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 rjmurillo/ai-agents --skill chestertons-fencegit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/chestertons-fence)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/chestertons-fence"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/chestertons-fence/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/rjmurillo/ai-agents/chestertons-fence"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/chestertons-fence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 146 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00080 | $0.01428 |
| Opus 5 | $0.00040 | $0.00714 |
| Sonnet 5 | $0.00016 | $0.00286 |
| Haiku 4.5 | $0.00008 | $0.00143 |
Grade A, and why
chestertons-fence 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chesterton's Fence Investigation
Enforce epistemic humility before changing existing systems. Understand original purpose before proposing changes.
Quick Start
# Investigate why code exists before changing it
/chestertons-fence "path/to/file.py" "remove unused validation"
# Investigate an ADR before deprecating it
/chestertons-fence "docs/architecture/ADR-005.md" "allow bash scripts"
Triggers
| Phrase | Context |
|---|---|
why does this exist |
Investigating existing code or patterns |
chestertons fence |
Explicit investigation request |
before removing |
Planning deletion or replacement |
investigate history |
Researching original rationale |
prior art investigation |
ADR-required investigation |
Quick Reference
| Input | Output | Destination |
|---|---|---|
| File path or ADR number | Investigation report | .agents/analysis/NNN-chestertons-fence-TOPIC.md |
| Component description | Historical context summary | stdout (JSON) |
When to Use
Use this skill BEFORE proposing changes to existing:
- Code patterns or architectural decisions
- ADRs, constraints, or protocol rules
- Workflow configurations or CI pipelines
- Skills, hooks, or agent prompts
Process
1. Identify Structure What exists? Where is it defined?
|
v
2. Git Archaeology git log, git blame to find origin commit
|
v
3. PR/ADR Search Find the PR or ADR with original rationale
|
v
4. Dependency Analysis What references or depends on this?
|
v
5. Generate Report Fill the investigation template
|
v
6. Decision REMOVE | MODIFY | PRESERVE | REPLACE
Step Details
Step 1: Identify Structure. Locate the exact file, function, pattern, or constraint under investigation. Record its current form.
Step 2: Git Archaeology. Run git log --follow and git blame on the target. Identify the commit that introduced it, the author, and the date.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 147 lines · 80 tokens per session scan A d4e6c9609102
chestertons-fence is a skill published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 1,428 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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