CtrlK
BlogDocsLog inGet started
Tessl Logo

ai-agents-architect

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

48

Quality

51%

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 ./skills/ai-agents-architect/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 body is a well-organized knowledge skill: clear sections, useful patterns, and a strong 'Sharp Edges' catalog with severities and fixes. It is held back by inlined concept re-explanation, absence of executable code, and lack of validation/feedback loops or external references for progressive disclosure.

Suggestions

Trim or remove the 'Why this breaks' paragraphs that re-explain concepts Claude already knows (e.g. LLM output variability), keeping only the non-obvious failure mechanics.

Add at least one concrete, executable example per major pattern (e.g. a minimal ReAct loop with max_iterations, or a tool-definition JSON schema) to move from prescriptions to copy-paste guidance.

Split the 'Sharp Edges' catalog into a referenced file (e.g. SHARP_EDGES.md) and keep a concise index in SKILL.md, improving progressive disclosure and token efficiency.

DimensionReasoningScore

Conciseness

The body is mostly bullet-listed and avoids long prose padding, but several 'Sharp Edges' entries re-explain concepts Claude already knows (e.g. that LLMs produce inconsistent output, that more tools means more confusion) with full 'Why this breaks' paragraphs, so it is efficient with some unnecessary explanation that could be tightened.

3 / 5

Actionability

It gives high-level, concrete-ish guidance (patterns, checklists of limits to set, tool-spec fields to include) but no executable code or commands — the 'fixes' are bulleted prescriptions rather than copy-paste code, matching the 'some concrete guidance but incomplete / describes rather than fully instructs' anchor.

3 / 5

Workflow Clarity

Patterns are sequenced (ReAct, Plan-and-Execute list ordered steps) and 'When to use' guidance is present, but there are no validation checkpoints or feedback loops in the workflows; however this is not a destructive/batch-operation skill, so the hard cap-at-3 rule does not force the score down — it lands at 3 for implicit/absent checkpoints.

3 / 5

Progressive Disclosure

Content is well sectioned (Expertise, Patterns, Sharp Edges, Related Skills) with no bundle files to offload detail into, but everything is inlined in one monolithic file with no one-level-deep references; structure is decent but content that could live in separate references is inline.

3 / 5

Total

12

/

20

Passed

Description

53%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 clearly identifies the agent-design niche and several capability areas, but it reads as a role statement rather than a 'what + when' trigger description. It lacks an explicit 'Use when...' clause and natural trigger phrases, capping completeness and trigger quality at the midpoint.

Suggestions

Add an explicit 'Use when...' clause naming concrete trigger situations (e.g. 'Use when the user asks to build, debug, or orchestrate autonomous AI agents, or mentions langchain, crewai, autogen, or the Claude Agent SDK').

Replace category labels with a couple of concrete actions (e.g. 'design agent loops, wire up tool/function calling, add memory and planning') to lift specificity.

Include natural synonyms and framework names users actually say so the skill surfaces for the right requests.

DimensionReasoningScore

Specificity

Names the domain ('designing and building autonomous AI agents') and lists a few concrete capabilities ('tool use, memory systems, planning strategies, and multi-agent orchestration'), but these are category labels rather than concrete actions like 'extract text', 'fill forms', so coverage is moderate, not comprehensive.

3 / 5

Completeness

Has a clear 'what' but no explicit 'Use when...' clause or trigger guidance in the description itself; per the rubric a missing 'Use when...' clause caps completeness at 3.

3 / 5

Trigger Term Quality

Includes relevant natural phrases ('AI agents', 'autonomous', 'multi-agent', 'tool use') but misses common synonyms and framework names users actually say (langchain, crewai, autogen, claude agent sdk) that appear only in the body, so coverage is partial.

3 / 5

Distinctiveness Conflict Risk

The 'autonomous AI agents / multi-agent orchestration' niche is fairly distinct from generic coding or doc skills, with only minor overlap risk against adjacent skills like mcp-builder or prompt-engineer.

4 / 5

Total

13

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
sickn33/antigravity-awesome-skills
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.