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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.

Quality

92%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

96%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.

An exemplary instructional body: lean and dense (it models its own methodology), fully actionable with tables, a worked example, and a runnable command, and sequenced with explicit validation gates and feedback loops. The only structural improvement would be splitting the reference tables and command documentation into a one-level-deep bundle file.

DimensionReasoningScore

Conciseness

The body is itself written in the dense register it teaches: every section carries non-obvious payload (measured deltas like "853 → 778 tokens (8.8%)", per-glyph token costs, a rejected over-compression example) and nothing re-explains concepts Claude already knows. Only marginal trims are possible, matching anchor 5 rather than anchor 4's "minor instances of over-explanation".

5 / 5

Actionability

Fully concrete, copy-paste-ready guidance: a numbered Procedure, a 9-row frames table mapping English to compressed forms (e.g. "'The `name` field is the stable launch identifier.' → `name: stable launch id.`"), an operators table, tiered deletion lists, a complete worked example with a rejected variant and why, and a runnable command `omp compress <file>` with flags (-o, -i, -r). This matches anchor 5; anchor 4 would require gaps in covering the common cases, and none are evident.

5 / 5

Workflow Clarity

The Procedure is explicitly sequenced (steps 0-7) with a front validation gate ("STOP. Report that it is already dense and keep the original"), and Verification adds explicit checkpoints with feedback loops: ambiguity scan with fix-and-recheck, "Under ~10% is the signal to stop, revert, and keep the original", and the stop rule "Stop deleting when the next deletion makes the reader guess". The command path adds an approve gate ("Approval before a review turn is rejected"). This matches anchor 5's explicit validation and error-recovery loops; anchor 4 would have minor validation gaps, and the destructive-capable path (-i in place) is fully gated.

5 / 5

Progressive Disclosure

A well-organized single-file skill with clear section headers, no bundle files, and good scope delegation to sibling skills ("anatomy, surface-not-machinery, what stays out → `tool-prompt-optimization`", "House style ... → `system-prompts`"). However, at ~140 lines the reference material (frames/operators tables, the `omp compress` command documentation) is inlined where a one-level-deep reference file could keep SKILL.md leaner — anchor 4's "good structure; minor organization gaps" rather than anchor 5's well-signaled reference split.

4 / 5

Total

19

/

20

Passed

Description

83%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: third-person voice, a concrete statement of what the skill does with its key mechanics, and an explicit 'Use when' clause with multiple natural trigger scenarios. The main gaps are a few missing casual synonyms (shorten/condense/trim) and modest overlap risk with general prompt-optimization skills.

Suggestions

Add common user phrasings as trigger synonyms — e.g. 'shorten', 'condense', or 'trim' a prompt or tool description — alongside the existing 'compress' / 'reduce token count' triggers.

Sharpen distinctiveness from general optimization skills by stating up front that this is lossless token-level re-encoding, not summarization or content reduction.

DimensionReasoningScore

Specificity

"Re-encode verbose prose into a dense telegraphic register" is a concrete action elaborated with specific mechanics ("punctuation as connectives, label frames, verbless assertions", "without losing normativity or precision"), placing it above anchor 3's generic 1-2 actions. It stays below anchor 5 because it describes one core capability applied to several targets rather than multiple distinct concrete actions.

4 / 5

Completeness

Explicitly answers both: a clear what ("Re-encode verbose prose into a dense telegraphic register... without losing normativity or precision") and an explicit "Use when..." clause listing four concrete trigger scenarios. This matches anchor 5 exactly; anchor 4 would require the when-clause to be less explicit or specific.

5 / 5

Trigger Term Quality

Natural phrases users would say are well covered with synonyms — "compressing system prompts", "reducing token count or context bloat", "token-efficient", "rewriting text in compressed notation" — but common casual phrasings like "shorten", "condense", or "trim this prompt" are missing, matching anchor 4 ("a few natural terms missing") rather than anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The niche — token-level re-encoding of load-bearing LLM text preserving "normativity or precision" — is distinct, but broad triggers like "making documentation token-efficient for LLM input" and "reducing token count" could overlap with general prompt-optimization or context-management skills. That is anchor 4's "minor overlap risk with closely related skills", short of anchor 5's minimal conflict risk.

4 / 5

Total

17

/

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

can1357/oh-my-pi

Path

.omp/skills/semantic-compression/SKILL.md

Reviewed

Table of Contents

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