock Foundation Models and Guardrails, MCP tools via AgentCore Gateway, and observability with LangSmith and CloudWatch") and concrete actions ("building agentic AI workflows with LangGraph, deploying agents to AgentCore Runtime, adding interrupt-based human-in-the-loop review, or checkpointing workflows that run for days"). Coverage matches the skill body comprehensively, so it fits the 5 anchor rather than the 4 anchor's "minor gaps in coverage".

5 / 5

Completeness

Both questions are explicitly answered: what ("Production patterns for LangGraph StateGraph workflows deployed on AWS Bedrock AgentCore" plus the enumerated coverage list) and when ("Use when building agentic AI workflows with LangGraph, deploying agents to AgentCore Runtime, adding interrupt-based human-in-the-loop review, or checkpointing workflows that run for days") with concrete trigger phrases. It even adds a negative boundary ("Not for insurance underwriting domain rules — see underwriting"), matching the 5 anchor exactly.

5 / 5

Trigger Term Quality

Natural terms a user would say are comprehensively present: "LangGraph", "agentic AI workflows", "AgentCore Runtime", "Bedrock", "human-in-the-loop" paired with its synonym "HITL", "checkpointing", "Guardrails", "MCP tools". A score of 4 would require missing natural terms, but every plausible user phrasing for this domain (LangGraph deployment, HITL, long-running workflow checkpointing, model routing) is covered.

5 / 5

Distinctiveness Conflict Risk

"LangGraph StateGraph workflows deployed on AWS Bedrock AgentCore" carves out a clear niche with distinct triggers, and the explicit disambiguation ("Not for insurance underwriting domain rules — see underwriting") routes away from the nearest neighboring skill. Conflict risk is minimal, matching the 5 anchor ("clear niche with distinct triggers").

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (509 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

provectus/awos-recruitment

Path

registry/skills/langgraph-agentcore/SKILL.md

Reviewed

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