saga-orchestration

Implement saga patterns for distributed transactions and cross-aggregate workflows. Use this skill when implementing distributed transactions across microservices where 2PC is unavailable, designing compensating actions for failed order workflows that span inventory, payment, and shipping services, building event-driven saga coordinators for travel booking systems that must roll back hotel, flight, and car rental reservations atomically, or debugging stuck saga states in production where compensation steps never complete.

Quality

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Content

100%

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

A well-structured skill body: terse and concrete, with an executable example, clearly sequenced troubleshooting workflows with validation steps, and clean one-level-deep references to real bundle files. It assumes Claude's competence while supplying the non-obvious specifics.

Dimension	Reasoning	Score
Conciseness	The body is lean and action-oriented, assuming Claude's knowledge of sagas and microservices rather than restating fundamentals; every section earns its tokens. Not a 2 because there is no padded conceptual explanation.	3 / 3
Actionability	Provides an executable Python compensation handler plus concrete specifics (function names like `_compensate()`, event `SagaCompensationCompleted`, the `STEP_TIMEOUTS` dict pattern) rather than vague direction.	3 / 3
Workflow Clarity	Multi-step processes are clearly sequenced with explicit validation guidance — "Verify that `_compensate()` iterates from `current_step - 1` down to `0`, and add an integration test" — giving feedback loops for error recovery.	3 / 3
Progressive Disclosure	Overview in SKILL.md points cleanly to one-level-deep real reference files (`references/details.md`, `references/advanced-patterns.md`, both present) that are well-signaled and appropriately split, matching the clear-overview anchor.	3 / 3
	Total	12 / 12 Passed

Description

100%

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

A strong, third-person description that pairs concrete capabilities with explicit, naturally-phrased trigger conditions and concrete scenario examples. It clearly distinguishes the skill's niche and would trigger appropriately.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — "implement saga patterns", "designing compensating actions", "building event-driven saga coordinators", "debugging stuck saga states" — with specific domain examples, matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly answers both "what" ("Implement saga patterns for distributed transactions") and "when" ("Use this skill when implementing distributed transactions... or debugging stuck saga states"), satisfying the explicit-trigger anchor.	3 / 3
Trigger Term Quality	Natural trigger phrases like "distributed transactions across microservices", "compensating actions for failed order workflows", and "stuck saga states in production" mirror language a user would actually say; not the level below because it includes common variations rather than a single keyword.	3 / 3
Distinctiveness Conflict Risk	Sagas and distributed-transaction compensation form a clear niche with distinct triggers unlikely to overlap with unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: wshobson/agents
Commit: 5cc2549

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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