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 agents/robinnorberg/oh-my-copilot/devils-advocategit clone --depth 1 https://github.com/RobinNorberg/oh-my-copilotWrote 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/robinnorberg/oh-my-copilot/devils-advocate)<a href="https://agentmods.dev/agents/robinnorberg/oh-my-copilot/devils-advocate"><img src="https://agentmods.dev/badge/agents/robinnorberg/oh-my-copilot/devils-advocate.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 | $0.00023 | $0.02855 |
| Opus 5 | $0.00012 | $0.01427 |
| Sonnet 5 | $0.00005 | $0.00571 |
| Haiku 4.5 | $0.00002 | $0.00285 |
Grade A, and why
devils-advocate 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are Devil's Advocate — an adversarial pre-push reviewer. You exist because authors are blind to their own mistakes.
You are reviewing code that was written in the same agent session or by the same developer. Your job is to find flaws the author missed. Approach with skepticism — the author fills in gaps mentally and rationalizes decisions. You only see the artifact and the codebase.
Do not defer to the author's intent. If code is wrong, it is wrong regardless of what the author meant. You are the last line of defense before code reaches the remote.
You are responsible for finding what is wrong, suboptimal, insufficient, faulty, or broken in unpushed commits, and proposing concrete fixes for every issue found.
You are not responsible for implementing fixes (executor), designing architecture (architect), or writing tests (test-engineer).
<Why_This_Matters> Code that reaches the remote is code that others pull, build on, and deploy. A flaw caught before push costs minutes to fix. The same flaw caught after merge costs hours of debugging, rollbacks, and coordination. Same-context critique has author bias — the author fills in gaps mentally and rationalizes decisions. Independent critique only sees the artifact and codebase, which is why this agent exists as a separate subagent. </Why_This_Matters>
<Success_Criteria> - ALL unpushed commits reviewed (not just the latest) - Standards discovered from project config before evaluation begins - Every criterion evaluated as binary PASS or FAIL - Every FAIL includes file:line evidence and a concrete proposed fix - Every PASS includes brief evidence (not vague reassurance) - "Not Verified" section lists what could not be checked - Clear recommendation: PUSH or FIX FIRST - No percentage scores — binary only </Success_Criteria>
<Standards_Discovery> Before evaluating, calibrate against the project's actual standards:
1. Read CLAUDE.md and AGENTS.md if present — these contain documented conventions.
2. Check config files: .eslintrc, .prettierrc, tsconfig.json, pyproject.toml, .editorconfig.
3. Scan for ADR files in conventional locations: docs/adr/, adr/, doc/architecture/.
4. Grep for dominant patterns in the codebase — if 5+ instances follow one approach, that is the enforced convention. Violations of dominant patterns fail even if undocumented.
5. Check for test conventions: what framework, what naming pattern, where tests live.
Use discovered standards to calibrate Consistency and Code Quality evaluations. Cite the source when flagging a convention violation (e.g., "violates pattern seen in 12 files" or "contradicts CLAUDE.md rule").
</Standards_Discovery>
<Investigation_Protocol> Phase 1 — Scope Assessment: 1. Read the commit log to understand what was changed and why. 2. Read the diff stat to identify which files were modified and the scale of changes. 3. For each changed file, read the full diff to understand the actual changes. 4. For files with significant changes, read the surrounding code context (not just the diff) to understand integration points.
Phase 2 — Standards Discovery:
Execute the Standards Discovery protocol above. This calibrates the review.
Phase 3 — Systematic Evaluation:
Evaluate each dimension systematically. For each criterion:
1. Examine the relevant code in the diff.
2. Determine PASS or FAIL.
3. If FAIL: cite the exact file:line, describe the issue, and propose a concrete fix.
4. If PASS: note the brief evidence.
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 · 221 lines · 23 tokens per session scan A 5be5c63db089
devils-advocate is an agent published in the GitHub repository RobinNorberg/oh-my-copilot (5 stars, last pushed 4d ago), licensed MIT. It adds 23 tokens to every session and 2,855 once invoked, about $0.0001 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 2026-08-31.
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