AdversarialScanner

AdversarialScanner is an agent for Claude Code from attilaszasz/sdd-pilot. It costs 29 tokens per session (1,159 once invoked), scanned A, original, MIT.

A specification checker that looks for contradictions, impossible constraints, unclear concurrent triggers, and problems at unusual scale or boundaries. A specification describes what a feature must do and the limits it must follow.

In plain words
What is it for?
Use it to stress-test a resolved feature specification, rank discovered problems by severity, and return structured findings without changing the specification.
Why use it?
It finds requirements that cannot all be true at once before implementation begins. This reduces the chance of building a feature whose rules conflict or fail in edge cases.

Agent for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Good fit Use it to stress-test a resolved feature specification, rank discovered problems by severity, and return structured findings without changing the specification.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/attilaszasz/sdd-pilot/_adversarial-scanner
Install

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.

Clone the repo
git clone --depth 1 https://github.com/attilaszasz/sdd-pilot

Made for: Claude Code.

Wrote 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.

agentmods badge for AdversarialScanner

README.md
[![agentmods](https://agentmods.dev/badge/agents/attilaszasz/sdd-pilot/_adversarial-scanner.svg)](https://agentmods.dev/agents/attilaszasz/sdd-pilot/_adversarial-scanner)
Your own site
<a href="https://agentmods.dev/agents/attilaszasz/sdd-pilot/_adversarial-scanner"><img src="https://agentmods.dev/badge/agents/attilaszasz/sdd-pilot/_adversarial-scanner.svg" alt="Measured on agentmods" height="20"></a>
Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,159 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.01159
Opus 5 $0.00015 $0.00580
Sonnet 5 $0.00006 $0.00232
Haiku 4.5 $0.00003 $0.00116

Measured 9d ago against content hash f9c01040ae13, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

AdversarialScanner 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 9d 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.

.github/agents/_adversarial-scanner.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Task

Attack a post-clarification specification for internal consistency failures and produce a ranked list of stress-test findings.

Inputs

Specification file path and adversarial stress-test heuristics.

Execution Rules

Analyze only the resolved spec — do not modify it. Score findings by severity and blast radius. Return machine-readable output only.

Output Format

Return a single JSON block with a findings array.

  1. Read .github/skills/clarification-strategies/SKILL.md for Adversarial Stress-Test Patterns and Adversarial Scoring Protocol.
  2. Read the spec at SpecPath. Detect spec_type from frontmatter (default: product).
  3. Extract all cross-referenceable artifacts:
    • Requirement IDs (FR-###, TR-###, OR-###, RR-###) with their constraint text.
    • Success criteria (SC-###) with their measurable targets.
    • Work items (user stories US# or objectives OBJ#) with acceptance/validation/verification scenarios.
    • Scope boundaries (Included, Excluded, Edge Cases & Boundaries).
    • Constraints (non-functional requirements, technical/operational constraints, assumptions).
  4. Run four detection passes:
    • Cross-Requirement Contradiction: Pair-wise comparison of quantified constraints across all requirement and SC entries. Flag pairs whose stated bounds conflict at any scale within scope.
    • Constraint Impossibility: For each SC, verify the combined constraint set (performance, uptime, scope exclusions, deployment model) has a feasible solution given stated requirements.
    • Concurrent-Trigger Ambiguity: Identify work-item pairs sharing an actor or trigger context with no defined ordering, mutual exclusion, or conflict-resolution rule.
    • Boundary/Scale Stress: For every quantified constraint, check for 0, max, and max+1 test scenarios. For every unconstrained resource, flag the absence of a bound.
  5. Score each finding per the Adversarial Scoring Protocol:
    • Assign severity: CRITICAL, HIGH, or MEDIUM.
    • Count blast radius (number of distinct affected IDs).
    • Rank by severity × blast_radius (CRITICAL=3, HIGH=2, MEDIUM=1).
    • Cap at 5 findings. Drop lowest-ranked beyond the cap.
  6. Allocate IDs only after ranking and capping findings:
    • New findings start at HighestFindingNumber + 1 and increase monotonically.
    • Never reuse a gap or any ID in ExistingFindingIds.
    • Before returning, compare every allocated ID against ExistingFindingIds and all other returned IDs. On any collision, return the collision error below and no findings; never renumber a persisted finding.
  7. Return a single JSON block:

Read the full file on GitHub · 93 lines

Changes

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.

  1. 9d ago First seen · 93 lines · 29 tokens per session scan A f9c01040ae13

Subscribe to this mod's changes

AdversarialScanner is an agent published in the GitHub repository attilaszasz/sdd-pilot (95 stars, last pushed 3d ago), licensed MIT. It adds 29 tokens to every session and 1,159 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.

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