Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· Developer Tools
Vnstock Free Expert
Runs an end-to-end vnstock workflow for free-tier-safe Vietnam stock valuation, ranking, and API operations with strict rate-limit control; used when users r...
skill openclawclawhub Free
Runs an end-to-end vnstock workflow for free-tier-safe Vietnam stock valuation, ranking, and API operations with strict rate-limit control; used when users r...
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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, agents/openai.yaml, references/capabilities.md, references/free_tier_playbook.md, references/invocation_recipes.md, references/method_matrix.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
- NDTChan
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
VNStock Free Expert
Use this skill when the user needs advanced Vietnam stock analysis with vnstock, while staying safe on free-tier limits.
Important packaging note
This skill is self-contained and does not require shipping a separate vnstock/ docs folder. All operational knowledge needed by the agent is stored under: references/
Read order
Read references/capabilities.md. Read references/method_matrix.md for exact class/method mapping. Read references/free_tier_playbook.md before large runs.
Scope and constraints
Library: vnstock only. Preferred sources: kbs first, vci fallback. Never use tcbs. Treat Screener API as unavailable unless user confirms it is restored in their installed version.
Free-tier operating rules
No API key: target <= 20 requests/minute. Free API key: target <= 60 requests/minute. Safe default pacing in scripts: 3.2s/request. Reuse cached artifacts between steps.
Shared confidence rubric (required)
Report confidence as High / Medium / Low using this standard: High: universe coverage >= 95%, critical metrics coverage >= 80%, and hard errors <= 5% of symbols. Medium: universe coverage >= 80%, critical metrics coverage >= 60%, and hard errors <= 15%. Low: below Medium thresholds or material missing fields that can flip ranking results. Always output: Confidence level. Coverage stats (symbols_requested, symbols_scored, % missing by key metric). Top missing fields that may change conclusions.
API key configuration (implemented)
Skill-local key file: .env Variable: VNSTOCK_API_KEY All API-calling scripts auto-load this key and call vnstock auth setup before requests. You can override per run with --api-key "...".
Execution workflow (ordered)
Validate environment (python, vnstock, pandas) and load optional API key from .env. Build a universe using scripts/build_universe.py (group, exchange, or symbols mode). Collect market data with scripts/collect_market_data.py using safe pacing. Collect fundamentals with scripts/collect_fundamentals.py. Score and rank using scripts/score_stocks.py. Generate analyst-style memo with scripts/generate_report.py. Apply confidence rubric, disclose missing fields, and summarize risks.
Downstream handoff bundle (required when doing single-ticker deep dive)
When the user request is about valuing or building a memo for a specific ticker (or a small list), output a compact JSON bundle that downstream skills can reuse: ticker, as_of_date, currency financials (income/balance/cashflow + key ratios if available) price_history (returns 1m/3m/6m/12m) peer_set (if you built one) metadata.source and data_quality_notes This bundle is designed to feed equity-valuation-framework and portfolio-risk-manager.
A) Discovery and universal invocation (for broad feature coverage)
catalog_vnstock.py Path: scripts/catalog_vnstock.py Use when: You need to inspect available classes/methods in the installed vnstock version. You want to confirm compatibility before running a method. invoke_vnstock.py Path: scripts/invoke_vnstock.py Use when: You need to call any supported class/method beyond the prebuilt valuation pipeline. You want one generic entry point for Listing, Quote, Company, Finance, Trading, Fund, or other exported classes. This script supports dynamic invocation by class name and method name with JSON kwargs.
B) Valuation pipeline scripts
build_universe.py Use when building symbol universe from index/exchange/custom symbol list. Input: source + mode + group/exchange/symbols. Output: outputs/universe_*.csv and latest pointers. collect_market_data.py Use when collecting OHLCV/momentum fields (3M, 6M, 12M returns). Input: universe CSV path. Output: outputs/market_data_*.csv + per-symbol errors in JSON. collect_fundamentals.py Use when collecting valuation and quality metrics from finance/company APIs. Input: universe CSV path. Output: outputs/fundamentals_*.csv + per-symbol errors in JSON. score_stocks.py Use when ranking symbols with composite scoring. Input: market + fundamentals CSV files. Output: outputs/ranking_*.csv. generate_report.py Use when converting ranking output to analyst-style markdown memo. Input: ranking CSV file. Output: outputs/investment_memo_*.md. run_pipeline.py Use when running the end-to-end pipeline in one command. Input: source + universe mode. Output: all artifacts above in one run.
Error handling rules
Log symbol-level failures and continue processing remaining symbols. Do not claim missing metrics as zeros; mark them as missing. If a critical step fails, stop and report failed step + command + suggested retry scope.
Recommended decision logic
If request is βstandard valuation/rankingβ: run pipeline scripts. If request needs a specific vnstock capability not in pipeline: use catalog_vnstock.py then invoke_vnstock.py. If request volume is large: apply free_tier_playbook.md throttling and chunking strategy.
Confidence aggregation (required)
When output includes ranking and valuation interpretation: Compute data confidence from coverage metrics (symbols_scored, missing key fields, error ratio). Compute model confidence from method robustness (single metric vs multi-factor consistency). Final confidence = lower of data confidence and model confidence. In Low confidence cases, provide directional output only and list required missing inputs.
Required output template
What Was Run: scripts, source, universe scope, and pacing profile. Coverage: requested symbols, scored symbols, and missingness by key field. Top Results: ranked list with score columns. Key Risks: concentration, stale data, missing metrics, or provider limitations. Confidence and Gaps: final confidence + exact blockers.
Quick command examples
python scripts/catalog_vnstock.py --outdir ./outputs python scripts/invoke_vnstock.py --class-name Quote --init-kwargs '{"source":"kbs","symbol":"VCB"}' --method history --method-kwargs '{"start":"2024-01-01","end":"2024-12-31","interval":"1D"}' --outdir ./outputs python scripts/run_pipeline.py --source kbs --mode group --group VN30 --outdir ./outputs
Trigger examples
"Analyze VN30 using vnstock but keep it free-tier safe." "Rank Vietnamese stocks by value/quality/momentum with KBS data." "Run a full vnstock pipeline and return top candidates with risk notes."
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs1 Config
- SKILL.md
- references/capabilities.md
- references/free_tier_playbook.md
- references/invocation_recipes.md
- references/method_matrix.md
- agents/openai.yaml