Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/rjmurillo/ai-agentsnpx agentmods add skills/rjmurillo/ai-agents/ai-agents-research-methodologyWrote 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/rjmurillo/ai-agents/ai-agents-research-methodology)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/ai-agents-research-methodology"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/ai-agents-research-methodology/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/rjmurillo/ai-agents/ai-agents-research-methodology"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/ai-agents-research-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 269 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
- high Agent Snooping · line 272 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
- medium Agent Snooping · line 269 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 270 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00118 | $0.04468 |
| Opus 5.5 | $0.00047 | $0.01787 |
| Sonnet 5.5 | $0.00024 | $0.00894 |
| Haiku 4.5 | $0.00012 | $0.00447 |
Grade A, and why
ai-agents-research-methodology 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 6d 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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agents Research Methodology
This repo runs on verification-based governance: labels like "MANDATORY" or "NON-NEGOTIABLE" are insufficient, every requirement needs a verification mechanism. The same standard applies to ideas. An idea is not accepted because it sounds right, because a model agreed with it, or because a retro asserted it. It is accepted when it survives the lifecycle below and leaves an inspectable artifact at every stage.
This skill is the discipline. For the specific probe recipes, use
ai-agents-empirical-probe-toolkit. For the three open research programs, use
ai-agents-research-frontier. For the archive of past settled results, use
ai-agents-failure-archaeology.
Triggers
how do I prove this idearun the idea lifecyclewhat is the evidence barturn this hunch into a result
The Evidence Bar
A result is accepted here when ONE mechanism explains ALL observations, including the negative ones, and the explanation survives adversarial refutation. Partial explanations that cover only the confirming observations are hypotheses, not results.
The cautionary tale is PR #1989. Mitigation M1 was built on a root-cause claim
inherited from a retro RCA that nobody re-verified: the RCA said pagination was
missing, but get_unresolved_review_threads.py already paginated correctly
(.project-toolkit/retrospective/2026-05-10-pr-1989-recursive-failure.md:20). Five
commits were spent building on the false premise. The mechanism-check that
would have caught it costs one file read.
Map each claim type to the adversarial machinery that already exists. Do not invent a new review ritual; route into these:
| Claim type | Adversarial mechanism | How to invoke |
|---|---|---|
| Architectural decision (ADR) | adr-review 6-agent debate (architect, critic, independent-thinker, security, analyst, high-level-advisor) until consensus, 10 rounds max |
Auto-fires on any ADR-*.md create/edit (AGENTS.md "ADR Review"); or /adr-review path |
| A single decision's reasoning | decision-critic skill |
Say Poke holes in this decision or Validate my thinking on ... |
| Contrarian read of a plan | independent-thinker or critic agent (.claude/agents/) |
Task tool with that subagent_type |
| Strategic build/buy/defer | buy-vs-build-framework (Quick tier: 1-2 hours) |
Required gate for new capabilities, see Phase 3 |
| "Why does this constraint exist" | chestertons-fence |
Before proposing removal of anything settled |
| Behavioral claim about a prompt or rule | Eval harness (ADR-057) | See Predict Numbers Before Running |
| Prose claims in the write-up | prose-self-check, doc-accuracy |
Before emitting the artifact |
What ships with it
1 file 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.
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.
- 6d ago Changed · +6 lines 7a699d8c2967
- 14d ago Changed · +1 lines 61b95bfb88f7
- 1mo ago Changed · +6 lines 34eededb58c0
- 1mo ago Changed 00b34b33803d
- 1mo ago Changed scan B → A 5d80492ba7e0
- 1mo ago Changed dff1732dddf1
- 1mo ago First seen · 282 lines · 118 tokens per session scan B 7141af7456e2
ai-agents-research-methodology is a skill published in the GitHub repository rjmurillo/ai-agents (49 stars, last pushed today), licensed MIT. It adds 118 tokens to every session and 4,468 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
install-gemini-code-review-action
Installs, configures, or upgrades Dazbo's PR code review & issue triage GitHub Action (gemini-review-action) in a target repository. Use when the user wants to set up, configure, or update automated AI code reviews, issue triaging, or Gemini model workflows for their GitHub repository.
apply-review-feedback
Classify and apply accepted PR/MR review feedback.
review-remote
Review open PR/MR and publish findings.
matlab-review-code
Review MATLAB code for quality, performance, maintainability, and adherence to MathWorks coding standards. Uses checkmatlabcode and matlabcodingguidelines. Use when reviewing code, checking style, finding code smells, assessing quality, or preparing code for handoff or publication.
azure-devops-automation
CI/CD pipelines, infrastructure as code, and deployment automation for Azure workloads.
repository-readiness-eval
Evaluate a repository's readiness for AI-assisted development across 4 axes: code understanding, dependency restore, build success, and test execution. Use when onboarding to a new repo, assessing CI readiness, or validating that an AI agent can work effectively in a codebase.