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python-observability-patterns

Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.

84

1.17x
Quality

76%

Does it follow best practices?

Impact

100%

1.17x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./data/skills-md/0xdarkmatter/claude-mods/python-observability-patterns/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%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.

The content is lean, code-rich, and broadly actionable across logging, metrics, and tracing. Weaknesses are minor missing imports that hurt copy-paste readiness, the absence of any sequenced workflow with validation, and references to bundle files that do not exist.

Suggestions

Add the missing imports (logging, time) and a one-line `app = FastAPI()` so the structlog and metrics snippets run as-is.

Create the referenced ./references/structured-logging.md, ./references/metrics.md, ./references/tracing.md, and ./assets/logging-config.py, or remove the dangling references from the body.

Add a short end-to-end "Setup" sequence (configure logging -> register middleware -> expose /metrics -> export traces) with a verification step so the patterns form a checkable workflow.

DimensionReasoningScore

Conciseness

The body is lean and code-first with brief section headers ("Configure structlog", "Usage", "Define metrics") and no prose explaining what observability or the libraries are. Matches anchor 5 (lean, assumes Claude's competence, every token earns its place); the only mild redundancy is the See Also block echoing frontmatter relations, which is not enough to drop it.

5 / 5

Actionability

It gives concrete, mostly copy-pasteable code for structlog, context propagation, Prometheus, and OpenTelemetry plus a metric-type quick reference. Minor gaps hold it at anchor 4 rather than 5: "logging" and "time" are used without imports, "app" is undefined in the middleware snippets, and validate()/charge() are stub calls.

4 / 5

Workflow Clarity

This is a catalog of independent patterns rather than a sequenced process, so there is no end-to-end workflow with validation checkpoints (the middleware snippets have an implicit flow but no verification step). Matches anchor 3 (structure present but checkpoints missing/implicit); the destructive/batch cap is not triggered since these operations are not destructive.

3 / 5

Progressive Disclosure

The body is well-structured with a Quick Reference and a clearly signaled "Additional Resources" section pointing one level deep to ./references/*.md and ./assets/logging-config.py, but those directories and files do not exist, so the references are dangling. Per the guideline to score against the actual bundle structure, the broken navigation drops it to anchor 3 from an otherwise 5-worthy design.

3 / 5

Total

15

/

20

Passed

Description

81%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.

The description has strong trigger coverage and clearly answers both what and when, with a distinct observability niche. Its main weakness is specificity: "Observability patterns" is abstract and lists no concrete actions.

Suggestions

Replace the abstract "Observability patterns" with concrete verbs, e.g. "Configure structured logging with structlog, instrument Prometheus metrics, and add OpenTelemetry tracing for Python applications."

Tighten distinctiveness by leading with the library-specific triggers (structlog, opentelemetry, prometheus) so generic terms like "logging"/"monitoring" do not collide with sibling Python skills.

DimensionReasoningScore

Specificity

The phrase "Observability patterns for Python applications" names the domain but lists no concrete actions (no verbs like configure/instrument/export); the Triggers list are topics, not capabilities. This matches anchor 2 ("Names the domain but actions are minimal or generic") and is below anchor 3, which requires 1-2 concrete actions.

2 / 5

Completeness

It states what ("Observability patterns for Python applications") and an explicit when ("Triggers on: ...") with nine concrete trigger phrases, matching anchor 5 (clearly answers both what AND when with concrete trigger phrases). The explicit "Triggers on:" clause means the missing-trigger cap of 3 does not apply.

5 / 5

Trigger Term Quality

"Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id" gives comprehensive coverage of natural user terms plus library names and synonyms (monitoring/observability, logging/structlog). Matches anchor 5 (comprehensive coverage of natural terms including synonyms); not below because the term set is broad and natural.

5 / 5

Distinctiveness Conflict Risk

Library-specific triggers (structlog, opentelemetry, prometheus) carve a clear niche, but generic terms like "logging", "metrics", "monitoring" overlap with related Python skills listed in the frontmatter. Matches anchor 4 (mostly distinct; minor overlap risk with closely related skills); not 5 because of that broad-term overlap.

4 / 5

Total

16

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
NeverSight/skills_feed
Reviewed

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