python-background-jobs

Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

Quality

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

57%

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

A well-organized, code-rich skill with good progressive disclosure to a real reference file. It loses points for restating familiar concepts, a couple of non-executable code stubs, and the absence of validation feedback loops for batch operations.

Suggestions

Tighten the 'Core Concepts' section: drop or compress definitions Claude already knows (idempotency, at-least-once delivery, state machine) and keep only skill-specific guidance.

Make every code example executable: replace the `pass` placeholder in the process_order idempotency branch and complete the JobRepository SQL instead of leaving `...` and passing a dict as a single bind parameter.

Add an explicit validation/verification checkpoint (e.g. confirm job state transitions and retry/DLQ behavior) so the batch-operation workflow has a feedback loop.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and code-heavy, but the 'Core Concepts' section restates things Claude already knows (idempotency, at-least-once delivery, state machines) and a few prose passages could be tightened, so it is not fully lean.	2 / 3
Actionability	Most patterns give concrete, executable code, but Pattern 3 leaves a key branch as a bare `pass` and Pattern 4's repository SQL is an incomplete stub (`UPDATE jobs SET status = $1, ...`), so not everything is copy-paste ready.	2 / 3
Workflow Clarity	The body is a patterns catalog rather than a sequenced workflow, and for batch/background operations it offers no explicit validation checkpoints or feedback loops, which caps it at 2 per the rubric guideline.	2 / 3
Progressive Disclosure	Core patterns live in SKILL.md while advanced patterns (5–8) are clearly signaled and split one level deep into the real, verified references/details.md file, giving easy navigation.	3 / 3
	Total	9 / 12 Passed

Description

90%

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 description with an explicit 'Use when' trigger, good natural keyword coverage, and a clear, distinctive niche. Its only weakness is relying on conceptual pattern nouns where concrete actions would be sharper.

Dimension	Reasoning	Score
Specificity	Names the domain and several specific patterns ('task queues, workers, and event-driven architecture', 'decoupling work from request/response cycles'), but it leans on conceptual nouns rather than the concrete verb-noun actions the top anchor calls for, so it stops short of a 3.	2 / 3
Completeness	Explicitly answers both what ('Python background job patterns including task queues, workers...') and when ('Use when implementing async task processing, job queues...') with an explicit trigger clause.	3 / 3
Trigger Term Quality	Good coverage of natural terms a developer would say — 'background job', 'job queues', 'async task processing', 'long-running operations' — matching the top anchor for natural keyword coverage.	3 / 3
Distinctiveness Conflict Risk	Clear niche (Python background jobs/task queues) with distinct triggers that are unlikely to fire for unrelated skills.	3 / 3
	Total	11 / 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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