[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QH3FXCJS9NGDHKYCVPGZH9":3,"skill-same:01M6QH3FXCJS9NGDHKYCVPGZH9":91},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":14,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6QH3FXCJS9NGDHKYCVPGZH9","nmrglue",null,"Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions. Use for raw 1D NMR processing, ppm-axis verification, apodization, Fourier transformation, manual phasing, baseline correction, or reproducible spectral integration.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkd-nmrglue\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"free","1d","for","manual","spectral","Fourier","processing","scientific","phasing","python","transformation","local","scipy","use","access","credentials","network","Reproducible","verification","2026-11-10 10:12:30",false,"---\nname: nmrglue\ndescription: Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions. Use for raw 1D NMR processing, ppm-axis verification, apodization, Fourier transformation, manual phasing, baseline correction, or reproducible spectral integration.\nlicense: MIT\ncompatibility: Requires Python 3.12+, nmrglue, NumPy 2+, and SciPy. Installation needs network access; processing is local and needs no credentials.\nmetadata:\n  version: \"1.1\"\n  skill-author: K-Dense Inc.\n  tested-package-version: \"0.12\"\n  last-reviewed: \"2026-10-01\"\ncategory: \"scientific-computing\"\n---\n\n# nmrglue: calibrated 1D FID processing\n\n## When to use\n\nUse for a uniformly sampled complex 1D FID whose acquisition parameters and complex\nfrequency convention are available. The helper produces a descending ppm spectrum,\npositive peak candidates, signed region integrals, and a reproducible processing report.\nIt does not identify compounds or assign resonances.\n\nThe executable accepts a NumPy `.npz` containing exactly one complex `fid` array or a\ncanonical 1D complex time-domain NMRPipe file. NMRPipe reading is tested with a synthetic\nwrite\u002Fread round trip, known-spectrum recovery, and a small upstream NMRPipe-generated\nbinary fixture. Experimental Bruker, Varian, and JEOL\nimports are **not verified by this suite**. For those formats, first inspect the relevant\nnmrglue reader and acquisition metadata. Opening a converted file does not validate the\noriginal acquisition decoding.\nRead [references\u002Facquisition-and-validation.md](references\u002Facquisition-and-validation.md)\nfor conversion boundaries, axis calibration, and quantitative limits.\n\n## Workflow\n\n1. Preserve the raw FID. Establish spectral width in Hz, positive observation frequency\n   in MHz, carrier in ppm, observed nucleus, and the sign convention from the acquisition\n   or a known reference. Determine whether digital-filter\u002Fgroup-delay removal has already\n   occurred. Do not infer these from array length or typical instrument defaults.\n2. Copy [assets\u002Fprocessing.json](assets\u002Fprocessing.json) and replace its synthetic example\n   values with the measured parameters and explicit processing choices. Its sign `-i`\n   means a resonance at offset `f = (ppm - carrier_ppm) * observation_mhz` has time\n   dependence `exp(-2*pi*i*f*t)`. Select `+i` only for the opposite convention; the helper\n   conjugates it before processing. Validate with a known reference peak.\n3. Choose nonnegative exponential line broadening (Hz), an even zero-filled size at\n   least as large as the acquired FID, first-point scaling, and phase angles.\n   Zero filling improves interpolation, not acquired spectral resolution.\n   First-point scaling `0.5` is suitable for the supplied causal synthetic example;\n   acquisition and prior preprocessing may require another value.\n4. Run the helper, inspect the real and imaginary spectra, and revise manual phase if\n   needed. `phase0_deg + phase1_deg * index \u002F zero_fill_points` is applied after FT;\n   index zero is the high-ppm edge. There is no implicit pivot or automatic phase estimate.\n5. Only fit a linear baseline when explicitly supplied ppm regions are signal-free.\n   Set `baseline` to `linear` and add `baseline_regions_ppm` containing at least two\n   regions. Inspect residuals and broad peaks; fitting through signals biases integrals.\n6. Compare peak positions with references, inspect peak candidates for artifacts, and\n   integrate specified regions. Report overlapped peaks as overlapped. Preserve negative\n   areas as diagnostic evidence of phase\u002Fbaseline problems instead of taking absolute values.\n\n## Execute\n\nTested with Python 3.12, nmrglue 0.12, NumPy 2.5.3, and SciPy 1.18.1:\n\n```bash\nuv run --no-project --python 3.12 --with nmrglue==0.12 --with numpy==2.5.3 --with scipy==1.18.1 \\\n  python skills\u002Fnmrglue\u002Fscripts\u002Fprocess_1d.py fid.npz processing.json nmr-result\n```\n\nPaths assume the collection root. Adjust them when installed elsewhere. The output\ndirectory must be new, so repeated processing keeps previous results reviewable.\n\nFor an existing 1D NMRPipe FID, add `--input-format nmrpipe` and supply its path in place\nof `fid.npz`. The helper requires the canonical FDF2 direct dimension, complex quadrature,\na time-domain flag, and agreement between header and JSON spectral width, observation\nfrequency, and carrier. JSON settings remain explicit; a mismatch fails instead of silently\nrecalibrating. `FDF2TDSIZE` must equal the stored complex-point count, and `FDF2CENTER` \u002F\n`FDF2ORIG` must describe a canonical centered axis. Previously zero-filled, truncated,\nor recentered files need a separate acquisition-aware workflow. The nucleus\u002Fcomplex sign\nand previous digital-filter corrections still need acquisition evidence. A time-domain\nflag alone does not establish an unprocessed FID.\n\nThis executable synthetic example matches the supplied settings, generates resonances\nat 3 and 7 ppm in a 1:2 amplitude ratio, and does not represent an experimental sample:\n\n```python\nimport numpy as np\n\nt = np.arange(8192) \u002F 4000.0\nfid = sum(a * np.exp(-np.pi * 2.0 * t)\n          * np.exp(-2j * np.pi * (ppm - 5.0) * 400.0 * t)\n          for ppm, a in [(3.0, 1.0), (7.0, 2.0)])\nnp.savez(\"fid.npz\", fid=fid)\n```\n\nRun it with `assets\u002Fprocessing.json` as the settings argument. The repository suite\nexecutes this signal and the CLI, checks both peak locations within 0.001 ppm, checks\nintegral ratio and analytic area, and checks phase and baseline recovery. The NMRPipe\nround-trip test writes this FID using `ng.pipe.create_dic`\u002F`ng.pipe.write`, reads it through\nthe CLI, and verifies the recovered peaks and integral ratio. Processed frequency-domain\nfiles and conflicting calibration metadata are rejected.\n\nThe 2,176-byte upstream fixture checks complex sample order and header calibration using\na file generated by NMRPipe's `simTimeND` \u002F `SET` tools. Those native tools were not run\nin this review; this is fixture compatibility, not a live NMRPipe processing comparison.\n\n## Deliverables and interpretation\n\n- `spectrum.csv`: descending ppm, real signal after baseline correction, phased imaginary\n  signal, and the fitted real baseline. Plot NMR with the high-ppm end on the left.\n- `report.json`: input\u002Fsettings SHA-256, package versions, all settings, acquired duration,\n  zero-filled digital spacing, positive peak candidates, and signed region areas.\n\nIntegrals use endpoint interpolation and trapezoidal integration along increasing ppm;\narea units are arbitrary signal times ppm, independent of display direction. Regions\noutside the sampled ppm axis fail rather than being silently clipped. Peak prominence\nis a fraction of the largest positive real intensity; it is not a noise-derived detection\nlimit. Strong solvent signals can obscure weak candidates at the default threshold.\n\nFor quantitative NMR, additionally establish relaxation delay, pulse angle, saturation,\nreceiver behavior, internal\u002Fexternal reference amount, and integration uncertainty.\nThe helper does not calculate concentrations or correct unequal relaxation. Preserve\nthese limits with the result rather than converting arbitrary areas to molecule counts.\n\n## Upstream contracts\n\n- [Processing functions](https:\u002F\u002Fnmrglue.readthedocs.io\u002Fen\u002Flatest\u002Freference\u002Fproc_base.html):\n  exponential apodization, zero filling, FFT, and phase operations.\n- [File and axis utilities](https:\u002F\u002Fnmrglue.readthedocs.io\u002Fen\u002Flatest\u002Freference\u002Ffileiobase.html):\n  unit conversion uses spectral width in Hz, observation frequency in MHz, carrier in Hz.\n- [NMRPipe reader\u002Fwriter](https:\u002F\u002Fnmrglue.readthedocs.io\u002Fen\u002Flatest\u002Freference\u002Fpipe.html):\n  the validated ingestion path is a synthetic canonical 1D time-domain round trip.\n- [nmrglue project](https:\u002F\u002Fgithub.com\u002Fjjhelmus\u002Fnmrglue): upstream source and format readers.\n\nThe hosted `latest` documentation identified itself as 0.9-dev when checked; the actual\n0.12 package APIs and numerical behavior were tested. Its `proc_base.fft` uses the\nnegative-exponent NumPy FFT followed by `fftshift`; NMRPipe's FT convention corresponds\nto `fft_positive`, so do not substitute it without revisiting the FID sign and phase.\nSee the [v0.12 processing source](https:\u002F\u002Fgithub.com\u002Fjjhelmus\u002Fnmrglue\u002Fblob\u002Fv0.12\u002Fnmrglue\u002Fprocess\u002Fproc_base.py).\nMultidimensional processing, nonuniform sampling, automated assignment, and experimental vendor imports remain outside\nthis helper's validated scope.\n","# nmrglue: calibrated 1D FID processing\n\n## When to use\n\nUse for a uniformly sampled complex 1D FID whose acquisition parameters and complex\nfrequency convention are available. The helper produces a descending ppm spectrum,\npositive peak candidates, signed region integrals, and a reproducible processing report.\nIt does not identify compounds or assign resonances.\n\nThe executable accepts a NumPy `.npz` containing exactly one complex `fid` array or a\ncanonical 1D complex time-domain NMRPipe file. NMRPipe reading is tested with a synthetic\nwrite\u002Fread round trip, known-spectrum recovery, and a small upstream NMRPipe-generated\nbinary fixture. Experimental Bruker, Varian, and JEOL\nimports are **not verified by this suite**. For those formats, first inspect the relevant\nnmrglue reader and acquisition metadata. Opening a converted file does not validate the\noriginal acquisition decoding.\nRead [references\u002Facquisition-and-validation.md](references\u002Facquisition-and-validation.md)\nfor conversion boundaries, axis calibration, and quantitative limits.\n\n## Workflow\n\n1. Preserve the raw FID. Establish spectral width in Hz, positive observation frequency\n   in MHz, carrier in ppm, observed nucleus, and the sign convention from the acquisition\n   or a known reference. Determine whether digital-filter\u002Fgroup-delay removal has already\n   occurred. Do not infer these from array length or typical instrument defaults.\n2. Copy [assets\u002Fprocessing.json](assets\u002Fprocessing.json) and replace its synthetic example\n   values with the measured parameters and explicit processing choices. Its sign `-i`\n   means a resonance at offset `f = (ppm - carrier_ppm) * observation_mhz` has time\n   dependence `exp(-2*pi*i*f*t)`. Select `+i` only for the opposite convention; the helper\n   conjugates it before processing. Validate with a known reference peak.\n3. Choose nonnegative exponential line broadening (Hz), an even zero-filled size at\n   least as large as the acquired FID, first-point scaling, and phase angles.\n   Zero filling improves interpolation, not acquired spectral resolution.\n   First-point scaling `0.5` is suitable for the supplied causal synthetic example;\n   acquisition and prior preprocessing may require another value.\n4. Run the helper, inspect the real and imaginary spectra, and revise manual phase if\n   needed. `phase0_deg + phase1_deg * index \u002F zero_fill_points` is applied after FT;\n   index zero is the high-ppm edge. There is no implicit pivot or automatic phase estimate.\n5. Only fit a linear baseline when explicitly supplied ppm regions are signal-free.\n   Set `baseline` to `linear` and add `baseline_regions_ppm` containing at least two\n   regions. Inspect residuals and broad peaks; fitting through signals biases integrals.\n6. Compare peak positions with references, inspect peak candidates for artifacts, and\n   integrate specified regions. Report overlapped peaks as overlapped. Preserve negative\n   areas as diagnostic evidence of phase\u002Fbaseline problems instead of taking absolute values.\n\n## Execute\n\nTested with Python 3.12, nmrglue 0.12, NumPy 2.5.3, and SciPy 1.18.1:\n\n```bash\nuv run --no-project --python 3.12 --with nmrglue==0.12 --with numpy==2.5.3 --with scipy==1.18.1 \\\n  python skills\u002Fnmrglue\u002Fscripts\u002Fprocess_1d.py fid.npz processing.json nmr-result\n```\n\nPaths assume the collection root. Adjust them when installed elsewhere. The output\ndirectory must be new, so repeated processing keeps previous results reviewable.\n\nFor an existing 1D NMRPipe FID, add `--input-format nmrpipe` and supply its path in place\nof `fid.npz`. The helper requires the canonical FDF2 direct dimension, complex quadrature,\na time-domain flag, and agreement between header and JSON spectral width, observation\nfrequency, and carrier. JSON settings remain explicit; a mismatch fails instead of silently\nrecalibrating. `FDF2TDSIZE` must equal the stored complex-point count, and `FDF2CENTER` \u002F\n`FDF2ORIG` must describe a canonical centered axis. Previously zero-filled, truncated,\nor recentered files need a separate acquisition-aware workflow. The nucleus\u002Fcomplex sign\nand previous digital-filter corrections still need acquisition evidence. A time-domain\nflag alone does not establish an unprocessed FID.\n\nThis executable synthetic example matches the supplied settings, generates resonances\nat 3 and 7 ppm in a 1:2 amplitude ratio, and does not represent an experimental sample:\n\n```python\nimport numpy as np\n\nt = np.arange(8192) \u002F 4000.0\nfid = sum(a * np.exp(-np.pi * 2.0 * t)\n          * np.exp(-2j * np.pi * (ppm - 5.0) * 400.0 * t)\n          for ppm, a in [(3.0, 1.0), (7.0, 2.0)])\nnp.savez(\"fid.npz\", fid=fid)\n```\n\nRun it with `assets\u002Fprocessing.json` as the settings argument. The repository suite\nexecutes this signal and the CLI, checks both peak locations within 0.001 ppm, checks\nintegral ratio and analytic area, and checks phase and baseline recovery. The NMRPipe\nround-trip test writes this FID using `ng.pipe.create_dic`\u002F`ng.pipe.write`, reads it through\nthe CLI, and verifies the recovered peaks and integral ratio. Processed frequency-domain\nfiles and conflicting calibration metadata are rejected.\n\nThe 2,176-byte upstream fixture checks complex sample order and header calibration using\na file generated by NMRPipe's `simTimeND` \u002F `SET` tools. Those native tools were not run\nin this review; this is fixture compatibility, not a live NMRPipe processing comparison.\n\n## Deliverables and interpretation\n\n- `spectrum.csv`: descending ppm, real signal after baseline correction, phased imaginary\n  signal, and the fitted real baseline. Plot NMR with the high-ppm end on the left.\n- `report.json`: input\u002Fsettings SHA-256, package versions, all settings, acquired duration,\n  zero-filled digital spacing, positive peak candidates, and signed region areas.\n\nIntegrals use endpoint interpolation and trapezoidal integration along increasing ppm;\narea units are arbitrary signal times ppm, independent of display direction. Regions\noutside the sampled ppm axis fail rather than being silently clipped. Peak prominence\nis a fraction of the largest positive real intensity; it is not a noise-derived detection\nlimit. Strong solvent signals can obscure weak candidates at the default threshold.\n\nFor quantitative NMR, additionally establish relaxation delay, pulse angle, saturation,\nreceiver behavior, internal\u002Fexternal reference amount, and integration uncertainty.\nThe helper does not calculate concentrations or correct unequal relaxation. Preserve\nthese limits with the result rather than converting arbitrary areas to molecule counts.\n\n## Upstream contracts\n\n- [Processing functions](https:\u002F\u002Fnmrglue.readthedocs.io\u002Fen\u002Flatest\u002Freference\u002Fproc_base.html):\n  exponential apodization, zero filling, FFT, and phase operations.\n- [File and axis utilities](https:\u002F\u002Fnmrglue.readthedocs.io\u002Fen\u002Flatest\u002Freference\u002Ffileiobase.html):\n  unit conversion uses spectral width in Hz, observation frequency in MHz, carrier in Hz.\n- [NMRPipe reader\u002Fwriter](https:\u002F\u002Fnmrglue.readthedocs.io\u002Fen\u002Flatest\u002Freference\u002Fpipe.html):\n  the validated ingestion path is a synthetic canonical 1D time-domain round trip.\n- [nmrglue project](https:\u002F\u002Fgithub.com\u002Fjjhelmus\u002Fnmrglue): upstream source and format readers.\n\nThe hosted `latest` documentation identified itself as 0.9-dev when checked; the actual\n0.12 package APIs and numerical behavior were tested. Its `proc_base.fft` uses the\nnegative-exponent NumPy FFT followed by `fftshift`; NMRPipe's FT convention corresponds\nto `fft_positive`, so do not substitute it without revisiting the FID sign and phase.\nSee the [v0.12 processing source](https:\u002F\u002Fgithub.com\u002Fjjhelmus\u002Fnmrglue\u002Fblob\u002Fv0.12\u002Fnmrglue\u002Fprocess\u002Fproc_base.py).\nMultidimensional processing, nonuniform sampling, automated assignment, and experimental vendor imports remain outside\nthis helper's validated scope.\n",{"name":5,"license":15,"category":43,"metadata":44,"description":7,"compatibility":49},"scientific-computing",{"version":45,"skill-author":46,"last-reviewed":47,"tested-package-version":48},"1.1","K-Dense Inc.","2026-10-01","0.12","Requires Python 3.12+, nmrglue, NumPy 2+, and SciPy. Installation needs network access; processing is local and needs no credentials.","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkd-nmrglue","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill nmrglue",{"tool":65,"command":66},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill nmrglue",{"tool":68,"command":69},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill nmrglue","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkd-nmrglue\u002FSKILL.md",true,[73,79,85],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"assets\u002Fprocessing.json","ASSET","资产文件",364,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkd-nmrglue\u002Fassets\u002Fprocessing.json",{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Facquisition-and-validation.md","REFERENCE","参考文档",6157,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkd-nmrglue\u002Freferences\u002Facquisition-and-validation.md",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fprocess_1d.py","SCRIPT","脚本",10787,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkd-nmrglue\u002Fscripts\u002Fprocess_1d.py",[92,123,147,174,199,223],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":103,"publishedAt":38},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,21,120,121,122],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":38},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[130,131,132,133,108,134,135,136,137,138,139,140,141,142,32,143,144,145,146,21],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","platform","grow","posting","competitive",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":156,"publishedAt":38},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[153],{"categoryNo":154,"categoryName":155,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000023","Skill与智能体开发",[157,141,158,159,160,161,162,163,32,164,165,50,166,167,168,169,170,171,172,173],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":180,"publishedAt":38},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[181,182,183,184,185,186,187,188,189,32,190,115,191,192,193,194,195,196,197,198],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":206,"publishedAt":38},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[205],{"categoryNo":154,"categoryName":155,"taxonomyType":101,"taxonomyTypeDesc":102},[207,208,209,210,211,159,186,212,213,214,215,216,141,217,114,218,219,220,221,222],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":227,"categories":228,"keywords":229,"publishedAt":38},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[230,231,232,233,234,235,236,237,238,215,239,240,241,242,243,244,116,245,246,198],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]