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 Hmbown/Wizards-of-the-Ghosts --skill true-seeinggit clone --depth 1 https://github.com/Hmbown/Wizards-of-the-GhostsWrote 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/hmbown/wizards-of-the-ghosts/true-seeing)<a href="https://agentmods.dev/skills/hmbown/wizards-of-the-ghosts/true-seeing"><img src="https://agentmods.dev/badge/skills/hmbown/wizards-of-the-ghosts/true-seeing/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/hmbown/wizards-of-the-ghosts/true-seeing"><img src="https://agentmods.dev/badge/skills/hmbown/wizards-of-the-ghosts/true-seeing.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 | $0.00039 | $0.00599 |
| Opus 5 | $0.00019 | $0.00300 |
| Sonnet 5 | $0.00008 | $0.00120 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
true-seeing 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
True Seeing
Pierce through abstraction, spin, and obfuscation to surface ground truth.
What This Skill Does
Use this spell when the surface presentation is misleading and you need to see what is actually there — beneath the marketing, the abstractions, the outdated docs, or the complexity. In this grimoire, True Seeing is treated as a metaphorical spell with a shipping-now delivery profile. Canonical reference input: True Seeing (spell).
When To Use
- A claim, document, or system description feels too clean and you suspect important details are hidden or glossed over.
- You need to verify what something actually does versus what it says it does.
- Abstraction layers, marketing language, or organizational complexity are obscuring the real state of affairs.
Prerequisites
- No extra runtime dependencies beyond Hermes Agent and the normal toolset for this session.
Procedure
- Restate the target, the success condition, and any no-touch boundaries before taking action.
- Identify the claim, abstraction, or presentation to examine.
- Trace through to primary sources, actual behavior, raw data, or ground-level implementation.
- Compare the surface presentation against the discovered ground truth.
- Return the discrepancies, confirmed truths, and remaining uncertainties.
- Package the result as the deliverables below, with confidence, assumptions, and unresolved risk called out explicitly.
Deliverables
- A ground-truth assessment comparing claims against evidence.
- A list of confirmed facts, identified discrepancies, and unresolvable unknowns.
- A recommendation on which claims to trust and which to investigate further.
Pitfalls / Guardrails
- Keep the metaphor anchored to a real mechanism instead of drifting into lore.
- Do not present your own interpretation as objective truth — show the evidence chain.
- Acknowledge when ground truth is genuinely unknowable rather than forcing a conclusion.
Verification
- Check that the result includes every deliverable promised above.
- Check that confirmed facts, assumptions, and inferences are visibly separated.
- Check that the metaphor still maps cleanly to a real operational mechanism.
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 · 67 lines · 39 tokens per session scan A 5d38c58298a6
true-seeing is a skill published in the GitHub repository Hmbown/Wizards-of-the-Ghosts (107 stars, last pushed 5mo ago), licensed CC0-1.0. It adds 39 tokens to every session and 599 once invoked, about $0.0002 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.
Other skills, from other repositories
agent-creator
Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use…
code-research
Use when produce structured understanding of unfamiliar codebases — architecture, data flows, dependencies, and conventions. Use when joining a new project, tracing feature implementations, or mapping system architecture.
crewai-agents
Use when crewAI multi-agent orchestration — agents, tasks, crews, tools, memory, delegation. Use when working with crewai agents.
code-agent
Use when implementing features from specs — reads requirements, writes code with tests, iterates until verification passes.
deploy-agent
Use when ship code through controlled pipeline with verification gates and rollback plans.
planning-agent
Use when decompose complex tasks into executable steps with dependencies, risk assessment, and verification criteria.