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cost-anomaly-detection

Use when proactively scanning for cost anomalies, unusual spending, unexpected charges, or irregular patterns — during weekly reviews, after incidents, or when something looks off

76

1.25x
Quality

64%

Does it follow best practices?

Impact

100%

1.25x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/cost-analyst/skills/cost-anomaly-detection/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%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 skill is highly actionable with concrete API calls and runnable statistical code, and its procedure is well-sequenced. It is held back by significant verbosity and redundancy (anomaly types covered multiple times, oversized output templates) and by inlining large reference-style content rather than splitting it into the referenced bundle files.

Suggestions

Collapse the repeated anomaly-type taxonomies (Step 3 "Anomaly Types," "Common Anomaly Types," and the overlapping "Anomaly Detection Techniques"/"Advanced Techniques" sections) into a single canonical list to remove redundancy.

Move the 11-subsection Output Format template into a separate references/ file (e.g. output-format.md) and keep only a short example inline, shrinking the SKILL.md body substantially.

Add explicit validation checkpoints between detection steps — e.g. confirm the baseline period is statistically sufficient before flagging, and validate flagged anomalies against cached org context before classifying severity — so the batch workflow has real feedback loops.

DimensionReasoningScore

Conciseness

At 600+ lines the body is noticeably verbose: the anomaly-type taxonomy is repeated (Step 3 "Anomaly Types," then "Common Anomaly Types," plus overlapping "Anomaly Detection Techniques"/"Advanced Techniques"), and the 11-subsection Output Format is a large block of placeholder ($X,XXX) templates that could be trimmed.

2 / 5

Actionability

It provides concrete get_cost_data calls with real parameters and executable stdlib Python (statistics.mean/stdev, z-score, pct_change), with only minor gaps such as the pseudocode in Step 9 and placeholder lists like `baseline_costs = [...]`.

4 / 5

Workflow Clarity

Steps 1-11 are clearly sequenced, but this is a batch operation across many dimensions and the core detection workflow lacks explicit validate→fix→retry checkpoints; validation appears only implicitly via the end-stage False Positive Assessment, so the batch-operation cap holds it at 3.

3 / 5

Progressive Disclosure

Section structure is clear and a See Also block signals five reference files, but substantial content that could live in separate files (the 150-line Output Format template, the Common Anomaly Types catalog, Advanced Techniques) is inlined, and the referenced ${CLAUDE_PLUGIN_ROOT}/references/*.md files are not present in this bundle.

3 / 5

Total

12

/

20

Passed

Description

78%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 is strong: it uses a clear "Use when..." trigger clause, names the domain concretely, and includes natural keyword variations a user would actually say. Its main limitation is that the listed capabilities are synonyms of one scanning action rather than multiple distinct actions.

DimensionReasoningScore

Specificity

The description names the domain and one concrete action — "proactively scanning for cost anomalies, unusual spending, unexpected charges, or irregular patterns" — but the listed phrases are synonyms of a single scanning action rather than several distinct concrete actions, so it is not comprehensive enough for a 4.

3 / 5

Completeness

It explicitly answers both what ("proactively scanning for cost anomalies...") and when ("Use when... during weekly reviews, after incidents, or when something looks off") with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Natural user phrases like "cost anomalies," "unusual spending," "unexpected charges," and trigger contexts ("weekly reviews," "after incidents," "when something looks off") give good coverage, though a few common variations are absent so it stops short of a 5.

4 / 5

Distinctiveness Conflict Risk

"Cost anomaly detection" is a clear niche with distinct triggers, but it sits within a family of related CloudZero cost-analysis skills (it depends on understand-cloudzero-organization), creating minor overlap risk rather than minimal.

4 / 5

Total

16

/

20

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (622 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository
Cloudzero/cloudzero-claude-marketplace
Reviewed

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