Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Daily Stock Analysis
Deterministic daily stock analysis skill for global equities. Use when users need daily analysis, next-trading-day close prediction, prior forecast review, r...
skill openclawclawhub Free
Deterministic daily stock analysis skill for global equities. Use when users need daily analysis, next-trading-day close prediction, prior forecast review, r...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/financial-metrics.md, references/fundamental-analysis.md, references/metrics.md, references/minimal_mode.md, references/report_template.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- HeXavi8
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Daily Stock Analysis
Perform market-aware, evidence-based daily stock analysis with prediction, next-run review, rolling accuracy tracking, and a structured self-evolution mechanism that updates future assumptions from observed forecast errors.
Hard Rules
Read and write files only under working_directory. Save new reports only to: <working_directory>/daily-stock-analysis/reports/ Use filename: YYYY-MM-DD-<TICKER>-analysis.md If same ticker/day file exists, ask user: overwrite or new_version (-v2, -v3, ...) For unattended runs, default to new_version Always review history before new prediction. Limit history read count to control token usage: Script mode: max 5 files (default) Compatibility mode: max 3 files
Required Scripts (Use First)
Plan output path + collect history: python3 {baseDir}/scripts/report_manager.py plan \ --workdir <working_directory> \ --ticker <TICKER> \ --run-date <YYYY-MM-DD> \ --versioning auto \ --history-limit 5 Compute rolling accuracy from existing reports: python3 {baseDir}/scripts/calc_accuracy.py \ --workdir <working_directory> \ --ticker <TICKER> \ --windows 1,3,7,30 \ --history-limit 60 Optional: migrate legacy files after explicit user confirmation: python3 {baseDir}/scripts/report_manager.py migrate \ --workdir <working_directory> \ --file <ABS_PATH_1> --file <ABS_PATH_2>
Compatibility Mode (No Python / Small Model)
If Python scripts are unavailable or model capability is limited, switch to minimal mode: Read at most 3 recent reports for the same ticker. Use only a minimal source set: one official disclosure source one reliable market data source (Yahoo Finance acceptable) Output concise result only: recommendation pred_close_t1 prior review (prev_pred_close_t1, prev_actual_close_t1, AE, APE) if available one improvement_action Save report with same filename rules in canonical reports directory. See references/minimal_mode.md.
Minimal Run Protocol
Resolve ticker/exchange/market (ask if ambiguous). Run report_manager.py plan. Read history_files returned by script. If legacy_files exist, list all absolute paths and ask whether to migrate. Gather data using references/sources.md + references/search_queries.md. Run calc_accuracy.py for consistent metrics. Render report using references/report_template.md. Save to selected_output_file returned by report_manager.py.
Required Output Fields
Must include: recommendation pred_close_t1 prev_pred_close_t1 prev_actual_close_t1 AE, APE rolling strict/loose accuracy fields improvement_actions
Self-Improvement (Required)
Each run must include 1-3 concrete improvement_actions from recent misses and use them in the next run. Do not skip this step.
Scheduling Recommendation
Recommend users set this as a weekday recurring task (for example 10:00 local time) to keep prediction-review windows continuous.
References
Default: references/workflow.md references/report_template.md references/metrics.md references/search_queries.md references/sources.md references/minimal_mode.md references/security.md Deep-dive only (full_report mode): references/fundamental-analysis.md references/technical-analysis.md references/financial-metrics.md
Compliance
Always append: "This content is for research and informational purposes only and does not constitute investment advice or a return guarantee. Markets are risky; invest with caution."
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- references/financial-metrics.md
- references/fundamental-analysis.md
- references/metrics.md
- references/minimal_mode.md
- references/report_template.md