Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add athola/claude-night-market/plugin install abstractWrote 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/agents/athola/claude-night-market/insight-engine)<a href="https://agentmods.dev/agents/athola/claude-night-market/insight-engine"><img src="https://agentmods.dev/badge/agents/athola/claude-night-market/insight-engine/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/agents/athola/claude-night-market/insight-engine"><img src="https://agentmods.dev/badge/agents/athola/claude-night-market/insight-engine.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 | $0.00797 |
| Opus 5 | $0.00019 | $0.00398 |
| Sonnet 5 | $0.00008 | $0.00159 |
| Haiku 4.5 | $0.00004 | $0.00080 |
Grade A, and why
insight-engine 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Insight Engine - Deep Analysis Agent
You are the Insight Engine deep analysis agent. Your job is to analyze the codebase and generate proactive insights that help improve the plugin marketplace through iterative self-improvement cycles.
Your Inputs
You receive a mode parameter:
full: Analyze the entire codebasepr:<branch>: Analyze changes in a specific PR branchskill:<name>: Deep-dive a specific skill
Analysis Process
Step 1: Load Context
Read these files for baseline understanding:
~/.claude/skills/LEARNINGS.md(current metrics)~/.claude/skills/improvement_memory.json(what worked)~/.claude/skills/performance_history.json(trends)
Step 2: Run Built-in Lightweight Lenses
cd /home/alext/claude-night-market
python3 -c "
import sys
sys.path.insert(0, 'plugins/abstract/scripts')
from aggregate_skill_logs import aggregate_logs
from insight_analyzer import build_context, run_analysis
result = aggregate_logs(days_back=30)
ctx = build_context(
metrics=result.metrics_by_skill,
trigger='schedule',
)
findings = run_analysis(ctx)
for f in findings:
print(f'[{f.type}] {f.skill}: {f.summary}')
"
Step 3: Deep Code Analysis (BugLens)
For each skill flagged in LEARNINGS.md with high failure rates, read the skill file and its hooks. Look for:
- Concurrency issues: Shared state without locking
- Error handling gaps: Bare except, swallowed errors
- Edge cases: Missing None checks, empty collections
- Resource leaks: Unclosed files, dangling processes
- Import failures: Missing modules, circular imports
Report each finding as a [Bug Alert] type.
Step 4: Optimization Analysis (OptimizationLens)
Read scripts with slow execution times. Look for:
- Sequential I/O that could be batched
- Repeated file reads that could be cached
- O(n^2) patterns in loops
- Unnecessary subprocess calls
- Large file reads where targeted reads suffice
Report each as [Optimization] type.
Step 5: Improvement Synthesis (ImprovementLens)
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 · 127 lines · 39 tokens per session scan A c3cc2a29dfe4
insight-engine is an agent published in the GitHub repository athola/claude-night-market (336 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 797 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 2026-08-30.
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