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semantic-compression

Re-encode verbose prose into a dense telegraphic register — punctuation as connectives, label frames, verbless assertions — without losing normativity or precision. Use when compressing system prompts, tool/function descriptions, skill bodies, or agent instructions; reducing token count or context bloat; making documentation token-efficient for LLM input; or rewriting text in compressed notation.

77

Quality

96%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

High

Do not use without reviewing

SKILL.md
Quality
Evals
Security

Quality

Content

100%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 tight, self-applying reference: lean prose, concrete frames and operators, a worked example with a rejected variant, and a numbered procedure with explicit validation and stop rules. Organization is clean and navigable with no nested references.

DimensionReasoningScore

Conciseness

The body is written in the same dense telegraphic register it teaches; nearly every line is load-bearing with minimal padding, matching the 'lean and efficient; every token earns its place' anchor.

5 / 5

Actionability

Provides a concrete frame table (English→compressed), explicit operator semantics, a worked example with a rejected over-compressed variant, and exact token-cost measurements, giving fully executable guidance.

5 / 5

Workflow Clarity

Numbered Procedure (0–7) with an explicit Density Gate stop-rule and a Verification section containing 'declare losses', 'ambiguity scan', 'measure the pair', and a 'Stop rule' — explicit validation steps with feedback loops.

5 / 5

Progressive Disclosure

A self-contained, well-sectioned SKILL.md (no bundle files present) with clean header navigation and one clearly-signaled, non-nested out-of-tree reference to the runtime contract, matching the 'clear overview with well-signaled one-level-deep references; easy navigation' anchor.

5 / 5

Total

20

/

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.

The description is third-person, concrete, and provides an explicit 'Use when…' trigger clause covering multiple synonyms. It is comprehensive in both capabilities and trigger coverage, with only minor synonym gaps.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Re-encode verbose prose', 'punctuation as connectives, label frames, verbless assertions') plus comprehensive compression targets, matching the 'multiple specific concrete actions; comprehensive coverage' anchor.

5 / 5

Completeness

Explicitly answers 'what' (re-encode into a telegraphic register) and 'when' via a 'Use when…' clause with concrete trigger phrases, matching the 'clearly and explicitly answers both what AND when' anchor.

5 / 5

Trigger Term Quality

Includes natural user phrases like 'compressing system prompts', 'reducing token count', 'context bloat', and 'token-efficient', but a few common natural synonyms ('shorten prompt', 'summarize to save tokens') are absent, matching the 'good coverage; a few natural terms missing' anchor.

4 / 5

Distinctiveness Conflict Risk

The 'semantic compression / telegraphic register' niche has distinct triggers (compress prompts, token-efficient docs) with minimal overlap risk against general editing skills, matching the 'clear niche with distinct triggers; minimal conflict risk' anchor.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
can1357/oh-my-pi
Reviewed

Table of Contents

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