Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/docxology/template/autoresearchnpx skills add docxology/template --skill autoresearchgit clone --depth 1 https://github.com/docxology/templateWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/docxology/template/autoresearch)<a href="https://agentmods.dev/skills/docxology/template/autoresearch"><img src="https://agentmods.dev/badge/skills/docxology/template/autoresearch.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.00551 |
| Opus 5 | $0.00028 | $0.00275 |
| Sonnet 5 | $0.00011 | $0.00110 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
infrastructure-autoresearch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
AutoResearch CLI-style measurement ideas are treated the same way: adopt exact metric extraction, review-status vocabulary, baseline/noise/confidence disclosure, and append-only evidence discipline; do not add lifecycle hooks, git commit/revert ownership, or no-human autonomous loops by default.
Commands
uv run python -m infrastructure.autoresearch.cli validate --project templates/template_code_project --fail-on-issues
Public API
from infrastructure.autoresearch import (
AutoResearchConfig,
AutoResearchIssue,
AutoResearchPlan,
AutoResearchReport,
mad_confidence,
metric_unit_from_name,
build_autoresearch_plan,
load_autoresearch_config,
parse_metric_lines,
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:
enabledstricttopicquality_checksstage_gatesrequired_artifactssecurity_profile(mapping:enabled,mode,integrity_algorithm,network_policy,external_signing,threat_model_frameworks)source_manifests(list of source-manifest artifact paths)
stage_gates must use exact stage names from pipeline.yaml. The full
accepted key set is defined by _CONFIG_KEYS in
infrastructure/autoresearch/config.py.
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.
Use parse_metric_lines() only for output already produced by a trusted local
command. It accepts exact METRIC name=value lines and rejects ambiguous or
invalid metric evidence. Use mad_confidence() as a disclosure helper for
baseline/best/noise comparisons, not as an automatic publication decision.
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- __init__.py 3.8 KB runs code
- __main__.py 215 B runs code
- AGENTS.md 4.2 KB
- cli.py 5.9 KB runs code
- config.py 8.5 KB runs code
- metrics.py 3.2 KB runs code
- models.py 11 KB runs code
- orchestrator.py 8.4 KB runs code
- planner.py 3.1 KB runs code
- README.md 5.9 KB
- reports.py 7.4 KB runs code
- validation_checks.py 23 KB runs code
- validation.py 6.1 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 73 lines · 55 tokens per session scan A be0975236731
infrastructure-autoresearch is a skill published in the GitHub repository docxology/template (19 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 551 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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