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stat-research-orchestrator

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

57

Quality

66%

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tessl review fix ./external/agents/stat_research_agent/skills/stat-research-orchestrator/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

75%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 well-structured, lean orchestration playbook with concrete sub-skill invocations, exact file-path contracts, and per-step templates, supported by explicit gating and a quality-audit checkpoint; the main gaps are the absence of runnable validation commands and unspecified FAIL-recovery feedback loops.

Suggestions

Add an explicit fix-and-retry loop for FAIL/PARTIAL statuses (e.g., 'If Step 2 status is PARTIAL/FAIL, re-invoke stat-theory-analyzer with the listed Blocking Ambiguities before proceeding').

Consider moving the per-step progress-file templates into a references/ file (e.g., PROGRESS_TEMPLATES.md) referenced from the Workflow section to reduce duplication with the 'Read:' field lists.

Make the Step 0 gate executable by specifying a concrete check (e.g., 'grep for `Target / Estimand:` and `Assumptions:` non-empty before proceeding') rather than only a prose instruction.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence (no explanations of what statistics or experiments are), using tight code blocks for paths and bullet lists of fields to read; minor trimming is possible because the Workflow 'Read:' lists and the Progress File Specification templates partially duplicate each other.

4 / 5

Actionability

Guidance is concrete and specific — exact sub-skills to invoke, exact progress file paths to wait for, exact fields to read, and full markdown templates per step — but it is instruction-only with no executable commands, and validation relies on reading PASS/FAIL status fields rather than runnable checks, leaving minor gaps.

4 / 5

Workflow Clarity

Steps 0–6 are clearly sequenced with explicit gating ('Do not proceed if the target or assumptions are undefined'), PASS/FAIL/PARTIAL status checkpoints, and a final Step 6 quality-audit checklist; minor gaps remain because explicit fix-and-retry feedback loops on FAIL are not spelled out.

4 / 5

Progressive Disclosure

Structure is clean with well-labeled sections (Overview, Full Pipeline, Workflow, Progress File Specification, Key Conventions) and no nested references; the ~125 lines of progress-file templates are inlined but legitimately define the inter-skill contract, so only minor organization gaps remain.

4 / 5

Total

16

/

20

Passed

Description

58%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 clearly conveys what the skill does and lists concrete pipeline stages, but it lacks an explicit 'Use when...' trigger clause and its keywords lean formal rather than naturally spoken, capping completeness and trigger-term quality.

Suggestions

Add an explicit 'Use when...' clause naming natural trigger phrases (e.g., 'Use when the user wants to run a statistical research study, design experiments, or compare methods with theoretical analysis').

Soften formal terms toward phrases users actually say (e.g., 'run a statistical study', 'analyze data', 'compare methods') to improve trigger-term quality.

Rephrase the stage list as concrete verb-object actions (e.g., 'formulate the problem, propose a method, analyze theory, run experiments, compare results') to lift specificity toward the comprehensive anchor.

DimensionReasoningScore

Specificity

Names the domain (statistical research pipeline) and lists several concrete stage-actions ('formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis'), but the actions are nominal stage labels rather than verb-object concrete operations, leaving minor gaps versus the comprehensive anchor.

4 / 5

Completeness

The 'what' is clear (orchestrate a multi-stage statistical research pipeline), but there is no 'Use when...' clause or equivalent explicit trigger guidance, so per the judging guidelines completeness is capped at 3 with 'when' only weakly implied.

3 / 5

Trigger Term Quality

Relevant terms appear ('statistical research', 'experiments', 'comparison', 'results') but the phrasing leans formal/technical ('formal problem formulation', 'theoretical analysis') and omits common natural variations a user would actually say, matching the 'some relevant keywords but missing variations' anchor.

3 / 5

Distinctiveness Conflict Risk

The 'statistical research pipeline centered on formal problem formulation... theory' framing carves a clear niche that is mostly distinct from generic data or coding skills, with only minor overlap risk against broad research/data-analysis skills.

4 / 5

Total

14

/

20

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

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
aiming-lab/AutoResearchClaw
Reviewed

Table of Contents

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