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axiom-observability

Maintain MoltNet Axiom observability assets. Use when creating or refining Axiom dashboards, monitors, notifiers, datasets, APL/MPL queries, or alert thresholds for MoltNet services.

72

Quality

91%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

90%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 highly actionable and token-efficient, with real commands, executable query examples, and operational specifics that earn their place. The only weaknesses are implicit error-recovery loops in the workflows and reference-style lists inlined in SKILL.md rather than split into bundle files.

DimensionReasoningScore

Conciseness

The body is dense with operational facts Claude cannot know (dataset names, field names, metric names, apply commands) and explains no general concepts, matching anchor 5. The single redundancy is the opening line restating the frontmatter description, which is not enough to drop to anchor 4's 'several instances of over-explanation'.

5 / 5

Actionability

Copy-paste-ready commands ("AXIOM_API_TOKEN=xaat-... node infra/axiom/dashboards/apply.mjs --dry-run"), executable APL/MPL query examples, exact field and metric names, and concrete per-family monitor guidance fully match anchor 5. It is above anchor 4 because the examples cover the common dashboard, monitor, and query cases end to end.

5 / 5

Workflow Clarity

Query and dashboard workflows are clearly sequenced with explicit checkpoints (--dry-run before apply, "verify the dashboard loads and every chart uses the expected dataset", probe before widening time windows), matching anchor 4. It is not 5 because error-recovery feedback loops (e.g., what to do when dry-run or verification fails) are absent, and not 3 because validation checkpoints are explicit rather than missing.

4 / 5

Progressive Disclosure

Sections are well organized with a clear source-of-truth overview, but the skill exceeds 50 lines and inlines long reference lists (log/trace fields, metrics) that could live in a separate reference file, matching anchor 4. It is not 5 because there are no well-signaled one-level-deep references splitting the reference material, and not 3 because navigation is easy and nothing is buried.

4 / 5

Total

18

/

20

Passed

Description

87%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 names a distinct niche, states both what it does and when to use it with concrete triggers, and uses natural domain vocabulary. The only minor gaps are limited action-verb variety and a few missing synonym trigger terms.

DimensionReasoningScore

Specificity

"creating or refining Axiom dashboards, monitors, notifiers, datasets, APL/MPL queries, or alert thresholds" lists several concrete capabilities across six asset types, matching anchor 4. It is not 5 because the action verbs are limited to create/refine (no export, audit, or removal actions), and not 3 because coverage goes well beyond 1-2 actions.

4 / 5

Completeness

"Maintain MoltNet Axiom observability assets" explicitly states what the skill does, and "Use when creating or refining Axiom dashboards, monitors, notifiers, datasets, APL/MPL queries, or alert thresholds for MoltNet services" gives concrete trigger phrases for when to use it, exactly matching anchor 5.

5 / 5

Trigger Term Quality

Terms like "Axiom dashboards", "monitors", "notifiers", "alert thresholds", and "observability" are natural phrases a user would say, giving good keyword coverage. It falls short of anchor 5 because common variations such as "alerts", "SLOs", or "logs/traces/metrics" are absent.

4 / 5

Distinctiveness Conflict Risk

The niche is clear ("MoltNet Axiom observability") with distinct triggers (Axiom, APL/MPL, notifiers) unlikely to collide with other skills. This matches anchor 5's clear-niche example and is clearly above anchor 4's overlap-risk example.

5 / 5

Total

18

/

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

getlarge/themoltnet

Path

.agents/skills/axiom-observability/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.