Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/lis186/SourceAtlasnpx agentmods add skills/lis186/sourceatlas/patternWrote 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/lis186/sourceatlas/pattern)<a href="https://agentmods.dev/skills/lis186/sourceatlas/pattern"><img src="https://agentmods.dev/badge/skills/lis186/sourceatlas/pattern/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/lis186/sourceatlas/pattern"><img src="https://agentmods.dev/badge/skills/lis186/sourceatlas/pattern.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 4 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00010 | $0.02425 |
| Opus 5.5 | $0.00004 | $0.00970 |
| Sonnet 5.5 | $0.00002 | $0.00485 |
| Haiku 4.5 | $0.00001 | $0.00243 |
Grade A, and why
pattern 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 — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SourceAtlas: Pattern Learning Mode
Constitution: ANALYSIS_CONSTITUTION.md v1.0
Context
Pattern requested: $ARGUMENTS Goal: Learn how THIS specific codebase implements the requested pattern Time Limit: 5-10 minutes maximum
Quick Start
- Check cache (skip if
--forceflag present) - Detect pattern using
find-patterns.shscript - Apply output mode (--brief / --full / smart)
- Analyze top 2-3 files (high-entropy priority)
- Extract conventions and best practices
- Generate implementation guide
- Verify output using verification-guide.md
- Auto-save to
.sourceatlas/patterns/
Your Task
You are analyzing how THIS codebase implements a specific pattern by:
- Finding 2-3 best example files
- Extracting design patterns and conventions
- Providing actionable implementation guidance
Output Modes
| Parameter | Behavior | Use Case |
|---|---|---|
--brief |
List files only | Quick file browser |
--full |
Analyze all files | Deep learning |
| (default) | Smart: ≤5 files→full; >5→selection UI | Balanced |
Core Workflow
Execute these steps in order. See workflow.md for complete details.
Step 0: Parse Output Mode (10 seconds)
Purpose: Determine how much analysis to perform.
Detect flags:
--brief→ List files only, no analysis--full→ Analyze all found files- No flag → Smart mode (adapt based on file count)
→ See workflow.md#step-0
Step 1: Execute Pattern Detection (1-2 minutes)
Purpose: Find relevant files using optimized search.
Use find-patterns.sh script first:
# Locate script (global → local)
if [ -f ~/.claude/scripts/atlas/find-patterns.sh ]; then
SCRIPT_PATH=~/.claude/scripts/atlas/find-patterns.sh
elif [ -f scripts/atlas/find-patterns.sh ]; then
SCRIPT_PATH=scripts/atlas/find-patterns.sh
fi
bash "$SCRIPT_PATH" "$ARGUMENTS"
What ships with it
4 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.
- 1mo ago First seen · 364 lines · 0 tokens per session scan A b0aee1b87a3f
pattern is a skill published in the GitHub repository lis186/SourceAtlas (48 stars, last pushed 2mo ago), licensed MIT. It adds 10 tokens to every session and 2,425 once invoked, about $0.0000 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 2026-08-30.
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