CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

prompt-engineer

Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.

47

Quality

59%

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/prompt-engineer/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

31%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 persona-style document dominated by encyclopedic bullet lists of prompting techniques Claude already knows, padded with generic filler sections. Its genuine strengths — the output format template, the 'always show the full prompt' rule, and the completion checklist — are buried in verbosity, and nothing is split into reference files.

Suggestions

Cut the 'Capabilities' and 'Knowledge Base' taxonomy lists (which merely enumerate techniques Claude already knows) down to the handful of points that encode this skill's actual methodology, or move them to a reference file linked from SKILL.md.

Replace the placeholder code block in 'Required Output Format' with one complete, worked example prompt demonstrating the expected output structure end-to-end.

Delete the empty 'Use this skill when' / 'Do not use this skill when' / 'Instructions' boilerplate sections — their content is already covered by the frontmatter description and adds no guidance.

DimensionReasoningScore

Conciseness

Roughly 200 lines enumerate techniques Claude already knows (e.g., 'Zero-shot chain-of-thought with "Let's think step by step"', 'Tree-of-thoughts for exploring multiple reasoning paths') with no added guidance, and the 'Use this skill when' / 'Instructions' sections are near-empty filler ('Working on prompt engineer tasks or workflows'). Heavily padded, though in list form rather than prose explanations, so anchor 2 fits better than 1.

2 / 5

Actionability

Almost entirely descriptive taxonomy; the only concrete elements are the 'Required Output Format' template and 'Response Approach' sequence, and the central code block is a placeholder ('[Display the complete prompt text here - this is the most important part]') with no worked example prompt anywhere. More than nothing, so above anchor 1, but it lacks the specific executable steps of anchor 3.

2 / 5

Workflow Clarity

'Response Approach' provides a clear 8-step numbered sequence and 'Before Completing Any Task' adds a checklist, but every checkpoint is self-referential with no externally verifiable validation step. Fits anchor 3 (sequence present, checkpoints missing or implicit); the destructive/batch cap does not apply to this generation skill.

3 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent) and ~200 lines of capability taxonomy — the natural content of separate reference files — are inlined monolithically in SKILL.md. Section headers provide some structure (above anchor 1), but anchor 2 ('content that clearly belongs in separate files is inlined') is the best fit.

2 / 5

Total

9

/

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 solid description with an explicit 'Use when' trigger clause naming three natural use cases and some concrete technique keywords. Its main weakness is a capability-framed, somewhat buzzword-heavy 'what' section ('LLM optimization', 'AI system design') that stops short of describing concrete deliverables.

DimensionReasoningScore

Specificity

Names concrete techniques ('Masters chain-of-thought, constitutional AI, and production prompt strategies') but frames them as capabilities ('specializing in', 'Masters') rather than concrete actions, and 'LLM optimization, and AI system design' is generic. Matches anchor 3 (domain plus 1-2 concrete actions, not comprehensive) rather than 4, which requires several specific actions with only minor gaps.

3 / 5

Completeness

Explicitly answers both what (expert prompt engineer with named techniques) and when ('Use when building AI features, improving agent performance, or crafting system prompts'). Not 5 because the what-side leans on buzzwords ('AI system design', 'LLM optimization') without concrete output descriptions; not 3 because the when-clause is explicit, not weakly implied.

4 / 5

Trigger Term Quality

'Use when building AI features, improving agent performance, or crafting system prompts' contains natural phrases users would say, plus 'prompting techniques, LLM optimization' in the what-clause. A few natural terms and synonyms ('write prompts', 'prompt optimization') are missing, fitting anchor 4 rather than the comprehensive synonym coverage of 5.

4 / 5

Distinctiveness Conflict Risk

Prompt engineering is a clear niche with distinct triggers, but 'building AI features' is broad enough to overlap with API-integration or agent-building skills. Fits anchor 4 (mostly distinct, minor overlap risk with closely related skills).

4 / 5

Total

15

/

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

rmyndharis/antigravity-skills

Path

skills/prompt-engineer/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.