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 bitflight-devops/hallucination-detector --skill delegategit clone --depth 1 https://github.com/bitflight-devops/hallucination-detectorWrote 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/bitflight-devops/hallucination-detector/delegate)<a href="https://agentmods.dev/skills/bitflight-devops/hallucination-detector/delegate"><img src="https://agentmods.dev/badge/skills/bitflight-devops/hallucination-detector/delegate/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/bitflight-devops/hallucination-detector/delegate"><img src="https://agentmods.dev/badge/skills/bitflight-devops/hallucination-detector/delegate.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 | $0.00060 | $0.00577 |
| Opus 5.5 | $0.00024 | $0.00231 |
| Sonnet 5.5 | $0.00012 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
delegate 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 1mo 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Delegation Template
Workflow Reference: See Multi-Agent Orchestration for complete delegation flow with DONE/BLOCKED signaling.
Step 1: Analyze the task. Do you have the "WHERE, WHAT, WHY"?
Step 2: Construct the prompt using the template below.
Template
Your ROLE_TYPE is sub-agent.
[Task Identification - one sentence]
OBSERVATIONS (Factual only):
- [Verbatim error messages]
- [Exact file:line references]
- [Environment state]
- [NO interpretations or "I think"]
DEFINITION OF SUCCESS (The "WHAT"):
- [Specific measurable outcome]
- [Acceptance criteria]
- [Verification method]
CONTEXT (The "WHERE" & "WHY"):
- Location: [Where to look]
- Scope: [Boundaries]
- Constraints: [Hard requirements vs Preferences]
AVAILABLE RESOURCES:
- [List available MCP tools]
- [Reference docs with @filepath]
YOUR TASK:
1. Run /verify (as completion criteria guide)
2. Perform comprehensive context gathering
3. Form hypothesis → Experiment → Verify
4. Implement solution
5. Only report completion after /verify criteria are met
Delegation Rules
Check before sending:
| Rule | Check |
|---|---|
| Formula | Delegation = Observations + Success Criteria + Resources - Assumptions - Micromanagement |
| No HOW | Do NOT tell agent how to implement (e.g., "Change line 42 to X") |
| Constraints OK | DO tell agent constraints (e.g., "Must use the 'requests' library") |
| No Assumptions | Do NOT say "The issue is probably..." |
| Full Scope | If code smell found, instruct agent to audit entire pattern, not single instance |
Quick Checklist
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.
- 1mo ago First seen · 79 lines · 60 tokens per session scan A 84c5b6d9971d
delegate is a skill published in the GitHub repository bitflight-devops/hallucination-detector (8 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 577 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
implementing-llm-guardrails-for-security
Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII detection and content…
deep-code-review
Use when reviewing current branch for bugs before pushing or merging, when wanting a thorough multi-agent review of local changes, or when preparing work for human review.
compare
Compare multiple alternatives using explicit criteria, weighted scoring, and tradeoff analysis. Use when choosing between options, evaluating alternatives, or making decisions.
critique
Find failure modes, edge cases, ambiguities, and exploit paths in plans, code, or designs. Use when reviewing proposals, auditing security, stress-testing logic, or validating assumptions.
explain
Produce clear reasoning with assumptions, causal chains, and evidence. Use when clarifying decisions, teaching concepts, justifying recommendations, or documenting rationale.
integrate
Combine heterogeneous data sources into a unified model with conflict resolution, schema alignment, and provenance tracking. Use when merging data from multiple systems, consolidating information, or building comprehensive views.