Name: jbvc/json-to-llm-context
Rating: 68 (1 reviews)
Author: jbvc

jbvc/json-to-llm-context

Turn JSON or PostgreSQL jsonb payloads into compact readable context for LLMs. Use when a user wants to compress JSON, reduce token usage, summarize API responses, or convert structured data into model-friendly text without dumping raw paths.

Quality

85%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Quality

Content

72%

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

This is a solid, well-structured skill with strong actionability through concrete CLI examples and good progressive disclosure. The main weaknesses are minor verbosity and a workflow that lacks explicit validation checkpoints—the failure handling exists but isn't woven into the step sequence as concrete gates. Overall it's a high-quality skill that would benefit from minor tightening.

Suggestions

Integrate validation checkpoints directly into the workflow steps (e.g., 'Validate JSON with `python3 -m json.tool payload.json` before processing; if invalid, stop and report the error').

Trim unnecessary explanations like the 'Do not use this skill for' list and the Notes section to improve conciseness.

Dimension	Reasoning	Score
Conciseness	Generally efficient but includes some unnecessary explanations (e.g., 'json and PostgreSQL jsonb are treated the same once parsed' is obvious, and the 'Do not use this skill for' list is somewhat unnecessary). The output examples and CLI flags are well-presented but the overall content could be tightened by ~20%.	2 / 3
Actionability	Provides fully executable bash commands with concrete flags, clear input/output examples, and specific CLI options with real usage patterns. The quick start section is copy-paste ready and the tuning examples show realistic flag combinations.	3 / 3
Workflow Clarity	The workflow section lists 5 steps with a basic sequence, but validation is weak—step 1 says 'confirm the input is valid JSON' without specifying how, and step 4's 'still feels too long' is subjective rather than having a concrete checkpoint. The failure handling section helps but isn't integrated into the workflow as explicit validation gates.	2 / 3
Progressive Disclosure	Well-structured with clear sections (Overview, Workflow, Quick Start, Output Style, Style Options, Safety Controls) and a single-level reference to 'references/rules.md' with explicit conditions for when to consult it. Content is appropriately split between inline essentials and deferred details.	3 / 3
	Total	10 / 12 Passed

Description

89%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a well-crafted description with a clear 'Use when' clause and good trigger term coverage. Its main weakness is that the specific capabilities could be more concrete—listing particular transformation actions rather than the somewhat abstract 'compact readable context'. Overall it performs well for skill selection purposes.

Suggestions

Add more specific concrete actions, e.g., 'flatten nested objects, collapse arrays, strip nulls, produce prose summaries' to improve specificity.

Dimension	Reasoning	Score
Specificity	Names the domain (JSON/PostgreSQL jsonb) and the general action (turning payloads into compact readable context), but doesn't list multiple specific concrete actions like 'flatten nested objects, strip null fields, summarize arrays'. The actions described are somewhat abstract ('compact readable context', 'model-friendly text').	2 / 3
Completeness	Clearly answers both 'what' (turn JSON/jsonb payloads into compact readable context for LLMs) and 'when' (explicit 'Use when' clause listing compress JSON, reduce token usage, summarize API responses, convert structured data).	3 / 3
Trigger Term Quality	Includes strong natural trigger terms users would actually say: 'compress JSON', 'reduce token usage', 'summarize API responses', 'structured data', 'raw paths', 'PostgreSQL jsonb', 'model-friendly text'. Good coverage of variations a user might use.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear niche: JSON-to-LLM-context compression. The combination of JSON/jsonb, token reduction, and LLM-friendly output is distinctive and unlikely to conflict with generic JSON processing or general summarization skills.	3 / 3
	Total	11 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Reviewed

4 months ago

Table of Contents

Discovery Implementation Validation