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skill-improvement-from-observability

Complete feedback loop from observability insights to skill updates. Use when analyzing enhanced telemetry patterns and automatically improving skills.

Quality

66%

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SecuritybySnyk

Low

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tessl review fix ./skills/skill-improvement-from-observability/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%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 strongly action-oriented — ready-to-shape LogQL, a pattern-to-skill mapping table, and a report format — with a clear six-step loop and a safety gate for auto-applied changes. Its weaknesses are repetition (the same error-rate queries recur across four sections), the absence of validation/regression steps in the workflow despite the batch nature of skill modification, and a monolithic single-file layout that should split its query catalog into references.

Suggestions

Add an explicit validation checkpoint to the workflow between recommendation and deployment (e.g., step 5.5: run skill-tester regression checks and review-multi review; only auto-apply changes classified safe, route the rest to human review) — currently validation skills are listed but never sequenced.

Consolidate the error-rate/efficiency queries that appear in §3, §6, Key Queries, and Effectiveness Metrics into a single section to remove the quadruple repetition.

Move the query catalog and the three example improvement flows into references/ files (e.g., references/queries.md, references/example-flows.md) so SKILL.md stays a lean overview, and fix the non-executable LogQL fragments (boolean label filters, stream-selector labels that only exist post-JSON parsing).

DimensionReasoningScore

Conciseness

The body is query-dense and assumes Claude knows Loki/LogQL with no concept padding, but near-identical error-rate queries appear four times (§3 Error Analysis, §6 Track Effectiveness, Key Queries → High-Impact Errors, Effectiveness Metrics) and the three ASCII "Example Improvement Flows" restate the workflow decoratively. This is 'mostly efficient but could be tightened' — not a 2 because there is no conceptual over-explanation, not a 4 because the repetition exceeds minor trimming.

3 / 5

Actionability

Concrete LogQL queries ("sum by (tool, error_type) (count_over_time(...))"), a pattern→skill mapping table, a specific implementation ("Add pathlib.Path(file).exists() check"), and a JSON report format make the guidance mostly executable. Not a 5 because several queries won't run as written ("| previous_tool=\"Read\" and tool_name=\"Read\"" uses non-LogQL boolean syntax and "pattern=\"debugging\"" in a stream selector assumes a label that only exists after JSON parsing), and delegation to observability-analyzer/skill-updater lacks invocation commands. Not a 3 because most guidance is concrete and executable in form.

4 / 5

Workflow Clarity

The six numbered steps are clearly sequenced with a built-in feedback loop (§6 before/after tracking) and a safety classification gate, but the apply-and-validate portion is missing from the sequence: skill-updater appears only in example flows, and skill-tester/review-multi validation is never a workflow step. Since automatically modifying skills is a batch operation, the missing validation checkpoints cap this at 3 per the rubric guideline — not a 2 because the sequence itself is well defined with an effectiveness feedback loop.

3 / 5

Progressive Disclosure

A single 233-line file with no bundle files: the query catalog, example flows, and metrics sections are reference material inlined in SKILL.md, matching the anchor example of ~200 lines of reference content that should live in a separate file. Not a 4 because substantial catalog content belongs in a reference file; not a 2 because section structure is good and nothing is buried. The under-50-line simple-skill exception does not apply.

3 / 5

Total

13

/

20

Passed

Description

58%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 the right structure (what + explicit 'Use when') and names its niche, but its actions are generic verbs, its trigger vocabulary is thin on natural synonyms, and its 'when' clause is circular rather than situation-based. Within its own skill ecosystem it also reads as overlapping with observability-analyzer and skill-updater.

Suggestions

List the concrete operations instead of generic verbs, e.g.: "Query enhanced telemetry in Loki, detect error and inefficiency patterns, map them to skills, generate improvement recommendations, and track before/after impact."

Broaden trigger terms with natural synonyms, e.g.: "Use when the user wants to improve skills based on telemetry/observability data, reduce recurring tool errors, or analyze skill performance from monitoring data."

Replace the circular 'when' clause with user-recognizable situations (e.g., after a telemetry review, when recurring tool failures are spotted, when asked to optimize a skill based on usage data) so it does not compete with observability-analyzer for the same triggers.

DimensionReasoningScore

Specificity

The phrase "Complete feedback loop from observability insights to skill updates" names the domain, and "analyzing enhanced telemetry patterns and automatically improving skills" gives two actions — matching the anchor for a named domain with 1-2 concrete actions that are not comprehensive. It is not a 4 because it never lists the specific operations (querying Loki, detecting patterns, mapping to skills, generating recommendations, tracking impact); not a 2 because it does state actions beyond merely naming the domain.

3 / 5

Completeness

It answers both questions: what ("Complete feedback loop from observability insights to skill updates") and when ("Use when analyzing enhanced telemetry patterns and automatically improving skills"). It sits at 4 rather than 5 because the 'when' clause is circular — it restates the skill's function instead of giving concrete, user-recognizable trigger situations — and at 4 rather than 3 because 'when' is explicitly present.

4 / 5

Trigger Term Quality

Trigger terms like "observability insights", "enhanced telemetry patterns", and "skill updates" are relevant to the domain but miss common variations and synonyms a user might naturally say (monitoring, logs, metrics, skill performance). This matches the anchor for some relevant keywords with missing variations — not a 4 because coverage is narrow, not a 2 because the terms are domain-specific rather than generic.

3 / 5

Distinctiveness Conflict Risk

The skill body reveals sibling skills (observability-analyzer, observability-pattern-detector, skill-updater), and the description's "analyzing enhanced telemetry patterns" and "automatically improving skills" overlap directly with the analyzer's and updater's jobs — fitting the anchor for somewhat specific but overlapping with similar skills. Not a 4 because within that ecosystem the overlap risk is more than minor; not a 2 because the feedback-loop niche itself is specific.

3 / 5

Total

13

/

20

Passed

Validation

100%

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

fernandezbaptiste/Skrillz

Path

skills/skill-improvement-from-observability/SKILL.md

Reviewed

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