Borrowing it
Nothing to install: this file belongs to dungnotnull/urban-noise-tree-barrier-planning-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/urban-noise-tree-barrier-planning-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/urban-noise-tree-barrier-planning-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/urban-noise-tree-barrier-planning-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/urban-noise-tree-barrier-planning-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/urban-noise-tree-barrier-planning-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/urban-noise-tree-barrier-planning-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/urban-noise-tree-barrier-planning-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 | $0.01659 | $0.01659 |
| Opus 5 | $0.00830 | $0.00830 |
| Sonnet 5 | $0.00332 | $0.00332 |
| Haiku 4.5 | $0.00166 | $0.00166 |
Grade A, and why
urban-noise-tree-barrier-planning-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 11d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md - Skill 288: urban-noise-tree-barrier-planning
Skill Identity
- Skill Name:
urban-noise-tree-barrier-planning - Tagline: Urban Tree & Noise-Barrier Planning for Highways - Urban Green Noise-Barrier Planning & Arboriculture analysis & decision-support harness.
- Current Phase: Phase 6 - v2.0 Production-Grade Upgrade
- Status: PRODUCTION READY v2.0.0
- Folder: D:\972026\288-urban-noise-tree-barrier-planning\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Urban Green Noise-Barrier Planning & Arboriculture. It gathers authoritative real-time and reference data, applies recognized domain methods (CNOSSOS-EU subset + Maekawa insertion loss, vegetation attenuation, i-Tree-style co-benefits), 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.
Architecture (v2.0 - modular skill registry + CoT router)
The harness is implemented as a Python package (src/urban_noise_barrier/)
plus markdown skill files. The engine wires a SkillRegistry, ToolRegistry,
HookBus, Router, and ContextManager.
/urban-noise-tree-barrier-planning invoked
|
+- Step 0: sub-router -> intent + ordered pipeline (CoT trace)
+- Step 1: sub-gather-requirements -> structured requirements
+- Step 2: sub-evidence-collector -> tiered evidence bundle (evidence.collect)
+- Step 3: sub-core-analysis -> noise map + barrier + species + veg + co-benefits + scenarios
+- Step 4: sub-knowledge-updater -> SECOND-KNOWLEDGE-BRAIN citations + gaps
+- Step 5: sub-advisor -> risk-disclosed verdict + evidence chain
+- Step 6: sub-quality-gate -> U1-U6 + G1-G4 verify + auto-fix
Sub-Skills
| Skill | Stage | Purpose |
|---|---|---|
skills/sub-router.md |
routing | CoT intent classification + pipeline selection |
skills/sub-gather-requirements.md |
intake | Clarify object/scope/timeframe/inputs/audience/language |
skills/sub-evidence-collector.md |
evidence | Assemble + validate tiered evidence bundle |
skills/sub-core-analysis.md |
analysis | Noise map, barrier, species, vegetation, co-benefits, scenarios |
skills/sub-knowledge-updater.md |
knowledge | Query SECOND-KNOWLEDGE-BRAIN.md; surface citations + gaps |
skills/sub-advisor.md |
synthesis | Risk-disclosed verdict + evidence chain + remediation |
skills/sub-quality-gate.md |
gate | Verify U1-U6 + G1-G4; auto-fix + limitation |
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.
- 11d ago First seen · 154 lines · 1,659 tokens per session scan A 700755f21ec0
urban-noise-tree-barrier-planning-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/urban-noise-tree-barrier-planning-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,659 tokens to every session, about $0.0083 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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