Borrowing it
Nothing to install: this file belongs to kimrejstrom/alpacalyzer-algo-trader. 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/kimrejstrom/alpacalyzer-algo-trader/main/.agents/skills/observability/SKILL.mdgit clone --depth 1 https://github.com/kimrejstrom/alpacalyzer-algo-traderWrote 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/kimrejstrom/alpacalyzer-algo-trader/observability)<a href="https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/observability"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/observability/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/kimrejstrom/alpacalyzer-algo-trader/observability"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/observability.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 | $0.00026 | $0.01200 |
| Opus 5 | $0.00013 | $0.00600 |
| Sonnet 5 | $0.00005 | $0.00240 |
| Haiku 4.5 | $0.00003 | $0.00120 |
Grade A, and why
observability 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Skill
How It Works
All runtime metrics flow through the structured event system (events.jsonl). Every trading event, LLM call, and error is emitted as a typed Pydantic model via emit_event() and written as JSON lines to logs/events.jsonl by the FileEventHandler.
The metrics summary script reads this structured data directly — no regex parsing of free-text logs.
Running the Metrics Summary
python scripts/agent_metrics_summary.py
This outputs structured JSON with:
- LLM metrics: Call count, latency, token usage, cost per agent
- Trade metrics: Fills, rejects, entries, exits, PnL
- Error metrics: Error rates by type and component
- Scan metrics: Scanner activity by source
- Last run info: Timestamp, duration, cycle count
Event Types
| Event | Source | Key Fields |
|---|---|---|
SCAN_COMPLETE |
Scanners | source, tickers_found, duration |
SIGNAL_GENERATED |
Execution engine | ticker, action, confidence, strategy |
SIGNAL_EXPIRED |
Signal queue | ticker, created_at, reason |
ENTRY_TRIGGERED |
Execution engine | ticker, side, quantity, entry_price, stop_loss |
EXIT_TRIGGERED |
Execution engine | ticker, pnl, pnl_pct, reason |
ORDER_SUBMITTED |
Order manager | ticker, order_type, quantity |
ORDER_FILLED |
Trade update handler | ticker, side, filled_qty, avg_price |
ORDER_REJECTED |
Trade update handler | ticker, reason |
POSITION_OPENED |
Execution engine | ticker, side, entry_price, strategy |
POSITION_CLOSED |
Execution engine | ticker, pnl, pnl_pct, exit_reason |
COOLDOWN_STARTED |
Cooldown manager | ticker, duration |
COOLDOWN_ENDED |
Cooldown manager | ticker |
CYCLE_COMPLETE |
Execution engine | entries/exits triggered, duration |
LLM_CALL |
LLMClient.complete_structured() |
agent, model, tier, latency_ms, tokens, cost |
AGENT_REASONING |
show_agent_reasoning() |
agent, tickers, reasoning |
ERROR |
Any component | error_type, component, message |
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 · 138 lines · 26 tokens per session scan A 89d44ff9dc91
observability is a skill published in the GitHub repository kimrejstrom/alpacalyzer-algo-trader (2 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 1,200 once invoked, about $0.0001 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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