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dbos-python

Guide for building reliable, fault-tolerant Python applications with DBOS durable workflows. Use when adding DBOS to existing Python code, creating workflows and steps, or using queues for concurrency control.

60

Quality

71%

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tessl review fix ./skills/dbos-python/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%Scale 1-5

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

The skill provides a reasonable overview of DBOS Python patterns with executable code examples for the most critical operations (config/launch, workflow/step structure). However, it suffers from referencing rule files that don't exist in the bundle, leaving most of the promised content (queues, communication, testing, client, advanced patterns) undelivered. The workflow guidance lacks validation checkpoints and error recovery steps, which is particularly important for a fault-tolerance framework.

Suggestions

Provide the referenced rule files (references/lifecycle-config.md, references/workflow-determinism.md, references/queue-concurrency.md) or inline the essential content from them into the SKILL.md

Add validation/error recovery guidance, e.g., how to verify DBOS launched successfully, how to handle step failures, and how to debug workflow replay issues

Include a concrete example for queue usage since it's listed as HIGH priority but has no code example

Remove or condense the priority table — the prefix column adds no value without the actual rule files, and Claude doesn't need a taxonomy to follow instructions

DimensionReasoningScore

Conciseness

Generally efficient with minimal over-explanation. The priority table adds some overhead but provides useful organizational context. The 'When to Use' section is slightly verbose for Claude but not egregiously so. The description line at the top repeats the frontmatter.

4 / 5

Actionability

Provides executable Python code examples for configuration/launch and workflow/step structure. The key constraints section gives concrete do/don't rules. However, the references to detailed rule files (lifecycle-config.md, workflow-determinism.md, queue-concurrency.md) are not backed by actual bundle files, leaving significant gaps in actionable guidance for queues, communication, testing, and advanced patterns.

4 / 5

Workflow Clarity

The configuration and launch sequence is clear, and the workflow/step relationship is demonstrated. However, there are no validation checkpoints, no error recovery guidance, and no feedback loops for when things go wrong. For a framework dealing with fault-tolerant workflows, the absence of validation/verification steps is notable.

3 / 5

Progressive Disclosure

The skill references rule files in a 'references/' directory (lifecycle-config.md, workflow-determinism.md, queue-concurrency.md) but no bundle files are provided, meaning these references are broken. The priority table promises 9 categories of rules but only a fraction is covered inline. The structure suggests good intent but fails in execution since the referenced content doesn't exist.

2 / 5

Total

13

/

20

Passed

Description

82%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.

This is a solid skill description that clearly identifies its niche (DBOS durable workflows in Python) and provides explicit 'Use when' guidance with concrete trigger scenarios. The specificity of capabilities could be slightly improved by listing more concrete actions, but the description is well-structured and distinctive enough for reliable skill selection.

DimensionReasoningScore

Specificity

Names the domain (DBOS durable workflows in Python) and mentions a few concrete actions (building workflows, steps, queues, concurrency control), but doesn't comprehensively list specific capabilities like error handling patterns, retry logic, or specific API methods.

3 / 5

Completeness

Clearly answers both 'what' (building reliable, fault-tolerant Python applications with DBOS durable workflows) and 'when' (adding DBOS to existing Python code, creating workflows and steps, using queues for concurrency control) with explicit trigger phrases.

5 / 5

Trigger Term Quality

Includes good natural keywords like 'DBOS', 'durable workflows', 'Python', 'queues', 'concurrency control', 'workflows and steps'. Missing some variations users might say like 'fault tolerance', 'reliable execution', 'task queue', or 'DBOS decorator'.

4 / 5

Distinctiveness Conflict Risk

DBOS is a very specific framework, making this skill highly distinctive with minimal conflict risk. The combination of 'DBOS', 'durable workflows', and 'Python' creates a clear niche that is unlikely to overlap with other skills.

5 / 5

Total

17

/

20

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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