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autoresearch

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

64

Quality

79%

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tessl review fix ./skills/vendor-ai-research/0-autoresearch-skill/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

70%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.

A well-structured orchestration skill with clear multi-loop workflows, explicit validation checkpoints, and concrete copy-paste artifacts. Its main weaknesses are conciseness (duplicated continuity blocks and repeated principles) and a broken templates/ reference on a mandatory onboarding step.

Suggestions

Remove the duplicated /loop prompt and cron JSON from the body, replacing them with a short pointer to references/agent-continuity.md, and consolidate the 'Research Discipline' principles with the sections that already state them (git protocol, inner-loop guidance) to cut redundancy.

Create the missing templates/ directory with research-state.yaml, research-log.md, and findings.md starter templates (or fix the references to point at real files), since line 58 makes it a mandatory initialization step that currently dead-ends.

Fix the dangling templates/progress-presentation.html link in references/progress-reporting.md and tighten vague fallbacks like 'reportlab, matplotlib, or similar' to one concrete default.

DimensionReasoningScore

Conciseness

The 411-line body is mostly efficient procedural content, but it duplicates the full /loop prompt and cron JSON that also appear in references/agent-continuity.md, and the 'Research Discipline' section re-states the git 'Lock before you run' rule and 'Never stop' principle already covered earlier, with some motivational padding ('Humans love seeing the upward curve').

3 / 5

Actionability

Highly actionable for an instruction skill: concrete workspace tree, git commit-message patterns, routing tables, a copy-paste /loop command and cron JSON, a trajectory JSON schema, and a sanity-check checklist; minor gaps include the broken templates/ reference and vague 'reportlab, matplotlib, or similar' fallbacks.

4 / 5

Workflow Clarity

The Bootstrap → Inner Loop → Outer Loop → Finalize flow is clearly sequenced with explicit validation checkpoints (sanity-check list, lock-before-run git protocol, baseline reproduction) and feedback loops (stuck → literature/ideation, positive/negative branches, DEEPEN/BROADEN/PIVOT/CONCLUDE criteria).

5 / 5

Progressive Disclosure

The three real references are clearly signaled and one-level-deep, but templates/ is referenced for the mandatory state-file initialization (line 58) and again in references/progress-reporting.md yet does not exist, and the agent-continuity setup is inlined in the body and duplicated in its reference rather than cleanly split.

3 / 5

Total

15

/

20

Passed

Description

88%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, specific description that concretely covers the orchestration lifecycle and pairs a clear 'what' with an explicit trigger-based 'when'. Trigger-term coverage is good but could include more common synonyms, and the broad 'starting a research project' phrasing carries minor overlap risk with adjacent skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions across the full lifecycle — 'runs rapid experiment iterations', 'synthesizes results, identifies patterns, and steers research direction', 'Routes to domain-specific skills for execution', 'produces research presentations and papers' — giving comprehensive coverage of the orchestration capability.

5 / 5

Completeness

It clearly answers 'what' (two-loop orchestration, routing, continuity, outputs) and explicitly answers 'when' with concrete trigger phrases in the 'Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort' clause.

5 / 5

Trigger Term Quality

The 'Use when' clause surfaces natural phrases ('starting a research project', 'running autonomous experiments', 'managing a multi-hypothesis research effort'), but common synonyms a user might say ('automated research', 'run experiments autonomously') are missing, so coverage is good rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

The two-loop autonomous-research-orchestration niche is largely distinct, but 'starting a research project' has minor overlap risk with the closely related ideation and paper-writing skills it explicitly routes to.

4 / 5

Total

18

/

20

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 1 missing

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

13

/

16

Passed

Repository
OpenRaiser/NanoResearch
Reviewed

Table of Contents

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