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 marcusgoll/Spec-Flow --skill hallucination-detectorgit clone --depth 1 https://github.com/marcusgoll/Spec-FlowWrote 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/marcusgoll/spec-flow/hallucination-detector)<a href="https://agentmods.dev/skills/marcusgoll/spec-flow/hallucination-detector"><img src="https://agentmods.dev/badge/skills/marcusgoll/spec-flow/hallucination-detector/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/marcusgoll/spec-flow/hallucination-detector"><img src="https://agentmods.dev/badge/skills/marcusgoll/spec-flow/hallucination-detector.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00083 | $0.07588 |
| Opus 5 | $0.00042 | $0.03794 |
| Sonnet 5 | $0.00017 | $0.01518 |
| Haiku 4.5 | $0.00008 | $0.00759 |
Grade A, and why
hallucination-detector 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 8d 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 — 1,184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hallucinated technical decisions destroy projects:
- Suggesting React when project uses Vue (wrong framework)
- Creating duplicate User entity when one already exists (duplicate schema)
- Recommending fake npm packages that don't exist (non-existent dependencies)
- Proposing PostgreSQL functions not available in project's version (incompatible APIs)
- Suggesting AWS services when project uses Google Cloud (wrong cloud provider)
- Inventing API endpoints that don't exist in external service (fake APIs)
This skill acts as a reality checker that:
- Loads project's tech stack from docs/project/tech-stack.md (single source of truth)
- Validates all technical suggestions against documented choices
- Verifies entities, APIs, packages exist in codebase or documentation
- Requires evidence/citations for all technical claims
- Blocks suggestions that violate documented architecture
- Corrects hallucinations with actual project technology
The result: Zero hallucinated tech decisions, implementation matches architecture, code that actually works.
<quick_start> <trigger_pattern> Auto-trigger when detecting these suggestion patterns:
Technology suggestions:
- "Use [framework/library]" → Validate against tech-stack.md
- "Install [npm package]" → Verify package exists, check compatibility
- "Import from [module]" → Verify module exists in dependencies
API/Service suggestions:
- "Call [API endpoint]" → Verify endpoint exists in API docs
- "Use [external service]" → Confirm service in tech-stack.md
- "Query [database function]" → Check function exists in DB version
Schema suggestions:
- "Create [Entity] model" → Check if entity already exists
- "Add [column] to [table]" → Verify table exists, column doesn't
- "Define [interface]" → Check for existing similar types
Pattern suggestions:
- "Follow [architecture pattern]" → Validate against system-architecture.md
- "Use [design pattern]" → Check if pattern aligns with project conventions </trigger_pattern>
<basic_workflow> Step 1: Detect technical suggestion
- AI: "Let's use Redux for state management"
- Detected: Framework suggestion (Redux)
Step 2: Load tech stack from docs/project/tech-stack.md
# State Management
- **Library**: Zustand
- **Rationale**: Simpler than Redux, less boilerplate
Step 3: Validate suggestion against tech stack
- Suggested: Redux
- Documented: Zustand
- Mismatch detected: HALLUCINATION
Step 4: Block hallucinated suggestion
🚨 HALLUCINATION DETECTED
Suggested: Redux for state management
Reality: Project uses Zustand (documented in tech-stack.md)
Reason for Zustand (from tech-stack.md):
- Simpler than Redux
- Less boilerplate
- Already integrated in project
Corrected suggestion:
Use Zustand for state management (as documented)
Evidence: docs/project/tech-stack.md, line 23
Step 5: Provide correct suggestion
- AI: "I'll use Zustand for state management (project's documented choice)" </basic_workflow>
<immediate_value> Without hallucination-detector:
AI: "Let's use Redux for state management and Axios for HTTP"
Developer: *Implements Redux + Axios*
Code review: "Why Redux? We use Zustand. Why Axios? We use fetch wrapper."
Result: Wasted 3 hours, need to refactor entire implementation
With hallucination-detector:
AI: "Let's use Redux for state management"
Detector: "🚨 HALLUCINATION: Project uses Zustand, not Redux (tech-stack.md)"
AI: "Corrected: I'll use Zustand for state management"
Developer: *Implements with Zustand correctly*
Result: Correct implementation on first try, zero refactoring
</immediate_value> </quick_start>
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.
- 8d ago First seen · 1,184 lines · 83 tokens per session scan A 5d40ed974c10
hallucination-detector is a skill published in the GitHub repository marcusgoll/Spec-Flow (93 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 7,588 once invoked, about $0.0004 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 2026-09-03.
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