new-project

Build a new AI agent with Olakai monitoring from scratch — project setup, SDK integration, KPI configuration, and end-to-end validation

Quality

61%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./content/olakai/skills/new-project/SKILL.md

Quality

Discovery

67%

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 on specificity and distinctiveness, clearly naming the product (Olakai) and listing concrete steps in the workflow. However, it lacks an explicit 'Use when...' clause, which is critical for Claude to know when to select this skill, and could benefit from more natural trigger terms that users would actually say.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user wants to create a new AI agent project with Olakai monitoring, or asks about setting up observability for an agent.'

Include additional natural trigger terms users might say, such as 'observability', 'monitoring setup', 'new agent project', 'instrument an agent', or 'agent telemetry'.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: project setup, SDK integration, KPI configuration, and end-to-end validation. These are distinct, actionable steps in a clear workflow.	3 / 3
Completeness	Clearly answers 'what does this do' (build a new AI agent with Olakai monitoring, covering setup through validation), but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric.	2 / 3
Trigger Term Quality	Includes some relevant terms like 'AI agent', 'Olakai', 'SDK integration', 'KPI configuration', but misses common user variations like 'observability', 'monitoring setup', 'new project', 'agent scaffolding', or 'telemetry'. Users might not naturally say 'end-to-end validation'.	2 / 3
Distinctiveness Conflict Risk	The combination of 'Olakai monitoring' with 'new AI agent from scratch' creates a very specific niche. The named product (Olakai) and the from-scratch scope make it unlikely to conflict with other skills.	3 / 3
	Total	10 / 12 Passed

Implementation

55%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill excels at actionability with complete, executable code in multiple languages and a well-structured validation workflow with explicit feedback loops. However, it is severely over-long and repetitive, explaining the same concepts multiple times and including motivational content Claude doesn't need. The lack of any progressive disclosure — everything crammed into one massive file with no external references — compounds the verbosity problem.

Suggestions

Extract the KPI Formula Reference, Task Categories Reference, and Quick Reference sections into separate referenced files (e.g., KPI_FORMULAS.md, TASK_CATEGORIES.md, QUICK_REFERENCE.md) to reduce the main file to an overview with clear navigation.

Remove the 'Why Custom KPIs Are Essential' section entirely — Claude doesn't need motivation, just instructions. Similarly, consolidate the 4+ repeated warnings about CustomDataConfig registration into a single callout.

Choose one primary language (TypeScript or Python) for inline examples and move the other to a separate file (e.g., PYTHON_EXAMPLE.md), since the implementations are nearly identical in structure.

Remove the 'What NOT to Include in customData' table or reduce it to a single sentence ('Don't duplicate fields already tracked: session ID, agent ID, user email, timestamp, request time, tokens, model, provider').

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~450+ lines with significant redundancy. The 'Why Custom KPIs Are Essential' section explains motivation Claude doesn't need. The 'What NOT to Include in customData' table, 'Understanding the customData to KPI Pipeline' section, and repeated warnings about CustomDataConfig registration are over-explained. The same concepts (e.g., 'only registered fields become KPI variables') are restated 4+ times. Both TypeScript and Python implementations are nearly identical, and the Quick Reference section largely duplicates earlier content.	1 / 3
Actionability	The skill provides fully executable CLI commands, complete TypeScript and Python code examples, REST API curl commands, and specific JSON output examples. Every step has concrete, copy-paste-ready commands and code with realistic parameter values.	3 / 3
Workflow Clarity	The 5-step workflow is clearly sequenced with explicit dependencies (e.g., 'This step MUST be completed before Step 3'). Step 4 provides a detailed validation flow with a visual flowchart showing check → fix → retry loops for each failure mode (missing customData, string instead of numeric kpiData, null values). The production checklist in Step 5 adds a final verification gate.	3 / 3
Progressive Disclosure	This is a monolithic wall of text with no references to external files despite being ~450+ lines. The KPI Formula Reference, Task Categories Reference, Quick Reference, and language-specific implementations could all be split into separate files. With no bundle files provided, there's no progressive disclosure structure at all — everything is inlined.	1 / 3
	Total	8 / 12 Passed

Validation

72%

Warnings & errors only

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

Validation — 8 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (642 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
metadata_field	'metadata' should map string keys to string values	Warning

	Total	8 / 11 Passed

Repository: andrewyng/context-hub
Commit: 04c149c

Reviewed: 20 days ago

Table of Contents

Discovery Implementation Validation

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