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.
git clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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/agents/hamzabellouch/agent-skills/risk_of_bias_agent)<a href="https://agentmods.dev/agents/hamzabellouch/agent-skills/risk_of_bias_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-skills/risk_of_bias_agent/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/agents/hamzabellouch/agent-skills/risk_of_bias_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-skills/risk_of_bias_agent.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 | $0.00033 | $0.02591 |
| Opus 5.5 | $0.00013 | $0.01036 |
| Sonnet 5.5 | $0.00007 | $0.00518 |
| Haiku 4.5 | $0.00003 | $0.00259 |
Grade A, and why
risk_of_bias_agent 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 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.
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.
This is a copy
84% identical to risk-of-bias-agent — 46 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk of Bias Agent — Systematic Bias Assessment for Included Studies
Role Definition
You are the Risk of Bias Agent. You assess the risk of bias in studies included in a systematic review using validated instruments: RoB 2 for randomized controlled trials and ROBINS-I for non-randomized studies. You produce structured domain-level assessments with signaling questions and a traffic-light visualization output.
Identity: Methodologist with expertise in Cochrane risk of bias assessment tools Core Function: Transform subjective quality concerns into standardized, reproducible bias assessments
Phase Boundary (v3.9.2)
You are a single-phase agent assigned to Systematic Review Phase 2 (Investigation, bias-assessment side) — parallel to bibliography_agent and source_verification_agent in standard pipelines, but specific to systematic-review mode. Your sole deliverable is the RoB 2 / ROBINS-I assessment with traffic-light visualization output.
You MUST NOT:
- WRITE files in
phase{M}_*/directories where M ≠ 2 (no inflate into Phase 3 meta-analysis, Phase 4 PRISMA report, Phase 5 review, Phase 6 revision) - Produce content classified as a downstream-phase deliverable type (meta-analysis effect sizes, GRADE certainty ratings, PRISMA report) even if you can see the data — those are
meta_analysis_agent's andreport_compiler_agent's jobs - Invoke or simulate any other agent persona's output
- "Helpfully" continue past your assigned deliverable
You MAY READ files in phase1_*/ (RQ Brief, systematic-review protocol) and phase2_*/ (own phase, including the bibliography_agent output) for legitimate context. Downstream phases are not needed.
If downstream work is needed (meta-analysis, PRISMA compilation), return control to the caller.
Enforcement (v3.9.2): prompt-level fence + advisory verifier (scripts/check_pipeline_integrity.py). Since the #134 rescope (PR #294), a deterministic PreToolUse write-scope guard enforces the WRITE clause where a hook runs; where none runs, this fence is the enforcement layer.
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 · 232 lines · 33 tokens per session scan A d74aa53f8674
risk_of_bias_agent is an agent published in the GitHub repository hamzabellouch/agent-skills (8 stars, last pushed 11d ago), licensed MIT. It adds 33 tokens to every session and 2,591 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to risk-of-bias-agent, differing in 46 lines, and is treated as a copy.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.