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 LIDR-academy/lidr-specboot --skill adversarial-reviewgit clone --depth 1 https://github.com/LIDR-academy/lidr-specbootWrote 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/lidr-academy/lidr-specboot/adversarial-review)<a href="https://agentmods.dev/skills/lidr-academy/lidr-specboot/adversarial-review"><img src="https://agentmods.dev/badge/skills/lidr-academy/lidr-specboot/adversarial-review/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/lidr-academy/lidr-specboot/adversarial-review"><img src="https://agentmods.dev/badge/skills/lidr-academy/lidr-specboot/adversarial-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.01144 |
| Opus 5 | $0.00018 | $0.00572 |
| Sonnet 5 | $0.00007 | $0.00229 |
| Haiku 4.5 | $0.00004 | $0.00114 |
Grade A, and why
adversarial-review 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 12d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
adversarial-review Skill
Act as an independent adversarial reviewer: assume gaps, flaws, or unsafe behavior may exist until you have argued against them with evidence.
This skill is intended for the verification window of spec-driven development (after implementation, before archiving), when the human runs a different agent or session than the one that implemented the change.
Do not prescribe which agent, model, or IDE to use. That is the human's choice.
Inputs
- Optional context from user (same style as
show-spec-working):- Direct ticket id in text (for example:
SCRUM-10) - Feature or change name
- Endpoint(s)
- Frontend route(s)
- Pull request: URL, or host owner/repo and number (for example:
https://github.com/org/repo/pull/42orowner/repo#42)
- Direct ticket id in text (for example:
- If missing, infer from the current session (active change, branch, or OpenSpec folder).
Resolve scope in this order: explicit ticket or change name → PR when given → current active work.
Mindset (adversarial review)
Borrowed from common red-team / adversarial practice:
- Try to break the system, not only to confirm happy paths.
- Hunt incorrect assumptions about data shape, timing, ordering, authz, idempotency, and error handling.
- Trace cross-boundary and composition risks: pieces that look fine in isolation but fail together (multi-file, API plus UI, retries plus side effects).
- Treat the diff as incomplete context: missing tests, missing negative paths, or spec drift can hide issues.
- Calibrate depth to risk: auth, payments, PII, privilege boundaries, and data mutation deserve stricter scrutiny.
Workflow
Step 1 — Load the specification side first
- Identify the OpenSpec change directory and read the relevant artifacts (proposal, design, specs, scenarios,
tasks.md). - Extract acceptance criteria and explicit non-goals. List what must be true for "done."
- Note anything underspecified (ambiguous acceptance, missing error cases, missing security constraints).
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.
- 12d ago First seen · 116 lines · 36 tokens per session scan A 66a9666b1ed1
adversarial-review is a skill published in the GitHub repository LIDR-academy/lidr-specboot (66 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,144 once invoked, about $0.0002 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.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…