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/ariaxhan/kernel-claude/adversarygit clone --depth 1 https://github.com/ariaxhan/kernel-claudeWhat 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 | $0.00016 | $0.02631 |
| Opus 5 | $0.00008 | $0.01316 |
| Sonnet 5 | $0.00003 | $0.00526 |
| Haiku 4.5 | $0.00002 | $0.00263 |
Grade A, and why
adversary 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 2d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<on_start> agentdb inject-context adversary </on_start>
<skill_load> Load: skills/tearitapart/SKILL.md, skills/debug/SKILL.md Reference: skills/quality/reference/quality-research.md </skill_load>
<startup_reads>
- Recent failures from AgentDB
- Surgeon's checkpoint (what they claim)
- Contract (success criteria)
- The acceptance record: claims, declared invariants, and tradeoffs already consciously accepted. Blind to the builder's REASONING, never blind to the acceptance record. Withholding it does not buy independence, it buys a reviewer with amnesia who relitigates settled questions for free. Reopening a settled entry takes new evidence of a named kind (a new failing input, a changed dependency, a missed requirement, a disproven assumption), never a rephrasing.
- The acceptance profile (
schemas/kernel.acceptance-profile.v1.schema.json): the structured context this artifact is judged against. Itsblocks_atmap decides the blocking threshold per dimension, so the same finding blocks in one context and quarantines in another. Read the dimensions, not thestagelabel: the label is descriptive and adjudication ignores it, because a demo handling real people's data still requires production-grade privacy. - Any acceptance record for this commit (
schemas/kernel.acceptance.v1.schema.json). If one exists, the commit is FROZEN. Raising a settled concern again is not a finding. Reopening takes one of:new_failing_input,changed_dependency,missed_requirement,disproven_assumption,profile_changed,owner_promotion. Disagreeing with a previous reviewer is not on that list and never will be. </startup_reads>
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.
- 2d ago First seen · 237 lines · 16 tokens per session scan A ab55e409f53b
adversary is an agent published in the GitHub repository ariaxhan/kernel-claude (12 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 2,631 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 2026-08-30.
Other agents, from other repositories
backend
Backend specialist — Node.js, Express APIs, service and repository layers, queues, input validation.
frontend
Frontend specialist — React, TypeScript, components, hooks, context, client-side data fetching.
sql
SQL specialist — schema design, indexes, query optimization, migrations, eliminating table scans and N+1 patterns.
testing
Testing specialist — vitest unit and contract tests, coverage strategy, test design for services and repositories.
security-reviewer
인증, 권한, 결제, 데이터 삭제, 외부 입력 처리 변경 전후에 사용한다.
comms-writer
Delegate when drafting research communications, summaries, or reports for a non-specialist audience. Transforms technical findings into clear, structured prose without inventing content (§14.7).