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engineer-prompts-for-instant

Write or fix a prompt for a fast/cheap model (Gemini Flash, GPT-4o Mini, Haiku) so its output is stable and parseable. Use for any request to draft, tune or debug such a prompt. Not for reasoning models (use engineer-prompts-for-reasoning).

70

Quality

88%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

A highly actionable, well-structured guide: a copy-paste template, concrete techniques, a specific troubleshooting table, and a complete worked example. The main weakness is verbosity in the intro and rationale passages that explain concepts Claude already knows, plus a missing explicit verify/test loop in the workflow.

Suggestions

Trim the intro paragraph and the 'fast learners' analogy in section 1 down to a single line (e.g., 'Instant models follow examples better than written rules') to remove explanations of concepts Claude already knows.

Drop the 'Why:' rationale lines in section 3 (e.g., "This gives the model 'space' to calculate") and keep only the concrete instructions.

Add a brief final step such as 'Test the prompt on a real input; if the output is malformed, apply the fixes in section 4 and retest' to close the workflow with an explicit validation loop.

DimensionReasoningScore

Conciseness

The template, rules, troubleshooting table, and worked example earn their tokens, but there is real padding: the intro ("This guide helps you get stable, accurate results from high-speed models... fast and cost-effective"), the analogy "Instant models are like fast learners who prefer copying a pattern over reading a textbook", and "Why: This gives the model 'space' to calculate" all explain things Claude already knows. This is more than the 4 anchor's 'minor instances' of over-explanation, but the padding is not pervasive enough for the 2 anchor.

3 / 5

Actionability

The skill provides a copy-paste-ready 4-part template, concrete constraint examples ("Do not include markdown code blocks", "If data is missing, write 'N/A'"), a troubleshooting table mapping each problem to an exact fix, and a complete worked example. The bracketed placeholders are the template's intended interface, not pseudocode gaps, so this matches the 5 anchor.

5 / 5

Workflow Clarity

The sequence is clear (golden rule -> 4-part template -> techniques -> troubleshooting -> worked example) and the troubleshooting table provides problem-to-fix recovery guidance. However, there is no explicit verify/test-the-prompt step or iterate loop after applying the template, which keeps it below the 5 anchor's 'explicit validation steps; feedback loops'.

4 / 5

Progressive Disclosure

This is a single-topic skill with no bundle files, well-organized headed sections, and no content that clearly belongs in a separate reference file. Everything is appropriately placed inline and navigation via the section headers is easy, matching the spirit of the 5 anchor for self-contained skills.

5 / 5

Total

17

/

20

Passed

Description

92%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 description: concrete actions, explicit use-when triggers, named models, and an explicit exclusion that prevents conflict with the reasoning-model sibling skill. The only gap is a few common synonyms (e.g., "prompt engineering", "optimize a prompt").

DimensionReasoningScore

Specificity

"Write or fix a prompt for a fast/cheap model (Gemini Flash, GPT-4o Mini, Haiku) so its output is stable and parseable" lists multiple concrete actions (write, fix, draft, tune, debug) with a concrete goal. Coverage of the domain's actions is comprehensive, matching the 5 anchor rather than the 4 anchor's 'minor gaps'.

5 / 5

Completeness

The description explicitly answers what ("Write or fix a prompt... so its output is stable and parseable") and when ("Use for any request to draft, tune or debug such a prompt") with concrete trigger phrases. It also adds an explicit exclusion ("Not for reasoning models"), matching the 5 anchor.

5 / 5

Trigger Term Quality

Natural terms are present ("prompt", "draft", "tune", "debug", plus concrete model names Gemini Flash, GPT-4o Mini, Haiku), but common user phrasings like "prompt engineering" or "improve/optimize a prompt" are missing. This fits 'good keyword coverage; a few natural terms missing' rather than the 5 anchor's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche (prompts for fast/cheap models) and explicitly disambiguates from the sibling skill ("Not for reasoning models (use engineer-prompts-for-reasoning)"). Minimal conflict risk, matching the 5 anchor's clear distinct triggers.

5 / 5

Total

19

/

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

korchasa/flowai-plugins

Path

plugins/flowai-engineering/skills/engineer-prompts-for-instant/SKILL.md

Reviewed

Table of Contents

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