CtrlK
BlogDocsLog inGet started
Tessl Logo

cli-anything-cloudanalyzer

Command-line interface for CloudAnalyzer — Agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.

60

Quality

71%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./cloudanalyzer/agent-harness/cli_anything/cloudanalyzer/skills/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 body is an exemplary executable command catalog — concrete, copy-paste-ready, and free of padding — with clear per-group organization. It falls short on workflow validation (no verification steps for batch or baseline-promotion operations) and on progressive disclosure, inlining a full CLI reference rather than splitting detailed per-group documentation into reference files.

Suggestions

Add validation checkpoints to the workflows, e.g. after '--json evaluate batch' or 'check run', instruct the agent to inspect the pass/fail or metrics fields in the JSON output and only proceed to 'baseline save'/'baseline decision' when gates pass.

Move per-group command detail (options, examples) into references/ files (e.g. evaluate.md, process.md) and keep SKILL.md as a concise overview with well-signaled one-level-deep links.

Deduplicate the 'Typical Agent Workflows' section or make each workflow show a novel end-to-end sequence (e.g. parse --json output, gate on --min-auc, then save the summary) instead of repeating commands already documented above.

DimensionReasoningScore

Conciseness

The body is a lean one-line-description-plus-example catalog for each command with no re-teaching of concepts Claude already knows. However, the 'Typical Agent Workflows' section repeats verbatim command examples already shown in the command reference (e.g. Workflow 1 duplicates the 'evaluate run' example, Workflow 2 duplicates 'check init'/'check run'), which could be trimmed.

4 / 5

Actionability

Effectively all 27 commands come with copy-paste-ready invocations using realistic arguments and concrete option flags (e.g. 'cli-anything-cloudanalyzer --json evaluate ground est_ground.pcd est_ng.pcd ref_ground.pcd ref_ng.pcd --min-f1 0.9'), covering the common cases; only the trivial 'info version' lacks an example.

5 / 5

Workflow Clarity

Four multi-step workflows are sequenced (evaluate/gate, config-driven QA, baseline management, ground QA), but none include validation or verification steps, and batch operations ('evaluate batch', 'trajectory batch') and the baseline promote/reject decision run without checking the JSON results first. Per the rubric guidelines, missing validation in batch operations caps workflow clarity at 3.

3 / 5

Progressive Disclosure

Section structure is clear (8 numbered groups with per-command headers), but the entire ~300-line, 27-command reference is inlined in SKILL.md with no bundle files; per-group command detail that belongs in references/ files (e.g. evaluate.md, process.md) is inline, matching the anchor for structure present but content that should be separate is inline.

3 / 5

Total

15

/

20

Passed

Description

70%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.

A strong, specific description that names the tool, its domain, and all 8 command groups with a concrete count (27 commands). Its main weaknesses are the complete absence of a 'Use when...' trigger clause and missing natural synonyms and file extensions that users would say when needing this skill.

Suggestions

Add an explicit trigger clause, e.g. 'Use when evaluating point clouds or SLAM/odometry trajectories (.pcd, .las files), checking ground segmentation quality, or gating mapping/localization/perception results.'

Include natural trigger synonyms and metrics users actually mention: ATE, RPE, drift, Chamfer, F1/IoU, SLAM, .pcd, .las.

Replace generic group names ('processing', 'visualization') with one concrete action each (e.g. 'voxel downsampling, outlier filtering, format conversion' and 'point cloud viewing and heatmap inspection').

DimensionReasoningScore

Specificity

The description comprehensively enumerates all 8 capability groups ('point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL'), but several entries such as 'processing' and 'visualization' are generic category names rather than concrete actions, placing it between the 4 and 5 anchors.

4 / 5

Completeness

The 'what' is clear and concrete (27 commands, 8 named groups), but there is no 'Use when...' clause or any equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guidelines.

3 / 5

Trigger Term Quality

Good natural keyword coverage including 'point cloud evaluation', 'ground segmentation', 'trajectory evaluation', and 'mapping, localization, and perception', but common variations users would actually say are missing, such as .pcd/.las file extensions, ATE, RPE, Chamfer, drift, or SLAM.

4 / 5

Distinctiveness Conflict Risk

The description targets a clearly named niche (CloudAnalyzer, a QA platform for mapping/localization/perception point cloud outputs) with distinct capability areas, giving it minimal conflict risk with other skills. Third-person voice is used correctly with no first/second person phrasing.

5 / 5

Total

16

/

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
HKUDS/CLI-Anything
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.