Borrowing it
Nothing to install: this file belongs to dungnotnull/base-building-trap-defense-design-agent-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dungnotnull/base-building-trap-defense-design-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/base-building-trap-defense-design-agent-skillWrote 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/instructions/dungnotnull/base-building-trap-defense-design-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/base-building-trap-defense-design-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/base-building-trap-defense-design-agent-skill/claude-md/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/instructions/dungnotnull/base-building-trap-defense-design-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/base-building-trap-defense-design-agent-skill/claude-md.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 | $0.01910 | $0.01910 |
| Opus 5 | $0.00955 | $0.00955 |
| Sonnet 5 | $0.00382 | $0.00382 |
| Haiku 4.5 | $0.00191 | $0.00191 |
Grade A, and why
base-building-trap-defense-design-agent-skill CLAUDE.md 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — Skill 250: base-building-trap-defense-design
Skill Identity
- Skill Name:
base-building-trap-defense-design - Tagline: Trap & Defense System Design for Base-Building Games — Base-Building Game Defense & Trap System Design analysis & decision-support harness.
- Current Phase: Production Ready Enhanced (Phase 0–5 complete + Enhancement Phase complete)
- Version: 2.1.0
- Folder:
D:\972026\250-base-building-trap-defense-design\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Base-Building Game Defense & Trap System Design. It gathers authoritative real-time and reference data, applies recognized domain methods (defense-in- depth layering, chokepoint funneling, trap-synergy chains, cost-vs-coverage efficiency frontier, PvP counter-play balance), cross-references academic research, and delivers actionable outputs that are fully evidenced, risk/limitation-disclosed, and traceable to authoritative sources — continuously self-improving through an automated knowledge crawl pipeline.
Harness Flow Summary
/base-building-trap-defense-design invoked
|
+-- Pre-Flight: language detection (vi/en)
+-- Step 1: sub-gather-requirements -> normalize object/game/mode/threats/resources
+-- Step 2: sub-evidence-collector -> fetch live trap/structure stats + patch notes
+-- Step 3: sub-core-analysis -> L0-L5 layers, chokepoints, trap logic, cost/coverage, counter-play, metrics
+-- Step 4: sub-knowledge-updater -> Tier-labeled citations + crawl-gap flags
+-- Step 5: sub-advisor -> risk-disclosed verdict + evidence chain + remediation
\-- Step 6: main (quality gate) -> verify U1-U6 + G1-G4, auto-fix, deliver
Context is passed between steps via a typed Context Envelope (see
skills/main.md → Context Management) so no evidence, metric, or constraint
is lost across the pipeline.
Sub-Skills
| skills/sub-gather-requirements.md | Clarify/normalize object, game, game-mode, threats, resources, timeframe, audience, language before any data fetching. |
| skills/sub-evidence-collector.md | Fetch authoritative real-time and reference data: trap/structure stats, wikis, patch notes, recent meta; sets degradation level. |
| skills/sub-core-analysis.md | Design layered defense (L0-L5), chokepoints, trap synergy/trigger logic, cost-vs-coverage frontier, PvP counter-play, metrics. |
| skills/sub-knowledge-updater.md | Query SECOND-KNOWLEDGE-BRAIN.md; surface Tier-labeled citations; flag crawl gaps. |
| skills/sub-advisor.md | Synthesize a risk-disclosed verdict (Strong Defense / Conditional / Easily Raidable / Inconclusive) with evidence chain and remediation. |
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 · 171 lines · 1,910 tokens per session scan A b33199a1bcc6
base-building-trap-defense-design-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/base-building-trap-defense-design-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,910 tokens to every session, about $0.0095 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 2026-08-31.
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