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Error Logger · aiskillstore | DecimalAI

aiskillstore/error-logger

SkillScore100Maintained

Structured JSON logging with correlation IDs for multi-service systems. Use when implementing logging, debugging failures, or tracing errors across services. Triggers on: add logging, error handling, debug failures, trace errors.

DevOpsby aiskillstore

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

DevOps & SRE pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/aiskillstore-error-logger/SKILL.md

decimalai skills pull aiskillstore-error-logger --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/aiskillstore-error-logger/SKILL.md https://app.decimal.ai/s/aiskillstore-error-logger/SKILL.md

Raw file: /s/aiskillstore-error-logger/SKILL.md ↗ · machine-readable summary at /s/aiskillstore-error-logger.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

25 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 19d ago

Yes — +52% lift-27% tokens0% turns100% of whole cases pass with skill (25/25)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	-20%	0%
case-02	✗→✓	▲ Improved	-44%	0%
case-03	✗→✓	▲ Improved	-4%	0%
case-04	✗→✓	▲ Improved	5%	0%
case-05	✗→✓	▲ Improved	-30%	0%

Showing 5 of 25 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

aiskillstore / marketplace

skills/barissozen/error-logger/SKILL.md

View on GitHub ↗

Imported 8/10/2026Version v1

Skill contents

SKILL.md

Error Logger

Structured JSON logging with correlation IDs for multi-service systems.

When to Use

Implementing logging infrastructure
Debugging failures across services
Tracing errors with correlation IDs
Adding error handling to operations
Reviewing logging patterns

Workflow

Step 1: Create Operation Context

Start operation with appropriate prefix (liq_, arb_, quo_, op_).

Step 2: Log with Context

Include correlation ID in all related log entries.

Step 3: Propagate Correlation ID

Pass via X-Correlation-ID header across services.

Log Format

json
{
  "timestamp": "2024-01-15T14:32:01.847Z",
  "level": "ERROR",
  "correlation_id": "liq_18d4f2a1_x7k9",
  "service": "rust-hotpath",
  "event_type": "TX_REVERT",
  "message": "Liquidation reverted",
  "context": {}
}

Correlation ID

Format: {prefix}_{timestamp_hex}_{random} Prefixes: liq_, arb_, quo_, op_

Usage

typescript
const ctx = log.startOperation('liq');
log.error(ctx, 'TX_REVERT', 'Failed', { tx_hash, gas_used });

// Propagate via HTTP
headers: { 'X-Correlation-ID': ctx.correlation_id }

Log Levels

| Level | Use For | |-------|---------| | ERROR | Operation failures | | WARN | Retries, recoverable | | INFO | Normal operations | | DEBUG | Calculations |

Illustrative Want to see what aiskillstore/error-logger would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

9/3/2026, 05:48 UTC · 25 cases

Lift

+52%

100% vs 48%

Agent turns

1.0 vs 1.0

Tokens

-27%

1,473.28 vs 2,030.28

fail → pass · 13pass → pass · 12

Per-case results (25)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-01 I am building a TypeScript microservice that executes arbitrage strategies across decentralized exchanges. When a trade fails, I need a standard pattern to create a contextual trace ID, record an error log event with context details, and attach the tracing ID header for downstream microservice calls. Can you show me the code snippet following our system's standard error logger patterns?	fail→pass	16,332	18,902	+16%	1	1	0%	3,110	2,490	-20%	0	0	—
case-02 Our log indexing service requires an example JSON payload generated by our multi-service logging protocol when a liquidation operation encounters a transaction revert. Could you construct a sample JSON log record that includes all standard tracing fields, level, service identifier, event name, and contextual metadata?	fail→pass	19,524	11,181	-43%	1	1	0%	2,979	1,659	-44%	0	0	—
case-03 I have an order quoting worker in Node.js that communicates with several back-end pricing engines. I need to instrument this function using our standard logging contextual workflow—specifically initializing the request tracker, logging both recoverable retry warnings and critical failures, and passing the correlation header in fetch requests to secondary services. Please provide the code implementation for this workflow.	fail→pass	19,987	20,222	+1%	1	1	0%	3,962	3,823	-4%	0	0	—
case-04 Write a TypeScript function that logs an error when an EVM liquidation bot transaction fails and forwards the correlation context in an HTTP call to a notification endpoint.	fail→pass	15,552	18,622	+20%	1	1	0%	3,333	3,487	+5%	0	0	—
case-05 Show how to initialize a request tracking context for a general background worker task named batch_rebalance in TypeScript.	fail→pass	23,147	27,083	+17%	1	1	0%	3,711	2,580	-30%	0	0	—
case-06 In a DEX trade execution worker, when a transaction submission fails due to temporary network gas spikes and is about to be retried, what log level should be recorded?	pass→pass	15,240	3,550	-77%	1	1	0%	1,583	958	-39%	0	0	—
case-07 In a high-frequency trading engine, which log level is appropriate for recording intermediate pricing formula evaluation steps?	fail→pass	15,733	7,394	-53%	1	1	0%	1,703	792	-53%	0	0	—
case-08 Construct a JSON error log record for an operation failure, ensuring the time property matches our system's JSON schema standard.	pass→pass	8,377	4,763	-43%	1	1	0%	1,429	1,249	-13%	0	0	—
case-09 What is the string formatting structure used for correlation IDs in our logging infrastructure?	fail→pass	11,212	7,461	-33%	1	1	0%	764	741	-3%	0	0	—
case-10 Specify the HTTP header key used when propagating request correlation identifiers to secondary microservices.	pass→pass	11,035	2,056	-81%	1	1	0%	763	634	-17%	0	0	—
case-11 In our structured JSON log record schema, what property name is used to identify the specific event type like TX_REVERT?	fail→pass	8,939	6,964	-22%	1	1	0%	405	726	+79%	0	0	—
case-12 When adding contextual key-value pairs like gas_used or tx_hash to a structured JSON error log, under what top-level property should they be placed?	pass→pass	11,406	10,468	-8%	1	1	0%	2,080	940	-55%	0	0	—
case-13 Write a code snippet initializing a correlation context for an incoming order quote request in an express endpoint.	fail→pass	15,514	6,793	-56%	1	1	0%	1,949	1,825	-6%	0	0	—
case-14 What log level must be used when an arbitrage operation encounters an unrecoverable contract execution failure?	pass→pass	12,251	7,146	-42%	1	1	0%	1,935	688	-64%	0	0	—
case-19 Construct a complete JSON log object for a TX_REVERT event originating from the rust-hotpath service during a liquidation operation.	fail→pass	13,137	9,997	-24%	1	1	0%	2,783	1,425	-49%	0	0	—
case-15 When a multi-step trade routing operation completes successfully, at what log level should the completion event be logged?	pass→pass	15,874	3,346	-79%	1	1	0%	1,787	867	-51%	0	0	—
case-16 Construct a JSON log record for a Rust service called rust-hotpath, ensuring the service identifier field complies with our standard JSON schema.	fail→pass	13,323	4,030	-70%	1	1	0%	1,473	1,100	-25%	0	0	—
case-17 Identify the correlation ID prefixes designated for liquidations, arbitrage, quotes, and general operations in our logging standard.	fail→pass	9,220	7,134	-23%	1	1	0%	1,053	706	-33%	0	0	—
case-18 Show how to attach the correlation ID context ctx to a Node.js fetch call header object when calling an external pricing API.	pass→pass	17,548	14,706	-16%	1	1	0%	2,248	1,353	-40%	0	0	—
case-20 Show how to invoke log.startOperation in TypeScript when initiating an arbitrage strategy execution.	pass→pass	20,386	10,382	-49%	1	1	0%	2,718	1,400	-48%	0	0	—
case-21 Provide a sample JSON log record representing a timeout failure during a pricing quote query.	fail→pass	16,463	12,694	-23%	1	1	0%	1,935	1,412	-27%	0	0	—
case-22 Which log level should be selected when recording internal slippage boundary calculations inside an execution router?	pass→pass	12,580	8,952	-29%	1	1	0%	1,872	687	-63%	0	0	—
case-23 Write a Fluentbit configuration snippet (fluent-bit.conf) to collect log lines from stdout and route them to an Elasticsearch cluster at https://elasticsearch.internal:9200.	pass→pass	9,314	15,084	+62%	1	1	0%	1,558	2,211	+42%	0	0	—
case-24 Write a Prometheus alert rule YAML configuration named HighErrorRate that triggers when the 5-minute HTTP 5xx error rate exceeds 5% on pricing-engine.	pass→pass	11,578	5,120	-56%	1	1	0%	1,340	1,338	-0%	0	0	—
case-25 Show how to configure the OpenTelemetry Node.js SDK to use a ratio-based sampler set to a 10% sampling probability for incoming HTTP requests.	pass→pass	16,589	11,650	-30%	1	1	0%	2,284	1,741	-24%	0	0	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +52 percentage points is the difference between those two pass rates over the 25 comparable cases.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 48%
Pass rate with the skill: 100%
Cases attempted: 25
Cases counted toward lift: 25
Cases that regressed: 0
Skill version measured: v1
Run completed: 9/3/2026, 05:48 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-01

Prompt

I am building a TypeScript microservice that executes arbitrage strategies across decentralized exchanges. When a trade fails, I need a standard pattern to create a contextual trace ID, record an error log event with context details, and attach the tracing ID header for downstream microservice calls. Can you show me the code snippet following our system's standard error logger patterns?

Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.

Show both answers

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run100

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Efficiency · separate axis (at equal correctness)+0% turns-35% tokens

Shown for transparency; not credited unless a verified run shows no correctness regression.

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 19 days ago

Other measured skills in the registry, with their headline benchmark lift.