Multi-agent adversarial-verification orchestration for high-confidence conclusions. Fan-out finders, then verify every finding through a three-prism panel (exploitability / correctness / refutation) that defaults to disbelief, gate fixes behind load-bearing proof tests that catch agents who falsely claim "done/fixed", and roll out behind a build-first exit-code guard. Use when a fan-out task must produce trustworthy results — security audit, code review, research synthesis, migration — and a single agent's self-report cannot be trusted. Composes with securing-systems (what to look for) and shipping-changes (change closed loop); orchestration engine is the Workflow tool.
npx skills add https://github.com/telagod/code-abyss --skill orchestrating-adversarial-reviews
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> 单个 agent 会谎报"已修复 / 全覆盖 / 没问题"。结论的可信度不来自"谁说的",来自"扛过几次推翻"。
> 本 skill 是编排骨架:fan-out 发现 → 三棱镜对抗验证 → 证明性 guard → 守卫式上线。
> 信级:运行时行为 / 证明测试 > 多 agent 多数裁决 > 单 agent 自报(永远 [unverified])。
| 场景 | 用 | 理由 |
|------|----|------|
| 授权安全审计 / 加固闭环 | ✅ | 首个范例,见 workflow |
| 大面积代码审查(多维度、需高可信) | ✅ | dimensions → find → 对抗验证 |
| 研究综合 / 事实核查(结论要扛得住) | ✅ | 多源 fan-out + 证伪棱镜 |
| 大规模迁移 / 重构(site 发现 + 逐项验证) | ✅ | pipeline 逐项独立 + 证明测试 |
shipping-changes 的"何时不使用")。securing-systems / analyzing-security,本 skill 不重写知识,只编排。Recon (fan-out) 每维一个 finder, 并行深读, schema 出结构化 findings
| pipeline 而非 barrier: 维度A的发现可在维度B还在找时就进验证
v
Verify (三棱镜) 每条 finding 派 N 个 verifier, 各执一镜, 默认怀疑
| 可利用性 / 正确性 / 证伪猎杀 —— 票数 >= 多数 才保留
v
Synthesize / Ship 合成定级报告; 若是修复任务 -> 证明测试 guard -> build-first 上线
对应 Workflow 工具的 pipeline(items, findStage, verifyStage)(默认无栅栏,墙钟最短)。仅当"下一阶段需全部上一阶段结果"(去重 / 早退 / 跨条比较)才用 parallel 栅栏。
confirmed 才保留。cmd | tail 会吞掉 cmd 的退出码,判码用 cmd > log 2>&1; rc=$?。> 每条护栏的细节、PoC 判据、反向证伪实操、安全审计 worked example,见 references/workflow.md。
up --build 2>&1 | tail && rm <old>——管道吞码,build 失败仍删旧件。isolation: worktree。securing-systems —— 攻防知识总路由(找什么洞)。shipping-changes —— 单上下文变更闭环脊柱。cultivating-skills —— 本 skill 的孵化器 / 安全脊柱 / 升级漏斗。> FHIR REST endpoints (Patient, Observation, Encounter, Condition, MedicationRequest), (2) Validating FHIR resources and returning proper HTTP status codes and error responses, (3) Implementing SMART on FHIR authorization and OAuth scopes, (4) Working with Bundles, transactions, batch operations, or search pagination. Covers FHIR R4 resource structures, required fields, value sets (status codes, gender, intent), coding systems (LOINC, SNOMED, RxNorm, ICD-10), and OperationOutcome error handling.
Interact with ClawDirect, a directory of social web experiences for AI agents. Use this skill to browse the directory, like entries, or add new sites. Requires ATXP authentication for MCP tool calls. Triggers: browsing agent-oriented websites, discovering social platforms for agents, liking/voting on directory entries, or submitting new agent-facing sites to ClawDirect.
Shared audit integrity framework for all AppSec agents — enforces output quality, intellectual honesty, and continuous improvement through anti-rationalization guards, self-critique loops, retry protocols, non-negotiable behaviors, self-reflection quality gates (1-10 scoring, ≥8 threshold), and a self-learning system with lesson/memory governance for security analysis agents.
Opt out of the OneCLI gateway and supply Anthropic credentials from .env instead. For users who want simple .env-based credential management without the OneCLI agent vault. Reads the API key or OAuth token from .env and injects it into the container's API requests.
Cross-product Zoom reference skill. Use after the workflow is clear when you need shared platform guidance, app-model comparisons, authentication context, scopes, marketplace considerations, or API-vs-MCP routing.
>- Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.
Hunt Session Management vulnerabilities — session fixation (no regeneration on login), insufficient invalidation on logout / password-change / email-change, predictable or low-entropy session IDs, JWT-as-session with no exp/revocation, refresh-token rotation/reuse-detection gaps, OAuth/SSO session linkage, device-bound-session (DBSC) downgrade, and cookie attribute issues (Secure/HttpOnly/SameSite/__Host-). Validate with TWO real sessions (attacker A + victim B), body-diff every 200, and OOB confirmation for theft chains. Medium to Critical (fixation→admin hijack, no-invalidation→persistent ATO).
> Use this skill when the user is doing hands-on DOCA AES-GCM work on a BlueField DPU or ConnectX NIC — configuring `doca_aes_gcm_task_encrypt` / `_task_decrypt`, querying `doca_aes_gcm_cap_*` for per-key-type (only `DOCA_AES_GCM_KEY_128` / `_256` — AES-192 not supported) and per-task support, sizing plaintext against the max-buf cap, setting source / destination mmap permissions, validating with a NIST GCMVS or RFC 5288 vector, or debugging DOCA_ERROR_* including the security-critical tag-verification-failed outcome on decrypt. Trigger even when the user does not explicitly mention "DOCA AES-GCM" or IO_FAILED", "auth tag isn't verifying", "NOT_PERMITTED on my encrypt buffer", "is AES-192-GCM on this BlueField" (no), or "encrypted record came back tampered". Refuse and route elsewhere for non-GCM AES modes (CBC / CTR / XTS — CPU OpenSSL), key management (KMS / HSM / rotation), SHA (doca-sha), or general AEAD background.
Take telagod/orchestrating-adversarial-reviews from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.