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 skills add Agent-Engineer-Master/skill-engineer --skill auto-researchgit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWrote 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/agent-engineer-master/skill-engineer/auto-research)<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/auto-research"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/auto-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/auto-research"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/auto-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00142 | $0.01539 |
| Opus 5.5 | $0.00057 | $0.00616 |
| Sonnet 5.5 | $0.00028 | $0.00308 |
| Haiku 4.5 | $0.00014 | $0.00154 |
Grade C, and why
auto-research scanned grade C with 1 finding 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 1mo 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer --> How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Research
Autonomously runs a hypothesis→test→evaluate→keep/discard optimization loop on a target skill or CLAUDE.md. Three isolated sub-agents handle testing and evaluation — the main agent orchestrates and decides.
Output Contract
Produces: updated target skill file (SKILL.md or CLAUDE.md), iteration dashboard (markdown), entry appended to results.md.
Does not produce: new skills, research reports, publishing automation, or criteria for skills it has not been given.
Startup
Read references/learnings.md and summarise the 3 most relevant bullets for this run. Read the target skill file in full. If results.md exists, read the last run entry for this target.
Step 1: Criteria Design
Read references/criteria-framework.md.
Propose 3–5 candidate criteria for the target skill. For each criterion:
- State the exact binary condition (true/false only — no gradients)
- Classify: Level 1 (deterministic, script-checkable) or Level 2 (pattern/style, LLM judge required)
- Confirm it tests one variable only — split any criterion containing "and"
- Rank by expected impact
State the recommended iteration cap (5 for 1–2 criteria; 10 for 3+) and target pass rate (default 100%).
Wait for human approval. Human may approve, edit, or add criteria before the loop starts.
If any criterion cannot be expressed as a true/false binary, stop and ask the human to reformulate it before proceeding.
Step 2: Eval Harness Setup
Generate a minimum of 5 diverse test inputs appropriate for the target skill. Write to harness/test-inputs.md.
This file is read-only for the remainder of the run. The loop may never modify it.
If 10 or more inputs are generated, designate the last 20% as the holdout validation set. Note the split at the top of harness/test-inputs.md.
Run the current unmodified target skill on the training inputs 5 times. Score every output against all approved criteria. Calculate baseline pass rate per criterion and overall.
What ships with it
12 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.
- agents/eval-deterministic.md 2.0 KB
- agents/eval-judge.md 2.3 KB
- agents/test-runner.md 1.4 KB
- assets/dashboard-template.md 1.4 KB
- evals/evals.json 4.5 KB
- evals/judge.md 1.2 KB
- evals/rubric.md 2.7 KB
- harness/.gitkeep 0 B
- references/criteria-framework.md 4.4 KB
- references/edge-cases.md 233 B
- references/learnings.md 1.1 KB
- results.md 0 B
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.
- 1mo ago First seen · 143 lines · 142 tokens per session scan C 7e5fb0f3e4e2
auto-research is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (8 stars, last pushed 3mo ago), licensed MIT. It adds 142 tokens to every session and 1,539 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
loop-engineering
A guide to building reliable loops for AI agents that plan, act, check their work, and continue until a task is truly complete. It includes independent verification and a proposer-reviewer pattern in which one agent's work is checked using outside evidence.
agent-evaluation
A guide to measuring whether an AI agent works reliably on the tasks that matter. It covers repeatable test environments, program checks, human-like scoring rules, failure analysis, and experiments that separate model problems from system problems.
validate
Validate Semantica pipelines, extraction quality, graph schemas, and ontology consistency. Returns structured error/warning checklists. Uses PipelineValidator, PipelineBuilder.validatepipeline(), GraphValidator, and OntologyValidator. Sub-commands: pipeline, step, dependencies, extraction, graph, ontology, performance.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
gsd-add-tests
Generate tests for a completed phase based on UAT criteria and implementation.