[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6M9NF5NHFSCKS319CMEZ71Z":3,"skill-same:01M6M9NF5NHFSCKS319CMEZ71Z":115},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":65,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":68,"updatedAt":69,"install":70,"resources":82},"01M6M9NF5NHFSCKS319CMEZ71Z","alphagenome",null,"Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"RNA","2026","prediction","variants","track","functional","quantile","vcf","impact","free","google","deep","human","scoring","cap","later","edit","write","read","effect","website","2026-11-10 10:12:30",false,"---\nname: alphagenome\ndescription: Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.\nlicense: MIT\ncompatibility: Python 3.10+ with the alphagenome package (0.9.0 or later for the Atlas client; brings numpy, pandas, anndata, grpcio). Network access to gdmscience.googleapis.com:443 and a free non-commercial AlphaGenome API key in ALPHAGENOME_API_KEY (ALPHA_GENOME_API_KEY also read). Human data is GRCh38 only; mouse is mm10 (model only).\nallowed-tools: Read Write Edit Bash\nmetadata:\n  version: '1.0'\n  category: life-sciences\n  maintainer: Kalaris Labs\n  upstream-version: alphagenome 0.9.0\n  last-reviewed: '2026-09-13'\n  openclaw:\n    primaryEnv: ALPHAGENOME_API_KEY\n    envVars:\n    - name: ALPHAGENOME_API_KEY\n      required: true\n      description: AlphaGenome API key, free for non-commercial use from deepmind.google.com\u002Fscience\u002Falphagenome. ALPHA_GENOME_API_KEY is accepted as an alternative spelling.\n  tags: variant effect prediction, genomics, regulatory genomics, GRCh38, functional annotation\n---\n\n# AlphaGenome and the AlphaGenome Atlas\n\nAlphaGenome is DeepMind's sequence-to-function model: 1 Mb of DNA in, base-pair\npredictions for eleven assay types across thousands of human and mouse tracks\nout. The **AlphaGenome Atlas** (released 2026-09-08) is that model run once over\nevery possible single-nucleotide change in GRCh38, about 9 billion variants,\nstored with a single ranking number, the **AlphaGenome Variant Impact (AVI)**\nscore, its genome-wide percentile, and an 18-way attribution of what drives it.\nBoth are reached through one `pip install alphagenome` and one API key.\n\n> Research and theoretical modelling only. Outputs must not be used to train\n> other models, and are not for diagnostic procedures or medical decisions.\n\n## When to use which\n\n| You have | Use | Why |\n| --- | --- | --- |\n| hg38 SNVs (a VCF, a credible set, a region up to ~1 kb) | **Atlas** via `scripts\u002Fatlas_query.py` | precomputed, higher quota, includes AVI and attributions |\n| indels, mouse variants, a non-reference background, a custom scorer or window | **model** via `scripts\u002Fscore_variants.py` or Python | the Atlas is SNV-only and hg38-only |\n| a hypothesis to explain (which motif, which tissue, REF vs ALT tracks) | model `predict_variant` + plots, Atlas track scores, portal link | mechanism, not just rank |\n| GRCh37 coordinates, rsIDs, unnormalised indels | `genomic-coordinates` first, then come back | wrong build or swapped REF gives a plausible wrong answer |\n| ClinVar assertions, gene-disease validity, ACMG framing | `folklore-variant-evidence`, `database-lookup` | AlphaGenome is one evidence line, never the verdict |\n| promoter\u002Fenhancer\u002Fexpression predictions without a DeepMind key | `genomic-intelligence` | different provider, keyless demo tier |\n\n## Setup\n\n```bash\nuv pip install alphagenome                     # PyPI; tested on Python 3.12 and 3.13, alphagenome 0.9.0\nexport ALPHAGENOME_API_KEY=\"...\"               # https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome\ncd skills\u002Falphagenome\u002Fscripts\npython atlas_query.py scorers                  # proves key + network in one call\n```\n\nNever put the key on a command line or in a file you commit; the scripts only\nread it from the environment. An invalid key surfaces as `ValueError: API key\nnot valid`, not as a permission error.\n\n## The coordinate contract\n\n- A variant is **1-based** `chr:pos:ref>alt` (`chr22:36201698:A>C`). gnomAD\n  (`22-36201698-A-C`), GTEx (`chr22_36201698_A_C_b38`), and Open Targets\n  spellings are accepted by the scripts and by `genome.Variant.from_str`.\n- An interval on the command line is **1-based closed** `chr:start-end`; the\n  SDK's `genome.Interval` is **0-based half-open**. The scripts convert.\n- Human is **GRCh38 only**. The Atlas key is `chr:pos:alt`; REF is implied by\n  the reference, so a variant with REF and ALT swapped, or on GRCh37, returns a\n  wrong record silently. Check REF against the FASTA before trusting a lookup.\n- rsIDs are not accepted by the API or the portal. Resolve them to coordinates.\n- Use the `chr` prefix; `MT` becomes `chrM`.\n\n## Atlas workflow\n\n### 1. Rank with AVI\n\n```bash\npython atlas_query.py avi --variant chr22:36201698:A>C chr9:128225994:G>A\npython atlas_query.py avi --input candidates.vcf --min-phred 20 -o avi.tsv\npython atlas_query.py avi --interval chr11:5225727-5226575 --top-k 25 -o hbb_window.tsv\npython atlas_query.py avi --input credible_set.tsv --with-tracks -o avi_tracks.tsv\n```\n\nOutput, one row per variant:\n\n| Column | Meaning |\n| --- | --- |\n| `avi_raw` | composite model output (the 18 attributions sum to it) |\n| `avi_cdf_quantile` | cumulative quantile against all genome-wide SNVs, as served |\n| `avi_tail_quantile`, `avi_phred`, `avi_top_percent` | `tail = 1 - cdf`, `phred = -10 log10(tail)`; Phred 20 = top 1 %, 30 = top 0.1 % |\n| `top_feature`, `top_feature_value` | largest absolute SHAP attribution and its value |\n| `fi_MERGED_SPLICING` ... `fi_IS_DELETION` | all 18 attributions (keys in `references\u002Fatlas.md`) |\n| `top_track_*` (with `--with-tracks`) | the strongest track behind the top feature: scorer, track, biosample, ontology CURIE, gene, raw score |\n| `atlas_url` | deep link to the variant on the portal |\n| `error` | per-variant lookup failure (indel, `N` base, wrong REF) instead of a crash |\n\nThe Atlas report's advice: **rank, do not threshold**, and pick thresholds by\nregion or application. Pathogenic regulatory variants sit in lower AVI bins\nthan protein-truncating or splice-motif variants, so a single genome-wide\ncut-off under-calls exactly the variants this resource was built for.\n\nRead the attribution before the number. `MERGED_SPLICING` or `ALPHAMISSENSE`\non top means a splice or coding mechanism; `MAX_ABS_DNASE`, `MAX_ABS_CHIP_TF`,\n`MAX_ABS_RNA_SEQ` mean a regulatory mechanism you can resolve by track;\n`CACTUS_241_WAY` or `PHASTCONS_470_WAY` on top means conservation is carrying\nthe score and the molecular mechanism is not resolved.\n\n### 2. Resolve the mechanism by track\n\n```bash\npython atlas_query.py scorers                                   # what the server serves right now\npython atlas_query.py tracks --scorer RNA_SEQ --query colon     # find ontology CURIEs\npython atlas_query.py scores --variant chr22:36201698:A>C \\\n    --scorers RNA_SEQ DNASE SPLICE_SITE_USAGE --ontology UBERON:0001157 -o colon.tsv\npython atlas_query.py scores --interval chr11:5225727-5226575 --scorers CHIP_TF --gene HBB -o hbb_tf.tsv\n```\n\nOne row per variant x track (x gene for `RNA_SEQ`, `POLYADENYLATION`,\n`SPLICE_*`), with `raw_score` and, where served, `quantile_score`. Track-level\nscorer names: `ATAC`, `DNASE`, `CHIP_TF`, `CHIP_HISTONE`, `CAGE`, `PROCAP`,\n`RNA_SEQ`, `POLYADENYLATION`, `SPLICE_SITES`, `SPLICE_SITE_USAGE`,\n`SPLICE_JUNCTIONS`, `CONTACT_MAPS`, plus `*_ACTIVE` variants; `scorers` is the\nauthority on the live list. Filter by the tissue the question is about, not\nby the genome-wide maximum: 9,440 tracks means something is always extreme\nsomewhere.\n\n### 3. Send the reader to the portal\n\n```bash\npython atlas_link.py variant chr22:36201698:A>C --biosample \"colon\" --modalities RNA_SEQ,DNASE,CHIP_TF\npython atlas_link.py locus chr11:5225727-5226575 --tf GATA1\npython atlas_link.py gene HBB --markdown\n```\n\nNo key, no network. The site shows the AVI track, per-modality heatmaps over\nevery biosample, REF-vs-ALT prediction tracks, and motif instances. Attach a\nlink to every variant you report.\n\n### In Python\n\n```python\nimport os\nfrom alphagenome.atlas import atlas\nfrom alphagenome.data import genome\n\nclient = atlas.create(os.environ[\"ALPHAGENOME_API_KEY\"], timeout=30)\nscores = client.query_variant(\n    genome.Variant.from_str(\"chr22:36201698:A>C\"),\n    requested_scorers=[\"AVI_SCORE\", \"AVI_SCORE_FEATURE_IMPORTANCE\", \"RNA_SEQ\"],\n    ontology_terms=[\"UBERON:0001157\"],          # optional; ignored for scorers without ontology metadata\n)\navi = scores[\"AVI_SCORE\"]                        # AnnData: X (1,1) raw; layers['quantiles'] (1,1) cdf\nfi = scores[\"AVI_SCORE_FEATURE_IMPORTANCE\"]      # AnnData: X (1,18); var['name'] = feature keys\nrna = scores[\"RNA_SEQ\"]                          # AnnData: obs = variant x gene, var = tracks, X = log2 FC\nclient.query_interval(genome.Interval(\"chr11\", 5225726, 5226575), requested_scorers=[\"AVI_SCORE\"])\n```\n\n`query_interval` returns all 3 SNVs per base, in 32 bp chunks. Keep windows\nto about 1 kb (3,000 variants); `atlas_query.py` refuses more unless\n`--max-window` is raised. `query_variants` stops at the first failed lookup;\nthe script queries one variant at a time so misses become `error` cells.\n\n## Model workflow\n\n### Score variants the Atlas does not hold\n\n```bash\npython score_variants.py --variant chr22:36201698:A>C -o scores.tsv                   # 12 recommended scorers, 1 Mb\npython score_variants.py --input indels.vcf --scorers RNA_SEQ SPLICE_SITE_USAGE \\\n    --ontology UBERON:0001157 --min-abs-quantile 0.99 -o colon.tsv\npython score_variants.py --organism mouse --variant chr7:45000000:A>G --sequence-length 500KB\npython score_variants.py --list-scorers\npython score_variants.py --list-tracks --output-type RNA_SEQ --query liver -o tracks.tsv\n```\n\nOutput is the official tidy table from `variant_scorers.tidy_scores`: one row\nper variant x scorer x track (x gene) with `raw_score` and `quantile_score`,\nsorted by |raw|. Default scorers are the 12 recommended difference scorers;\n`--include-active` adds the seven `*_ACTIVE` activity scorers. At most 20\nscorers per request.\n\n```python\nfrom alphagenome.models import dna_client, variant_scorers\nmodel = dna_client.create(os.environ[\"ALPHAGENOME_API_KEY\"])\nvariant = genome.Variant.from_str(\"chr22:36201698:A>C\")\ninterval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB)\nadatas = model.score_variant(interval, variant, variant_scorers=[variant_scorers.RECOMMENDED_VARIANT_SCORERS[\"RNA_SEQ\"]])\ndf = variant_scorers.tidy_scores(adatas)         # filter df.ontology_curie afterwards; score_variant takes no ontology_terms\n```\n\n### Predict tracks and mutagenise\n\n```python\nvo = model.predict_variant(interval, variant,\n                           requested_outputs=[dna_client.OutputType.RNA_SEQ, dna_client.OutputType.DNASE],\n                           ontology_terms=[\"UBERON:0001157\"])\nvo.reference.rna_seq.values, vo.alternate.rna_seq.values      # (1048576, n_tracks)\n\nwindow = genome.Interval(\"chr20\", 3_753_000, 3_753_400).resize(dna_client.SEQUENCE_LENGTH_16KB)\nism = model.score_ism_variants(interval=window, ism_interval=window.resize(256),\n                               variant_scorers=[variant_scorers.CenterMaskScorer(\n                                   requested_output=dna_client.OutputType.DNASE, width=501,\n                                   aggregation_type=variant_scorers.AggregationType.DIFF_MEAN)])\n```\n\nSupported windows: 16 kb, 100 kb, 500 kb, 1 Mb (`2**14` to `2**20`); 1 Mb is\nthe default and is required for distal enhancers and contact maps. Ontology\nterms are CURIEs (`UBERON:0002048` lung, `CL:0000084` T cell); discover them\nwith `--list-tracks` or `model.output_metadata(...).concatenate()`. Plotting,\ngene annotation (GENCODE v46 Feather on GCS), splicing and haplotype recipes:\n`references\u002Fmodel-api.md`.\n\n## Reading the numbers\n\nAlways report raw score **and** quantile or Phred, with the scorer, track,\nbiosample CURIE, and gene. `raw_score` is the effect size on the scorer's scale\n(RNA_SEQ is log2 fold change: -1 is half); `quantile_score` is the rank against\ncommon variants and saturates near 0.99999. A quantile above 0.99 with |raw| \u003C\n0.1 is the standard artefact of a quiet region and means **no effect**. Unsigned\nscorers (`SPLICE_*`, `POLYADENYLATION`, `CONTACT_MAPS`, `*_ACTIVE`) have no\ndirection. Most variants are benign; \"AlphaGenome predicts no molecular effect\"\nis a complete answer, and a variant inside a peak whose REF and ALT tracks are\nidentical is not \"disrupting\" anything. Full rules, tissue matching, and the\nreporting checklist: `references\u002Finterpretation.md`.\n\nWhat the model cannot see: trans effects, non-polyadenylated RNAs (snRNA genes\nsuch as *RNU4-2*), cell types absent from training, protein-level consequences\n(AlphaMissense is folded into AVI for that), RNA structure and miRNA biology,\ndiploid dosage, developmental time, species other than human and mouse.\n\n## Limits, quota, terms\n\n- Atlas: GRCh38 SNVs only for now; indels were scored for the paper and are\n  promised later. Reference `N` bases were never scored.\n- Quotas are per key and unpublished; the Atlas is documented as having a\n  larger query rate than on-demand prediction. Transient `RESOURCE_EXHAUSTED`\n  and `UNAVAILABLE` are retried by the client (5 attempts, back-off to 60 s).\n- Access tiers (Atlas report): AVI scores are also a **permissively licensed**\n  Tabix download at https:\u002F\u002Falphagenome.google\u002Fdownloads; feature attributions\n  and splicing scores are non-commercial downloads; all other raw track scores\n  are API-only and non-commercial. Commercial API access is \"coming soon\" via\n  Google Cloud Model Garden.\n- The `alphagenome` client is Apache-2.0; model weights and outputs carry\n  DeepMind's terms. Cite Avsec et al., *Nature* 649:1206 (2026) and the Atlas\n  report (Cheng, Taylor, Nicolaisen, Pan, Bycroft, Perino, Ward et al., 2026).\n\n## References\n\n- `references\u002Fatlas.md` - what the Atlas contains, the 19 scorer\n  configurations with track counts, AVI training and the 18 features, quantile\n  to Phred, the client API and AnnData layout, error mapping, access tiers,\n  portal URL grammar, GTF and download locations.\n- `references\u002Fmodel-api.md` - `dna_client` cheat sheet: coordinates, sequence\n  lengths, output types and track counts, ontology metadata, predict and score\n  calls, recommended scorer configurations, ISM, gene annotation, plotting.\n- `references\u002Finterpretation.md` - raw versus quantile, AVI thresholds,\n  tissue matching, negative results, model blind spots, coordinate hygiene,\n  reporting checklist.\n- Scripts: `scripts\u002Fatlas_query.py` (Atlas: `avi`, `scores`, `scorers`,\n  `tracks`), `scripts\u002Fscore_variants.py` (model scoring, `--list-scorers`,\n  `--list-tracks`), `scripts\u002Fatlas_link.py` (portal deep links, offline).\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm tool versions, the reference genome\u002Fannotation build and the input formats (FASTQ, BAM, VCF, h5ad).\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Genome builds or identifiers do not match between inputs | Stop and harmonize (liftover, ID mapping) before continuing. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `shap`: Explain and audit machine-learning predictions with SHAP.\n","# AlphaGenome and the AlphaGenome Atlas\n\nAlphaGenome is DeepMind's sequence-to-function model: 1 Mb of DNA in, base-pair\npredictions for eleven assay types across thousands of human and mouse tracks\nout. The **AlphaGenome Atlas** (released 2026-09-08) is that model run once over\nevery possible single-nucleotide change in GRCh38, about 9 billion variants,\nstored with a single ranking number, the **AlphaGenome Variant Impact (AVI)**\nscore, its genome-wide percentile, and an 18-way attribution of what drives it.\nBoth are reached through one `pip install alphagenome` and one API key.\n\n> Research and theoretical modelling only. Outputs must not be used to train\n> other models, and are not for diagnostic procedures or medical decisions.\n\n## When to use which\n\n| You have | Use | Why |\n| --- | --- | --- |\n| hg38 SNVs (a VCF, a credible set, a region up to ~1 kb) | **Atlas** via `scripts\u002Fatlas_query.py` | precomputed, higher quota, includes AVI and attributions |\n| indels, mouse variants, a non-reference background, a custom scorer or window | **model** via `scripts\u002Fscore_variants.py` or Python | the Atlas is SNV-only and hg38-only |\n| a hypothesis to explain (which motif, which tissue, REF vs ALT tracks) | model `predict_variant` + plots, Atlas track scores, portal link | mechanism, not just rank |\n| GRCh37 coordinates, rsIDs, unnormalised indels | `genomic-coordinates` first, then come back | wrong build or swapped REF gives a plausible wrong answer |\n| ClinVar assertions, gene-disease validity, ACMG framing | `folklore-variant-evidence`, `database-lookup` | AlphaGenome is one evidence line, never the verdict |\n| promoter\u002Fenhancer\u002Fexpression predictions without a DeepMind key | `genomic-intelligence` | different provider, keyless demo tier |\n\n## Setup\n\n```bash\nuv pip install alphagenome                     # PyPI; tested on Python 3.12 and 3.13, alphagenome 0.9.0\nexport ALPHAGENOME_API_KEY=\"...\"               # https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome\ncd skills\u002Falphagenome\u002Fscripts\npython atlas_query.py scorers                  # proves key + network in one call\n```\n\nNever put the key on a command line or in a file you commit; the scripts only\nread it from the environment. An invalid key surfaces as `ValueError: API key\nnot valid`, not as a permission error.\n\n## The coordinate contract\n\n- A variant is **1-based** `chr:pos:ref>alt` (`chr22:36201698:A>C`). gnomAD\n  (`22-36201698-A-C`), GTEx (`chr22_36201698_A_C_b38`), and Open Targets\n  spellings are accepted by the scripts and by `genome.Variant.from_str`.\n- An interval on the command line is **1-based closed** `chr:start-end`; the\n  SDK's `genome.Interval` is **0-based half-open**. The scripts convert.\n- Human is **GRCh38 only**. The Atlas key is `chr:pos:alt`; REF is implied by\n  the reference, so a variant with REF and ALT swapped, or on GRCh37, returns a\n  wrong record silently. Check REF against the FASTA before trusting a lookup.\n- rsIDs are not accepted by the API or the portal. Resolve them to coordinates.\n- Use the `chr` prefix; `MT` becomes `chrM`.\n\n## Atlas workflow\n\n### 1. Rank with AVI\n\n```bash\npython atlas_query.py avi --variant chr22:36201698:A>C chr9:128225994:G>A\npython atlas_query.py avi --input candidates.vcf --min-phred 20 -o avi.tsv\npython atlas_query.py avi --interval chr11:5225727-5226575 --top-k 25 -o hbb_window.tsv\npython atlas_query.py avi --input credible_set.tsv --with-tracks -o avi_tracks.tsv\n```\n\nOutput, one row per variant:\n\n| Column | Meaning |\n| --- | --- |\n| `avi_raw` | composite model output (the 18 attributions sum to it) |\n| `avi_cdf_quantile` | cumulative quantile against all genome-wide SNVs, as served |\n| `avi_tail_quantile`, `avi_phred`, `avi_top_percent` | `tail = 1 - cdf`, `phred = -10 log10(tail)`; Phred 20 = top 1 %, 30 = top 0.1 % |\n| `top_feature`, `top_feature_value` | largest absolute SHAP attribution and its value |\n| `fi_MERGED_SPLICING` ... `fi_IS_DELETION` | all 18 attributions (keys in `references\u002Fatlas.md`) |\n| `top_track_*` (with `--with-tracks`) | the strongest track behind the top feature: scorer, track, biosample, ontology CURIE, gene, raw score |\n| `atlas_url` | deep link to the variant on the portal |\n| `error` | per-variant lookup failure (indel, `N` base, wrong REF) instead of a crash |\n\nThe Atlas report's advice: **rank, do not threshold**, and pick thresholds by\nregion or application. Pathogenic regulatory variants sit in lower AVI bins\nthan protein-truncating or splice-motif variants, so a single genome-wide\ncut-off under-calls exactly the variants this resource was built for.\n\nRead the attribution before the number. `MERGED_SPLICING` or `ALPHAMISSENSE`\non top means a splice or coding mechanism; `MAX_ABS_DNASE`, `MAX_ABS_CHIP_TF`,\n`MAX_ABS_RNA_SEQ` mean a regulatory mechanism you can resolve by track;\n`CACTUS_241_WAY` or `PHASTCONS_470_WAY` on top means conservation is carrying\nthe score and the molecular mechanism is not resolved.\n\n### 2. Resolve the mechanism by track\n\n```bash\npython atlas_query.py scorers                                   # what the server serves right now\npython atlas_query.py tracks --scorer RNA_SEQ --query colon     # find ontology CURIEs\npython atlas_query.py scores --variant chr22:36201698:A>C \\\n    --scorers RNA_SEQ DNASE SPLICE_SITE_USAGE --ontology UBERON:0001157 -o colon.tsv\npython atlas_query.py scores --interval chr11:5225727-5226575 --scorers CHIP_TF --gene HBB -o hbb_tf.tsv\n```\n\nOne row per variant x track (x gene for `RNA_SEQ`, `POLYADENYLATION`,\n`SPLICE_*`), with `raw_score` and, where served, `quantile_score`. Track-level\nscorer names: `ATAC`, `DNASE`, `CHIP_TF`, `CHIP_HISTONE`, `CAGE`, `PROCAP`,\n`RNA_SEQ`, `POLYADENYLATION`, `SPLICE_SITES`, `SPLICE_SITE_USAGE`,\n`SPLICE_JUNCTIONS`, `CONTACT_MAPS`, plus `*_ACTIVE` variants; `scorers` is the\nauthority on the live list. Filter by the tissue the question is about, not\nby the genome-wide maximum: 9,440 tracks means something is always extreme\nsomewhere.\n\n### 3. Send the reader to the portal\n\n```bash\npython atlas_link.py variant chr22:36201698:A>C --biosample \"colon\" --modalities RNA_SEQ,DNASE,CHIP_TF\npython atlas_link.py locus chr11:5225727-5226575 --tf GATA1\npython atlas_link.py gene HBB --markdown\n```\n\nNo key, no network. The site shows the AVI track, per-modality heatmaps over\nevery biosample, REF-vs-ALT prediction tracks, and motif instances. Attach a\nlink to every variant you report.\n\n### In Python\n\n```python\nimport os\nfrom alphagenome.atlas import atlas\nfrom alphagenome.data import genome\n\nclient = atlas.create(os.environ[\"ALPHAGENOME_API_KEY\"], timeout=30)\nscores = client.query_variant(\n    genome.Variant.from_str(\"chr22:36201698:A>C\"),\n    requested_scorers=[\"AVI_SCORE\", \"AVI_SCORE_FEATURE_IMPORTANCE\", \"RNA_SEQ\"],\n    ontology_terms=[\"UBERON:0001157\"],          # optional; ignored for scorers without ontology metadata\n)\navi = scores[\"AVI_SCORE\"]                        # AnnData: X (1,1) raw; layers['quantiles'] (1,1) cdf\nfi = scores[\"AVI_SCORE_FEATURE_IMPORTANCE\"]      # AnnData: X (1,18); var['name'] = feature keys\nrna = scores[\"RNA_SEQ\"]                          # AnnData: obs = variant x gene, var = tracks, X = log2 FC\nclient.query_interval(genome.Interval(\"chr11\", 5225726, 5226575), requested_scorers=[\"AVI_SCORE\"])\n```\n\n`query_interval` returns all 3 SNVs per base, in 32 bp chunks. Keep windows\nto about 1 kb (3,000 variants); `atlas_query.py` refuses more unless\n`--max-window` is raised. `query_variants` stops at the first failed lookup;\nthe script queries one variant at a time so misses become `error` cells.\n\n## Model workflow\n\n### Score variants the Atlas does not hold\n\n```bash\npython score_variants.py --variant chr22:36201698:A>C -o scores.tsv                   # 12 recommended scorers, 1 Mb\npython score_variants.py --input indels.vcf --scorers RNA_SEQ SPLICE_SITE_USAGE \\\n    --ontology UBERON:0001157 --min-abs-quantile 0.99 -o colon.tsv\npython score_variants.py --organism mouse --variant chr7:45000000:A>G --sequence-length 500KB\npython score_variants.py --list-scorers\npython score_variants.py --list-tracks --output-type RNA_SEQ --query liver -o tracks.tsv\n```\n\nOutput is the official tidy table from `variant_scorers.tidy_scores`: one row\nper variant x scorer x track (x gene) with `raw_score` and `quantile_score`,\nsorted by |raw|. Default scorers are the 12 recommended difference scorers;\n`--include-active` adds the seven `*_ACTIVE` activity scorers. At most 20\nscorers per request.\n\n```python\nfrom alphagenome.models import dna_client, variant_scorers\nmodel = dna_client.create(os.environ[\"ALPHAGENOME_API_KEY\"])\nvariant = genome.Variant.from_str(\"chr22:36201698:A>C\")\ninterval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB)\nadatas = model.score_variant(interval, variant, variant_scorers=[variant_scorers.RECOMMENDED_VARIANT_SCORERS[\"RNA_SEQ\"]])\ndf = variant_scorers.tidy_scores(adatas)         # filter df.ontology_curie afterwards; score_variant takes no ontology_terms\n```\n\n### Predict tracks and mutagenise\n\n```python\nvo = model.predict_variant(interval, variant,\n                           requested_outputs=[dna_client.OutputType.RNA_SEQ, dna_client.OutputType.DNASE],\n                           ontology_terms=[\"UBERON:0001157\"])\nvo.reference.rna_seq.values, vo.alternate.rna_seq.values      # (1048576, n_tracks)\n\nwindow = genome.Interval(\"chr20\", 3_753_000, 3_753_400).resize(dna_client.SEQUENCE_LENGTH_16KB)\nism = model.score_ism_variants(interval=window, ism_interval=window.resize(256),\n                               variant_scorers=[variant_scorers.CenterMaskScorer(\n                                   requested_output=dna_client.OutputType.DNASE, width=501,\n                                   aggregation_type=variant_scorers.AggregationType.DIFF_MEAN)])\n```\n\nSupported windows: 16 kb, 100 kb, 500 kb, 1 Mb (`2**14` to `2**20`); 1 Mb is\nthe default and is required for distal enhancers and contact maps. Ontology\nterms are CURIEs (`UBERON:0002048` lung, `CL:0000084` T cell); discover them\nwith `--list-tracks` or `model.output_metadata(...).concatenate()`. Plotting,\ngene annotation (GENCODE v46 Feather on GCS), splicing and haplotype recipes:\n`references\u002Fmodel-api.md`.\n\n## Reading the numbers\n\nAlways report raw score **and** quantile or Phred, with the scorer, track,\nbiosample CURIE, and gene. `raw_score` is the effect size on the scorer's scale\n(RNA_SEQ is log2 fold change: -1 is half); `quantile_score` is the rank against\ncommon variants and saturates near 0.99999. A quantile above 0.99 with |raw| \u003C\n0.1 is the standard artefact of a quiet region and means **no effect**. Unsigned\nscorers (`SPLICE_*`, `POLYADENYLATION`, `CONTACT_MAPS`, `*_ACTIVE`) have no\ndirection. Most variants are benign; \"AlphaGenome predicts no molecular effect\"\nis a complete answer, and a variant inside a peak whose REF and ALT tracks are\nidentical is not \"disrupting\" anything. Full rules, tissue matching, and the\nreporting checklist: `references\u002Finterpretation.md`.\n\nWhat the model cannot see: trans effects, non-polyadenylated RNAs (snRNA genes\nsuch as *RNU4-2*), cell types absent from training, protein-level consequences\n(AlphaMissense is folded into AVI for that), RNA structure and miRNA biology,\ndiploid dosage, developmental time, species other than human and mouse.\n\n## Limits, quota, terms\n\n- Atlas: GRCh38 SNVs only for now; indels were scored for the paper and are\n  promised later. Reference `N` bases were never scored.\n- Quotas are per key and unpublished; the Atlas is documented as having a\n  larger query rate than on-demand prediction. Transient `RESOURCE_EXHAUSTED`\n  and `UNAVAILABLE` are retried by the client (5 attempts, back-off to 60 s).\n- Access tiers (Atlas report): AVI scores are also a **permissively licensed**\n  Tabix download at https:\u002F\u002Falphagenome.google\u002Fdownloads; feature attributions\n  and splicing scores are non-commercial downloads; all other raw track scores\n  are API-only and non-commercial. Commercial API access is \"coming soon\" via\n  Google Cloud Model Garden.\n- The `alphagenome` client is Apache-2.0; model weights and outputs carry\n  DeepMind's terms. Cite Avsec et al., *Nature* 649:1206 (2026) and the Atlas\n  report (Cheng, Taylor, Nicolaisen, Pan, Bycroft, Perino, Ward et al., 2026).\n\n## References\n\n- `references\u002Fatlas.md` - what the Atlas contains, the 19 scorer\n  configurations with track counts, AVI training and the 18 features, quantile\n  to Phred, the client API and AnnData layout, error mapping, access tiers,\n  portal URL grammar, GTF and download locations.\n- `references\u002Fmodel-api.md` - `dna_client` cheat sheet: coordinates, sequence\n  lengths, output types and track counts, ontology metadata, predict and score\n  calls, recommended scorer configurations, ISM, gene annotation, plotting.\n- `references\u002Finterpretation.md` - raw versus quantile, AVI thresholds,\n  tissue matching, negative results, model blind spots, coordinate hygiene,\n  reporting checklist.\n- Scripts: `scripts\u002Fatlas_query.py` (Atlas: `avi`, `scores`, `scorers`,\n  `tracks`), `scripts\u002Fscore_variants.py` (model scoring, `--list-scorers`,\n  `--list-tracks`), `scripts\u002Fatlas_link.py` (portal deep links, offline).\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm tool versions, the reference genome\u002Fannotation build and the input formats (FASTQ, BAM, VCF, h5ad).\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Genome builds or identifiers do not match between inputs | Stop and harmonize (liftover, ID mapping) before continuing. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `shap`: Explain and audit machine-learning predictions with SHAP.\n",{"name":5,"license":15,"metadata":45,"description":7,"allowed-tools":58,"compatibility":59},{"tags":46,"version":47,"category":48,"openclaw":49,"maintainer":55,"last-reviewed":56,"upstream-version":57},"variant effect prediction, genomics, regulatory genomics, GRCh38, functional annotation","1.0","life-sciences",{"envVars":50,"primaryEnv":52},[51],{"name":52,"required":53,"description":54},"ALPHAGENOME_API_KEY",true,"AlphaGenome API key, free for non-commercial use from deepmind.google.com\u002Fscience\u002Falphagenome. ALPHA_GENOME_API_KEY is accepted as an alternative spelling.","Kalaris Labs","2026-09-13","alphagenome 0.9.0","Read Write Edit Bash","Python 3.10+ with the alphagenome package (0.9.0 or later for the Atlas client; brings numpy, pandas, anndata, grpcio). Network access to gdmscience.googleapis.com:443 and a free non-commercial AlphaGenome API key in ALPHAGENOME_API_KEY (ALPHA_GENOME_API_KEY also read). Human data is GRCh38 only; mouse is mm10 (model only).","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome",490,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-14 02:12:02",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":53},[72,75,78],{"tool":73,"command":74},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill alphagenome",{"tool":76,"command":77},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill alphagenome",{"tool":79,"command":80},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill alphagenome","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002FSKILL.md",[83,89,93,97,103,107,111],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fatlas.md","REFERENCE","参考文档",13484,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002Freferences\u002Fatlas.md",{"path":90,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Finterpretation.md",7409,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002Freferences\u002Finterpretation.md",{"path":94,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fmodel-api.md",11629,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002Freferences\u002Fmodel-api.md",{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"scripts\u002F_common.py","SCRIPT","脚本",19437,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002Fscripts\u002F_common.py",{"path":104,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":105,"sourceHtmlUrl":106},"scripts\u002Fatlas_link.py",7487,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002Fscripts\u002Fatlas_link.py",{"path":108,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":109,"sourceHtmlUrl":110},"scripts\u002Fatlas_query.py",23226,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002Fscripts\u002Fatlas_query.py",{"path":112,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fscore_variants.py",11188,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Falphagenome\u002Fscripts\u002Fscore_variants.py",[116,125,132,139,166,191],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":120,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":124},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":120,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":124},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":120,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":124},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":120,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":145,"publishedAt":40},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":120,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":172,"publishedAt":40},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[173,174,175,161,176,177,178,179,180,181,182,183,184,185,151,186,187,188,189,190],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":120,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":208,"publishedAt":40},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[197,202,205],{"categoryNo":198,"categoryName":199,"taxonomyType":200,"taxonomyTypeDesc":201},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":203,"categoryName":204,"taxonomyType":200,"taxonomyTypeDesc":201},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":206,"categoryName":207,"taxonomyType":200,"taxonomyTypeDesc":201},"01JBSEEDCAT000000000000010","后端与接口",[193,209,210,211,212,213,214,187,215,152,216,217,218,219,157,220,221,222,223,224],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]