CtrlK
BlogDocsLog inGet started
Tessl Logo

agent-orchestration-improve-agent

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

51

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

62%

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

The skill's workflow is its strongest asset — a clearly sequenced, validated optimization pipeline with explicit rollback gates. Its weaknesses are verbosity that re-explains well-known techniques, placeholder-heavy guidance that is not directly executable, and a monolithic structure that should be split across reference files for a document this long.

Suggestions

Trim conceptual explanations of well-known techniques (Constitutional AI, chain-of-thought, few-shot examples, A/B statistical significance / Cohen's d) to lean, actionable guidance so every token earns its place.

Replace placeholder templates ("[X%]", "$ARGUMENTS", "agent-name-v[MAJOR].[MINOR].[PATCH]") with concrete executable commands, and verify the referenced tools (context-manager, prompt-engineer, parallel-test-runner) exist and are correctly invoked.

Split the long body into one-level-deep reference files for detailed metrics, example structures, and rollback procedures, with clearly signaled links from a concise overview — this monolithic document is too long to keep inline.

DimensionReasoningScore

Conciseness

The body is verbose and explains concepts Claude already knows — "Constitutional AI Integration", "Chain-of-Thought Enhancement", few-shot rationale, statistical significance / "Cohen's d", and the closing "Remember: Agent optimization is an iterative process" — alongside an extended-thinking block of conceptual padding, though real actionable structure is interleaved.

2 / 3

Actionability

It references tools ("context-manager", "prompt-engineer", "parallel-test-runner") and command patterns, but most guidance is placeholder templates — "$ARGUMENTS", "[X%]", "[Y]", "agent-name-v[MAJOR].[MINOR].[PATCH]" — and skeleton example structures rather than fully executable, copy-paste-ready code or verified commands.

2 / 3

Workflow Clarity

A clear four-phase sequence (Analysis → Prompt Engineering → Testing → Deployment) with explicit validation gates — success criteria, rollback triggers (">10% success rate drop", ">5% critical errors"), staged rollout percentages, and "Fix and re-test before retry" feedback loops — plus checklists for the complex process.

3 / 3

Progressive Disclosure

The ~350-line body is well-organized into headed phases but is a monolithic wall of text with no bundle files and no references to separate files; detailed metrics lists and example skeletons that should be split out remain inline, so it does not qualify for the sub-50-line simple-skill exception.

2 / 3

Total

9

/

12

Passed

Description

50%

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 states the skill's purpose but is held back by abstract action categories and a complete absence of explicit "Use when..." trigger guidance. It names the domain adequately yet lacks the natural trigger terms and concrete actions that would maximize specificity and distinctiveness.

Suggestions

Add an explicit "Use when..." trigger clause (e.g., "Use when improving an existing agent's performance, diagnosing agent failures, or running agent A/B tests") to satisfy the completeness "when" requirement and raise trigger quality.

Replace abstract activity categories with concrete actions (e.g., "collect baseline metrics, classify failure modes, apply prompt-engineering techniques, run A/B tests, and stage rollouts") to improve specificity.

Include natural trigger terms a user would actually say — "improve agent performance", "optimize agent prompts", "agent regression testing" — to broaden trigger coverage and reduce overlap with related skills.

DimensionReasoningScore

Specificity

Quotes "performance analysis, prompt engineering, and continuous iteration" and "improvement of existing agents" — it names the domain and several activities, but these are abstract categories of work rather than concrete, granular actions like "extract text and tables from PDF files, fill forms, merge documents".

2 / 3

Completeness

The description answers "what" (systematic improvement of agents via analysis, prompt engineering, iteration) but the "when" is entirely missing — there is no explicit trigger clause, which caps completeness at 2 per the rubric guideline.

2 / 3

Trigger Term Quality

Terms like "improvement", "agent", "performance", and "prompt engineering" are relevant and somewhat natural, but there is no "Use when..." phrasing and coverage of common variations a user would say is limited.

2 / 3

Distinctiveness Conflict Risk

The "existing agents" improvement niche is somewhat specific, but the generic terms (improvement, performance, prompt engineering) could still overlap with general prompt-engineering or agent-creation skills.

2 / 3

Total

8

/

12

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.