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.
git clone --depth 1 https://github.com/attilaszasz/sdd-pilotWrote 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/attilaszasz/sdd-pilot/_adversarial-scanner)<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>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 | $0.00029 | $0.01159 |
| Opus 5 | $0.00015 | $0.00580 |
| Sonnet 5 | $0.00006 | $0.00232 |
| Haiku 4.5 | $0.00003 | $0.00116 |
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.
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.
- Read
.github/skills/clarification-strategies/SKILL.mdfor Adversarial Stress-Test Patterns and Adversarial Scoring Protocol. - Read the spec at
SpecPath. Detectspec_typefrom frontmatter (default:product). - 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 objectivesOBJ#) with acceptance/validation/verification scenarios. - Scope boundaries (Included, Excluded, Edge Cases & Boundaries).
- Constraints (non-functional requirements, technical/operational constraints, assumptions).
- Requirement IDs (
- 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.
- 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.
- Allocate IDs only after ranking and capping findings:
- New findings start at
HighestFindingNumber + 1and increase monotonically. - Never reuse a gap or any ID in
ExistingFindingIds. - Before returning, compare every allocated ID against
ExistingFindingIdsand all other returned IDs. On any collision, return the collision error below and no findings; never renumber a persisted finding.
- New findings start at
- Return a single JSON block:
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.
- 9d ago First seen · 93 lines · 29 tokens per session scan A f9c01040ae13
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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