Use when merging a release-bearing PR, writing a PR title for tensor-grep, diagnosing "why didn't my release publish" or "why hasn't npm/the docs site updated", running the post-publish dogfood, or making any external-facing speed/GPU/LSP/benchmark claim about tg. Covers semantic-release mechanics in ci.yml, the push-race + one-merge-per-tick discipline (worked example: the 384-399 sequence), the npm/docs manual-dispatch publish gap, PyPI/npm/Homebrew/winget publish gates, and the not-faster-grep positioning + reproducibility standard for comparator claims. As of 2026-07-24, v1.95.0.
EnglishUse when you have a hunch, a candidate optimization, a novel technique, or an "I think X is faster/better/possible" idea for tensor-grep and must turn it into an ACCEPTED result or a DOCUMENTED RETIREMENT — the research discipline. Covers the evidence bar (one mechanism must explain every observation INCLUDING the negatives, and survive an assigned adversarial refutation whose every claim cites file:line), predicting the number + noise band BEFORE you run, the idea lifecycle (experimental default-OFF -> dogfood/benchmark -> council-verify -> conscious flag-flip to adopted OR retirement recorded in docs/PAPER.md so it is never retried), verifying an AI-drafted plan against real code, and where good ideas actually come from. The scientific-method META sibling to change-control (gates) and research-frontier (targets).
EnglishUse when building, extending, or reviewing tensor-grep's APPROVED local hybrid semantic search — BM25 + CPU dense embeddings fused with Reciprocal Rank Fusion (RRF), no API key, no GPU (roadmap item 1). Load before adding a dense/embedding leg, RRF fusion, a tg index command, or changing tg search --rank / --bm25. Covers the decision-gated build phases with exact commands + expected gate numbers, the ranked solution menu (Semble / ripvec / BM25-only) with derivation obligations, the retrieval-quality + editor-plane + token-economy promotion gates, the Backend Fail-Closed Contract for the dense leg, fenced-off wrong paths (no API-key embeddings, no GPU dependency, do not break --format rg / --json / --ndjson semantics), and routing promotion through change-control. STATUS as of 2026-07-08, v1.49.3: the dense leg + RRF fusion described here as the target architecture (Candidate 1 / the Semble pattern) SHIPPED as tg search --semantic (retrievaldense.py + retrievalfusion.py, default-OFF, gated on the semantic extra) — this skill's Phases 0-3 are now historical/reference for HOW it was built; see the STATUS note below before assuming any "not built yet" claim in Sections 1-2.
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AGENT SKILLS ATLASIncludes Tensor Grep Release and Positioning, Tensor Grep Research Methodology, and Tensor Grep Semantic Search Campaign.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLISupported by the Atlas install prompt. Skill-specific requirements vary.
Pack stats
What’s included
Tensor Grep Release and Positioning
Use when merging a release-bearing PR, writing a PR title for tensor-grep, diagnosing "why didn't my release publish" or "why hasn't npm/the docs site updated", running the post-publish dogfood, or making any external-facing speed/GPU/LSP/benchmark claim about tg. Covers semantic-release mechanics in ci.yml, the push-race + one-merge-per-tick discipline (worked example: the 384-399 sequence), the npm/docs manual-dispatch publish gap, PyPI/npm/Homebrew/winget publish gates, and the not-faster-grep positioning + reproducibility standard for comparator claims. As of 2026-07-24, v1.95.0.
Tensor Grep Research Methodology
Use when you have a hunch, a candidate optimization, a novel technique, or an "I think X is faster/better/possible" idea for tensor-grep and must turn it into an ACCEPTED result or a DOCUMENTED RETIREMENT — the research discipline. Covers the evidence bar (one mechanism must explain every observation INCLUDING the negatives, and survive an assigned adversarial refutation whose every claim cites file:line), predicting the number + noise band BEFORE you run, the idea lifecycle (experimental default-OFF -> dogfood/benchmark -> council-verify -> conscious flag-flip to adopted OR retirement recorded in docs/PAPER.md so it is never retried), verifying an AI-drafted plan against real code, and where good ideas actually come from. The scientific-method META sibling to change-control (gates) and research-frontier (targets).
Tensor Grep Semantic Search Campaign
Use when building, extending, or reviewing tensor-grep's APPROVED local hybrid semantic search — BM25 + CPU dense embeddings fused with Reciprocal Rank Fusion (RRF), no API key, no GPU (roadmap item 1). Load before adding a dense/embedding leg, RRF fusion, a tg index command, or changing tg search --rank / --bm25. Covers the decision-gated build phases with exact commands + expected gate numbers, the ranked solution menu (Semble / ripvec / BM25-only) with derivation obligations, the retrieval-quality + editor-plane + token-economy promotion gates, the Backend Fail-Closed Contract for the dense leg, fenced-off wrong paths (no API-key embeddings, no GPU dependency, do not break --format rg / --json / --ndjson semantics), and routing promotion through change-control. STATUS as of 2026-07-08, v1.49.3: the dense leg + RRF fusion described here as the target architecture (Candidate 1 / the Semble pattern) SHIPPED as tg search --semantic (retrievaldense.py + retrievalfusion.py, default-OFF, gated on the semantic extra) — this skill's Phases 0-3 are now historical/reference for HOW it was built; see the STATUS note below before assuming any "not built yet" claim in Sections 1-2.
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