session-status

name	session-status
description	Inspect bundle status, runtime configuration, and recent execution context. Use when user wants the operational audit surface: runtime root, recent runs, current context, and where the workflow left off.
argument-hint	["none"]
allowed-tools	Bash(python3 *), Read, Glob

Session Status

Inspect bundle runtime state for: $ARGUMENTS

Overview

• Implementation status: code-backed
• Local entry script: <bundle-root>/session-status/run.py
• Primary purpose: expose the bundle root, config path, output root, and recent command history so the agent can continue the workflow from explicit context instead of guesswork
• Research layer: operational monitoring (Cross-cutting concern for all workflow stages)
• Workflow role: operational control surface spanning artifact reuse, history lookup, and monitoring handoff
• Local executor guarantee: return the current runtime root, config path, last symbol, Feishu mode, and recent history for the chosen execution context

Use When

• The user asks what the bundle has been doing.
• The user wants to know where runtime output is being written.
• The user wants to confirm the current Feishu mode or the last-used symbol.
• The agent needs to decide whether to rerun, reuse artifacts, or continue a monitoring workflow.
• The user wants to understand current session state and context.
• The user wants to resume work from a previous session.
• The user wants to audit recent activity for troubleshooting.

Do Not Use When

• The user wants analysis or decision output. Use a market-facing skill.
• The user wants to inspect a specific prior report in detail. Use reports or analysis-history.
• The user wants a data-freshness diagnosis rather than runtime context. Use data-sync.
• The user wants portfolio performance tracking. Use paper-trading.

Inputs

• No required positional arguments.
• Optional --context through the local script if the caller needs a non-default execution context.

Execution

Step 1: Gather runtime context

Collect comprehensive session state information:

Runtime configuration:

• Bundle root directory path
• Config file path and contents
• Output directory path
• Execution context (default or custom)
• Environment variables (if relevant)
• Python version and dependencies

Session state:

• Last symbol used
• Last skill executed
• Last execution timestamp
• Session start time
• Session duration
• Active watchlists (if any)

Integration status:

• Feishu mode (enabled/disabled)
• Notification settings
• External integrations (if any)
• API connections (if any)

Recent activity:

• Recent commands (last 10-20)
• Recent symbols analyzed
• Recent artifacts created
• Recent errors or warnings
• Recent data syncs

Step 2: Inspect artifact state

Audit available artifacts and their status:

Artifact inventory:

• Analysis reports (count, latest date)
• Stock data packets (count, latest date)
• Backtest results (count, latest date)
• Paper trading records (count, latest date)
• Screening results (count, latest date)
• Dashboard snapshots (count, latest date)

Artifact freshness:

• Latest artifact timestamp
• Stale artifacts (> 7 days old)
• Orphaned artifacts (no longer referenced)
• Incomplete artifacts (partial writes)

Artifact locations:

• Output root directory
• Run directories (dated subdirectories)
• Cache directories
• Log directories
• Export directories

Step 3: Assess workflow state

Determine where the workflow left off:

Workflow stage identification:

• Stage 1 (Universe Formation): Last watchlist import or screening
• Stage 2 (Data Collection): Last data sync or market-data run
• Stage 3 (Data Cleaning): Last normalization or validation
• Stage 4 (Feature Engineering): Last market-analyze or technical-scan
• Stage 5 (Backtesting): Last backtest-evaluator run
• Stage 6 (Risk Management): Last decision-support or strategy-design
• Stage 7 (Live Trading): Last paper-trading execution

Workflow continuity:

• Can workflow resume from last state?
• Are artifacts reusable or stale?
• Are dependencies satisfied?
• Are there blocking issues?

Next logical steps:

• Based on last activity, what should happen next?
• Which artifacts can be reused?
• Which skills should be invoked?
• What validation is needed before proceeding?

Step 4: Diagnose operational issues

Identify any operational problems:

Common issues:

• Stale artifacts: Artifacts too old for current use
• Missing dependencies: Required data or artifacts not available
• Configuration errors: Invalid or missing config settings
• Permission issues: Cannot write to output directories
• Disk space issues: Output directory full or near capacity
• Integration failures: Feishu or other integrations not working

For each issue:

• Issue type and severity
• Impact on workflow
• Recommended resolution
• Workaround (if available)

Step 5: Generate status report

Organize findings into structured report:

Part 1: Runtime Configuration

• Bundle root directory
• Config file path
• Output directory path
• Execution context
• Integration status (Feishu, etc.)

Part 2: Session State

• Session start time and duration
• Last symbol used
• Last skill executed
• Last execution timestamp
• Active watchlists

Part 3: Recent Activity

• Recent commands (last 10-20 with timestamps)
• Recent symbols analyzed
• Recent artifacts created
• Recent errors or warnings
• Recent data syncs

Part 4: Artifact Inventory

• Analysis reports (count, latest)
• Stock data packets (count, latest)
• Backtest results (count, latest)
• Paper trading records (count, latest)
• Screening results (count, latest)
• Dashboard snapshots (count, latest)

Part 5: Artifact Freshness

• Latest artifact timestamp
• Stale artifacts (count and list)
• Orphaned artifacts (count and list)
• Incomplete artifacts (count and list)

Part 6: Workflow State

• Current workflow stage
• Last completed stage
• Next logical steps
• Artifacts available for reuse
• Dependencies satisfied/missing

Part 7: Operational Issues

• Issues detected (count and list)
• Severity distribution
• Impact on workflow
• Recommended resolutions

Part 8: Recommendations

• Immediate actions (critical issues)
• Artifact cleanup (stale/orphaned)
• Workflow resumption (next steps)
• Monitoring setup (ongoing)

Step 6: Run the local executor

python3 <bundle-root>/session-status/run.py

Step 7: Use result as operational audit

When delivering results, focus on actionable context:

Operational context interpretation:

• This is runtime state, not analysis or recommendations
• Artifact inventory is for reuse decisions, not performance assessment
• Recent activity is for continuity, not evaluation
• Workflow state is for resumption, not validation

Artifact reuse guidance:

• State which artifacts are fresh enough to reuse
• State which artifacts are stale and should be regenerated
• State which artifacts are missing and need generation
• State dependencies between artifacts

Workflow resumption guidance:

• State where workflow left off
• State next logical steps
• State which skills to invoke next
• State validation needed before proceeding

Issue resolution guidance:

• State operational issues clearly
• Provide specific resolution steps
• Prioritize by severity and impact
• Offer workarounds when available

Output Contract

• Minimum local executor output: human-readable text beginning with A-Stockit runtime status.
• Core fields: bundle root, config file, output directory, Feishu mode, last symbol, and recent context history.
• Side effects: adds one session-status history record for the current execution context.
• Caller-facing delivery standard:
  • Eight-part structure: Runtime configuration, session state, recent activity, artifact inventory, artifact freshness, workflow state, operational issues, recommendations
  • Operational context only: Present as runtime state, not analysis or recommendations
  • Artifact reuse guidance: State which artifacts are fresh/stale/missing and reusable
  • Workflow resumption guidance: Identify likely next useful artifact or skill for resuming work
  • Issue diagnosis: Specific operational issues with severity and resolution steps
  • Scope clarity: This is a context surface, not a semantic summary of every run
  • Actionable recommendations: Immediate actions, cleanup, resumption steps, monitoring
  • No analysis claims: Runtime state only, no market views or investment recommendations

Failure Handling

• Parse and argument errors: non-zero exit with a readable 命令错误 message.
• Unexpected runtime issues: readable failure text beginning with 执行失败:.
• Missing config or output directories: report issue and recommend initialization.
• Corrupted history or artifacts: flag for cleanup and regeneration.
• Permission issues: report specific permission errors and resolution steps.

Key Rules

• Use this skill for operational context only.
• Prefer it before rerunning workflows just to locate prior outputs.
• Treat it as the staging surface for artifact reuse, monitoring continuation, and workflow resumption.
• Artifact inventory is for reuse decisions, not performance evaluation.
• Recent activity is for continuity, not retrospective analysis.
• Workflow state is for resumption, not validation.
• Issue diagnosis must be specific and actionable.
• Recommendations must be prioritized by severity and impact.
• No analysis or investment claims. This is operational monitoring only.

Composition

• Pairs naturally with reports, analysis-history, data-sync, and paper-trading.
• Should be used before artifact-dependent skills to check reusability.
• Can inform data-sync about what needs refreshing.
• Can inform reports and analysis-history about available artifacts.
• Can inform workflow resumption decisions across all skills.