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ara-compiler

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

74

Quality

92%

Does it follow best practices?

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SecuritybySnyk

High

Do not use without reviewing

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

A well-structured, highly actionable skill body with a clear sequenced workflow, validation feedback loops, and clean one-level-deep references to real bundle files. Its only real weakness is conciseness — some rules and validation checks are duplicated between the inline protocol and the referenced checklist.

Suggestions

Remove the Critical Rules section's duplication of rules already embedded in the 4-stage protocol (exact numbers, no hallucination, no fake source labels), or compress it to a short pointer referencing the relevant stage.

Slim Step 5 (Validate) to the distinctive Seal-L1 entry points and defer the full per-file regex checklist to references/validation-checklist.md, which already contains it, to avoid inline repetition.

Tighten the Input Philosophy list of possible inputs and phrases like 'Think carefully about each stage' that add length without actionable specificity.

DimensionReasoningScore

Conciseness

The body is dense and largely assumes Claude's competence (no basic-concept explanations), but the Critical Rules section duplicates rules already stated in the 4-stage protocol (exact numbers, no hallucination, no fake source labels) and Step 5 validation repeats content in references/validation-checklist.md, so it could be tightened; not score 1 because it avoids padded concept explanations, not score 3 because of the redundancy.

2 / 3

Actionability

Provides concrete, copy-paste-ready guidance — exact file paths (logic/claims.md, src/configs/training.md), field-level requirements (Statement, Status, Falsification criteria ...), exact counts (>=5 concepts, >=3 experiments, >=8 nodes) and a regex-level validation checklist; an instruction-generation skill with no need for executable code, so its specificity satisfies the actionable anchor.

3 / 3

Workflow Clarity

A clear 7-step sequence (READ, REASON, GENERATE, COVERAGE CHECK loop, VALIDATE, FIX, REPORT) with explicit validation checkpoints, a max-3-rounds coverage loop, and a fix-and-re-validate feedback loop, matching the clear-sequence-with-validation anchor.

3 / 3

Progressive Disclosure

SKILL.md is an overview that signals three one-level-deep references (references/ara-schema.md, references/exploration-tree-spec.md, references/validation-checklist.md), all verified to exist as real files, with content appropriately split; not score 2 because references are clearly signaled and shallow rather than nested or buried.

3 / 3

Total

11

/

12

Passed

Description

100%

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, third-person description that states concrete capabilities, includes an explicit 'Use when ...' trigger clause, and carves out a distinct ARA-compilation niche. It fully satisfies the completeness and specificity anchors with only minor jargon.

DimensionReasoningScore

Specificity

Names multiple concrete actions and outputs — 'cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence' — rather than vague language, matching the multiple-specific-actions anchor.

3 / 3

Completeness

Explicitly answers both what ('Compiles any research input ... into a complete Agent-Native Research Artifact (ARA) ...') and when via an explicit 'Use when ingesting a paper or codebase ... building an ARA from scratch, or converting research outputs' clause, satisfying the both-what-and-when anchor.

3 / 3

Trigger Term Quality

Includes natural trigger phrasing a user would say — 'ingesting a paper or codebase', 'building an ARA from scratch', 'converting research outputs' — with good coverage of input variations (PDF papers, GitHub repos, code directories, raw notes); not score 2 because common variations are present, though some jargon ('agent-traversable') keeps it from being purely colloquial.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (ARA compilation with named layers) with distinct triggers tied to research-artifact ingestion, making conflict with other skills unlikely; not score 2 because the domain and triggers are specific rather than generic.

3 / 3

Total

12

/

12

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
Orchestra-Research/AI-Research-SKILLs
Reviewed

Table of Contents

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