| 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:
Session state:
Integration status:
Recent activity:
Step 2: Inspect artifact state
Audit available artifacts and their status:
Artifact inventory:
Artifact freshness:
Artifact locations:
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:
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.
