What is artemis-debug-secure?

The Artemis Database Debug Skill is a high-performance Claude agent designed for automated Jira ticket investigation and database debugging. Utilizing a Multi-Agent Swarm architecture, it executes parallel queries and analysis 3x faster than traditional methods. It features secure credential management, auto-learning from past resolutions, and seamless Jira integration to provide instant root cause analysis and anomaly detection for technical support and SRE teams.

When should I use artemis-debug-secure?

artemis-debug-secure is useful in the following scenarios: • Automated Root Cause Analysis: Automatically fetch Jira tickets and perform deep database dives to identify why specific transactions, promotions, or logins failed. • High-Speed Parallel Investigation: Utilize the Multi-Agent Swarm to run multiple database queries and screenshot captures simultaneously, reducing investigation time from minutes to seconds. • Knowledge Retention & Similar Issue Search: Leverage the auto-learn system to identify patterns in resolved tickets and suggest solutions based on historical data and past successful resolutions. • Secure Multi-User Debugging: Manage per-user database credentials securely while allowing team members to share the skill's investigative power without compromising security protocols.

name	artemis-debug-secure
description	Database investigation skill for Jira tickets with secure credential handling. Multi-Agent Swarm for 3x faster parallel execution. Auto-learns from investigations, searches similar tickets, integrates with Jira, and detects anomalies.

Artemis Database Debug Skill (SWARM Edition)

Performance

Metric	Classic	Swarm	Improvement
Investigation time	45-60s	15-20s	3x faster
Browser logins	Every time	Pooled (3)	No login wait
Query execution	Sequential	Parallel	2-3x faster
Screenshots	One-by-one	Batched	50% faster

Features

Feature	Description
Multi-Agent Swarm	5 specialized agents working in parallel
Connection Pooling	3 pre-logged browser sessions, reused
Auto-Learn	Learns patterns from resolved tickets
Similar Search	Finds past tickets with same issue
Jira Integration	Auto-fetch ticket, auto-comment results
Error Detection	Flags anomalies in query results
Multi-User	Per-laptop credentials, shareable

Multi-Agent Swarm Architecture

                         ┌────────────────────────┐
                         │   COORDINATOR AGENT    │
                         │   (Orchestrates all)   │
                         └───────────┬────────────┘
                                     │
          ┌──────────────┬───────────┼───────────┬──────────────┐
          ▼              ▼           ▼           ▼              ▼
    ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐
    │ DB AGENT │  │JIRA AGENT│  │ ANALYSIS │  │ LEARNING │
    │          │  │          │  │  AGENT   │  │  AGENT   │
    │ -Queries │  │ -Fetch   │  │ -Errors  │  │ -Brain   │
    │ -Pool    │  │ -Similar │  │ -RCA     │  │ -Patterns│
    │ -Screen  │  │ -Comment │  │ -Summary │  │ -Learn   │
    └──────────┘  └──────────┘  └──────────┘  └──────────┘
          │              │           │              │
          └──────────────┴───────────┴──────────────┘
                                │
                   ┌────────────┴────────────┐
                   │    SHARED RESOURCES     │
                   │  • Message Bus (async)  │
                   │  • Connection Pool (3)  │
                   │  • Result Store         │
                   └─────────────────────────┘

4-Phase Parallel Workflow

Phase	Tasks	Execution
1. Init	Fetch ticket, Load brain, Get customer_id	PARALLEL
2. Data	Execute queries, Search similar, Screenshots	PARALLEL
3. Analysis	Detect errors, Determine RCA, Find solutions	PARALLEL
4. Output	Generate summary, Format To CAS, Learn	Sequential

Claude Workflow (AUTO)

Step 1: Check Credentials

SKILL_DIR=~/.claude/skills/artemis-debug-secure
USER=$(whoami)
if [ -f "$SKILL_DIR/users/$USER/.credentials" ]; then
  echo "READY"
else
  echo "NEED_SETUP"
fi

Step 2: If NEED_SETUP -> Ask User

Use AskUserQuestion tool:

Questions:
1. "Artemis username?"
2. "Artemis password?"

Then save credentials:

SKILL_DIR=~/.claude/skills/artemis-debug-secure
USER=$(whoami)
mkdir -p "$SKILL_DIR/users/$USER"
cat > "$SKILL_DIR/users/$USER/.credentials" << 'EOF'
ARTEMIS_URL=https://artemis.568winex.com
ARTEMIS_USER={username_from_user}
ARTEMIS_PASS={password_from_user}
EOF
chmod 600 "$SKILL_DIR/users/$USER/.credentials"

Step 3: Fetch Jira Ticket (if ticket key provided)

Use mcp__jira__jira_get_issue(ticketKey)
Parse description to extract: username, webId, date, type

Step 4: Search Similar Tickets

Use mcp__jira__jira_search_issues with JQL:
  project = TCP AND summary ~ "{ticket_type}" AND status = Done ORDER BY created DESC
Review past solutions before investigating

Step 5: Run Investigation

cd ~/.claude/skills/artemis-debug-secure
python3 scripts/investigate.py -t {type} -w {webId} -u "{username}" -k {ticket_key} --learn

Step 6: Add Jira Comment

Use mcp__jira__jira_add_comment(ticketKey, body)
Include: findings, root cause, conclusion, screenshots

Step 7: Output To CAS (500 chars max)

To CAS:
{Root cause}. {Conclusion}. {Details}.

Auto-Detect Type

Keywords in Ticket	Type
promotion, bonus, reject, FP, lucky wheel	`promotion`
deposit, withdrawal, pending, payment	`payment`
vip, upgrade, level, benefits	`vip`
bet, settlement, winning, void	`betting`
login, locked, 2fa, suspended	`login`

Auto-Detect WebId

Site Mention	WebId
Saffaluck, SFL	20154
NocmakatiInc, NMI	20107
Bet25	20120
GBW, gbw777	20109
Lucky7	20132

See docs/webids.md for full list.

SQL Rules (MUST FOLLOW)

Rule	Correct	Wrong
Columns	`SELECT [Id], [Name]`	`SELECT *`
Count	`COUNT(1)`	`COUNT(*)`
Hint	`WITH(NOLOCK)`	None
Limit	`TOP 100`	None
Sort	`ORDER BY DESC`	None

Error Detection (AUTO)

The skill automatically detects these issues:

Type	Detection
Customer	Suspended, Closed, Deleted, Negative balance
Promotion	Multiple rejections, Same FP/IP conflicts
Transaction	Rejected, Balance mismatch, Large amounts
VIP	Downgrades, Rejected bonuses
Betting	Voided bets, Resettlements, Large stakes

Detected issues are flagged as ALERTS (critical) or WARNINGS (potential).

Auto-Learn System

After each investigation, the skill learns:

Pattern: ticket type + root cause + solution
Indicators: key fields from results (RejectSetting, Status, etc.)
Frequency: how often each cause appears

Use learned data:

# Show common causes for a type
python3 scripts/investigate.py --show-common promotion

# Output: [5x] SameFP: Fingerprint conflict...
#         [3x] TurnoverNotMet: Wagering requirement...

File Reference

Need	File
Query templates	`docs/queries.md`
Status codes	`docs/status-codes.md`
WebId mapping	`docs/webids.md`
Playbooks	`playbooks/{type}.md`
Brain patterns	`memory/brain.json`
Learnings	`memory/learnings.json`
Rules	`memory/rules.md`

Script Reference

Swarm (Recommended - 3x faster)

Script	Purpose
`scripts/swarm_investigate.py`	Main entry point - Multi-agent parallel
`scripts/agents/coordinator.py`	Orchestrates 4-phase workflow
`scripts/agents/db_agent.py`	Database operations with pooling
`scripts/agents/jira_agent.py`	Ticket parsing & formatting
`scripts/agents/analysis_agent.py`	Error detection & RCA
`scripts/agents/learning_agent.py`	Pattern learning & matching
`scripts/swarm/pool.py`	Connection pool (3 browsers)
`scripts/swarm/bus.py`	Async message bus
`scripts/swarm/context.py`	Shared investigation context
`scripts/swarm/store.py`	Result aggregation

Classic (Legacy)

Script	Purpose
`scripts/investigate.py`	Classic sequential investigation
`scripts/core.py`	Core classes (Artemis, UserConfig)
`scripts/brain.py`	Auto-learning module
`scripts/jira_integration.py`	Jira parsing & formatting
`scripts/error_detector.py`	Anomaly detection
`scripts/queries.json`	Query definitions

CLI Options

Swarm (Recommended)

python3 scripts/swarm_investigate.py [options]

Required (one of):
  --ticket          Jira ticket key (e.g., TCP-12345)
  OR
  -t, --type        Type: promotion, payment, vip, betting, login
  -w, --webid       WebId number
  -u, --username    Player username

Optional:
  --customer-id     CustomerId (auto-fetched if not provided)
  --headless        Run browsers in headless mode
  --pool-size       Connection pool size (default: 3)
  --benchmark       Run performance benchmark (3 iterations)
  --json            Output results as JSON

Classic (Legacy)

python3 scripts/investigate.py [options]

Required:
  -t, --type        Type: promotion, payment, vip, betting, login
  -w, --webid       WebId number
  -u, --username    Player username

Optional:
  -k, --ticket      Jira ticket key (e.g., TCP-12345)
  -c, --customerid  CustomerId (auto-fetched if not provided)
  --headless        Run without browser window
  --learn           Save learning from this investigation
  --show-common     Show common causes for a type
  -o, --output      Save results to JSON file

Multi-User Support

The users/ folder is gitignored. Each laptop creates its own:

users/{system_username}/.credentials

When skill is shared, new user's credentials are auto-created on first run.

Output Format

To CAS (500 chars max)

{Root cause}. {Conclusion}. {Details}.

Jira Comment

*DATABASE INVESTIGATION RESULTS*
*Ticket:* TCP-12345
*Player:* username
*WebId:* 20154

*Queries Executed:*
- Customer Info
- Rejection Records
- Balance History

*Key Findings:*
- Rejected: Same Fingerprint
- FP used by: other_player

*ROOT CAUSE:* Fingerprint conflict
*CONCLUSION:* Player's device was used by another account

Example Usage

With Jira Ticket

User: Investigate TCP-92018

Claude:
1. Fetch TCP-92018 via mcp__jira__jira_get_issue
2. Parse: username=player123, webId=20154 (Saffaluck), type=promotion
3. Search similar: mcp__jira__jira_search_issues(JQL)
4. Found 3 similar tickets - TCP-91234 had same root cause
5. Check credentials
6. Run: python3 investigate.py -t promotion -w 20154 -u player123 -k TCP-92018 --learn
7. Auto-detect: Fingerprint conflict
8. Comment results to Jira
9. Output To CAS response

Manual Mode

User: Check promotion for player123 on Saffaluck

Claude:
1. Infer: webId=20154, type=promotion
2. Check credentials
3. Run: python3 investigate.py -t promotion -w 20154 -u player123
4. Output To CAS response

artemis-debug-secure

When & Why to Use This Skill

Use Cases

Artemis Database Debug Skill (SWARM Edition)

Performance

Features

Multi-Agent Swarm Architecture

4-Phase Parallel Workflow

Claude Workflow (AUTO)

Step 1: Check Credentials

Step 2: If NEED_SETUP -> Ask User

Step 3: Fetch Jira Ticket (if ticket key provided)

Step 4: Search Similar Tickets

Step 5: Run Investigation

Step 6: Add Jira Comment

Step 7: Output To CAS (500 chars max)

Auto-Detect Type

Auto-Detect WebId

SQL Rules (MUST FOLLOW)

Error Detection (AUTO)

Auto-Learn System

File Reference

Script Reference

Swarm (Recommended - 3x faster)

Classic (Legacy)

CLI Options

Swarm (Recommended)

Classic (Legacy)

Multi-User Support

Output Format

To CAS (500 chars max)

Jira Comment

Example Usage

With Jira Ticket

Manual Mode