flashrag-evidence
flashrag-evidence retrieves grounded, citeable snippets from local VCO documentation, configuration, and skills catalogs to support decisions with file and line anchors. Use this skill when you need to verify protocol compliance, config semantics, routing rationale, or locate the exact source of a rule within your local evidence plane, but not for code dependency analysis or current web information.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/flashrag-evidence && cp -r /tmp/flashrag-evidence/bundled/skills/flashrag-evidence ~/.claude/skills/flashrag-evidenceSKILL.md
# FlashRAG Evidence (VCO)
## When to use
Use this skill when you need **grounded, citeable evidence** from local documentation/configuration to support VCO decisions or recommendations, especially for:
- VCO routing / pack selection rationale
- Protocol compliance (think/do/review/team/retro)
- Config semantics (thresholds, overlays, governance)
- “Show me where this rule comes from” / “give me the exact snippet”
This skill is **not** a replacement for GitNexus (code dependency graph) or web search. It focuses on **local docs and config**.
## Inputs
- Query: what you’re trying to verify (short, concrete)
- Optional: corpus root(s) to search (defaults below)
## Default corpus (evidence plane)
1. VCO core docs/config inside `~/.codex/skills/vibe/`:
- `protocols/`, `config/`, `references/`, `scripts/router/`
2. Skills catalog (`~/.codex/skills/**/SKILL.md`) for tool capability evidence
3. (Optional) Project-local VCO overlays under the current workspace, if present
## Workflow (Lite, no heavy deps)
1. Run the evidence retriever script:
- Windows PowerShell:
- `python C:\Users\羽裳\.codex\skills\flashrag-evidence\scripts\flashrag_evidence.py --query "…" --topk 8`
2. (Optional) Enable a faster FlashRAG-style BM25 backend (`bm25s`)
- Preflight (checks vendoring + env; does NOT read secrets):
- `pwsh C:\Users\羽裳\.codex\skills\vibe\scripts\ruc-nlpir\preflight.ps1`
- Manually create an isolated venv for the vendored runtime and install only the minimal packages you need. The old `install-upstreams.ps1` auto-install path has been removed on purpose.
- Use bm25s engine:
- `C:\Users\羽裳\.codex\_external\ruc-nlpir\.venv\Scripts\python.exe C:\Users\羽裳\.codex\skills\flashrag-evidence\scripts\flashrag_evidence.py --engine bm25s --query "…" --topk 8`
3. Use the returned snippets as P5 evidence:
- **[Command]** the exact command you ran
- **[Output]** the top snippets (path + line anchor)
- **[Claim]** the conclusion you draw (only what the evidence supports)
4. If coverage is low:
- Expand `--roots` to include the project workspace
- Increase `--topk`
- Fallback: targeted `rg -n` on the most likely file(s)
## Outputs
The script prints ranked evidence items:
- `path` + `line` (1-based) for quick navigation
- `score` for ranking
- `snippet` (short, safe to quote)
## Notes (non-redundancy)
- If you need **code call chains / blast radius**, use GitNexus overlays (not this).
- If you need **latest web facts**, use web search / deep research tools (not this).Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Full-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.
Unified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing.