infrastructure-autoresearch
Skill for deterministic AutoResearch readiness planning. Use when adding, validating, or documenting opt-in autoresearch.yaml controls, stage-gate readiness, evidence-grounded claims, artifact readiness reports, or AutoResearchClaw-inspired workflow checks in template projects.
Best use case
infrastructure-autoresearch is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Skill for deterministic AutoResearch readiness planning. Use when adding, validating, or documenting opt-in autoresearch.yaml controls, stage-gate readiness, evidence-grounded claims, artifact readiness reports, or AutoResearchClaw-inspired workflow checks in template projects.
Teams using infrastructure-autoresearch should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/autoresearch/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How infrastructure-autoresearch Compares
| Feature / Agent | infrastructure-autoresearch | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Skill for deterministic AutoResearch readiness planning. Use when adding, validating, or documenting opt-in autoresearch.yaml controls, stage-gate readiness, evidence-grounded claims, artifact readiness reports, or AutoResearchClaw-inspired workflow checks in template projects.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AutoResearch Readiness
Use this module for deterministic planning and readiness validation. It adapts
reviewed AutoResearchClaw design ideas as file-backed template controls, not as
an autonomous research agent.
## Commands
```bash
uv run python -m infrastructure.autoresearch.cli validate --project template_code_project --fail-on-issues
```
## Public API
```python
from infrastructure.autoresearch import (
AutoResearchConfig,
AutoResearchIssue,
AutoResearchPlan,
AutoResearchReport,
build_autoresearch_plan,
load_autoresearch_config,
parse_string_sequence,
validate_autoresearch_plan,
write_autoresearch_report,
)
```
`validate_autoresearch_plan(..., phase="intrinsic"|"extrinsic"|"all")` splits
pre-write structure checks from post-write artifact checks.
## Configuration
Project-local `autoresearch.yaml` supports:
- `enabled`
- `strict`
- `topic`
- `quality_checks`
- `stage_gates`
- `required_artifacts`
`stage_gates` must use exact stage names from `pipeline.yaml`.
## Guardrails
Keep v1 deterministic: do not add network calls, LLM calls, generated-code
execution, or autonomous loops here. Delegate execution and validation to the
existing pipeline, project, validation, and reporting modules.