[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69A7S5DFPGSEFRYD79FATVF":3,"skill-same:01M69A7S5DFPGSEFRYD79FATVF":86},{"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":26,"publishedAt":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":40,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":43,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M69A7S5DFPGSEFRYD79FATVF","phi-deidentifier",null,"Use this skill whenever the user asks to de-identify, redact, anonymise, scrub, or remove PHI\u002FPII from clinical text, notes, transcripts, or documents before sharing, exporting, or using them for analytics, testing, or demos — e.g. \"de-identify this note\", \"strip patient info\", \"make this HIPAA Safe Harbor compliant\", \"produce a limited data set\". It removes the 18 HIPAA Safe Harbor identifiers and emits an audit manifest of what was removed. Run the bundled scripts\u002Fdeidentify.py — do NOT hand-write redaction regexes yourself. This skill prepares de-identified text and an audit trail for human review; it does NOT certify legal compliance or make a formal expert determination.","microsoft\u002Fcat-agent-skills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69A1AHE1MABT7GVP5143M5Z",105,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fphi-deidentifier\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000037","律师与法务",[27,28,29,30,31],"phi deidentifier","testing","review","safe","analytics","2026-11-10 10:12:30",false,"---\r\nname: phi-deidentifier\r\ndescription: >-\r\n  Use this skill whenever the user asks to de-identify, redact, anonymise,\r\n  scrub, or remove PHI\u002FPII from clinical text, notes, transcripts, or documents\r\n  before sharing, exporting, or using them for analytics, testing, or\r\n  demos — e.g. \"de-identify this note\", \"strip patient info\", \"make this HIPAA\r\n  Safe Harbor compliant\", \"produce a limited data set\". It removes the 18 HIPAA\r\n  Safe Harbor identifiers and emits an audit manifest of what was removed. Run\r\n  the bundled scripts\u002Fdeidentify.py — do NOT hand-write redaction regexes\r\n  yourself. This skill prepares de-identified text and an audit trail for human\r\n  review; it does NOT certify legal compliance or make a formal expert\r\n  determination.\r\n---\r\n\r\n# PHI De-identifier\r\n\r\nDe-identify clinical \u002F health text to the **HIPAA Safe Harbor** standard (or a\r\n**Limited Data Set**) using the bundled, offline `scripts\u002Fdeidentify.py`. Every\r\nrun produces two things: the **redacted text** and an **audit manifest** listing\r\nwhat was removed, by category, plus anything that needs human review.\r\n\r\n## When to use\r\n\r\n- \"De-identify \u002F anonymise \u002F scrub \u002F redact this note (or file \u002F folder).\"\r\n- \"Remove patient identifiers before I share this.\"\r\n- \"Make this Safe Harbor compliant\" or \"produce a limited data set.\"\r\n- Preparing PHI-free samples for demos, test fixtures, analytics, or model prompts.\r\n\r\nDo **not** use it to answer clinical questions or to claim legal certification —\r\nit is a mechanical redactor plus an audit trail for a human to verify.\r\n\r\n## How to run it\r\n\r\nAlways call the bundled script; never improvise redaction logic.\r\n\r\n```bash\r\n# Safe Harbor (default): writes NOTE.deid.txt + NOTE.manifest.json next to input\r\npython scripts\u002Fdeidentify.py NOTE.txt\r\n\r\n# Limited Data Set: retains dates and city\u002Fstate\u002FZIP, strips direct identifiers\r\npython scripts\u002Fdeidentify.py NOTE.txt --mode limited\r\n\r\n# Inline text, machine-readable result to stdout\r\npython scripts\u002Fdeidentify.py --text \"John Doe, SSN 123-45-6789\" --json\r\n\r\n# HYBRID name recall: pass the person names YOU (the agent) spotted in free prose\r\npython scripts\u002Fdeidentify.py --text \"\u003Ctext>\" --names \"Rafael Alcaraz,Ngozi Okafor\" --json\r\n\r\n# STRUCTURED \u002F JSON input: redact by FIELD NAME (MRN, City, Employer, DOB, ...)\r\n# Use this whenever the input is a JSON object or a key\u002Fvalue record — NOT free text.\r\npython scripts\u002Fdeidentify.py intake_record.json --format record --json\r\n\r\n# ALSO emit a reversible token->value crosswalk (SENSITIVE — re-identifying)\r\npython scripts\u002Fdeidentify.py NOTE.txt --map crosswalk.json\r\n```\r\n\r\nSteps:\r\n\r\n1. Identify the input (a file path, a folder, or inline text) and the target\r\n   standard: **Safe Harbor** (default) or **Limited Data Set** (`--mode limited`).\r\n2. **Pick the right mode for the shape of the input.** If the input is a\r\n   **structured record** — a JSON object, or clear `Field: value` key\u002Fvalue pairs\r\n   (e.g. an intake record, an API payload, an exported row) — run it with\r\n   `--format record`. Record mode maps each field by its NAME (MRN, City,\r\n   Employer, DOB, SSN, …) and redacts deterministically; free-text mode will\r\n   MISS quoted\u002Fstructured identifiers like `\"MRN\": \"33108876\"` because there is\r\n   no adjacent label to anchor on. Use the default (free-text) mode only for\r\n   prose. Still run the name-recall pass on any free-text field VALUES (e.g. a\r\n   `ChiefComplaint` or `Notes` field that contains a person's name).\r\n3. **Name-recall pass (do this every time on free prose).** Before running, read\r\n   the text yourself and list every PERSON name and PLACE (city \u002F facility) you\r\n   can identify — including unusual or non-English names that a regex or the\r\n   bundled lexicon would miss (e.g. \"Rafael Alcaraz\", \"Okafor\"). This is the\r\n   model\u002Fagent contribution to the hybrid; you are the recall booster.\r\n4. Run `scripts\u002Fdeidentify.py`, passing the names from step 3 via\r\n   `--names \"Name One,Name Two\"` (or a `--names-file` with `{\"names\":[...],\r\n   \"places\":[...],\"orgs\":[...]}`). The script deterministically redacts every\r\n   occurrence, tokenizes consistently, runs its guard, and writes the manifest.\r\n   The script — not your paraphrase — is the auditable system of record.\r\n5. Return the **redacted text** exactly as produced, and summarise the\r\n   **manifest**: how many identifiers of each category were removed.\r\n6. **Surface `needs_human_review`.** Names caught heuristically, and any unknown\r\n   capitalised text flagged for review, are listed there — tell the user these\r\n   must be verified before release, and that bare names in free prose may still\r\n   be missed.\r\n7. Only write the `--map` crosswalk when the user explicitly needs to\r\n   re-identify later; warn that it is sensitive and must be stored securely,\r\n   separately from the de-identified output.\r\n\r\n## Use as a safety net before an outbound action\r\n\r\nAgents often carry actions that emit content — **Send Email, Create Record,\r\nPost Message, Export**. When an agent maker points one of those actions at this\r\nskill (e.g. an instruction like *\"before Send Email, de-identify the content\r\nusing phi-deidentifier\"*), apply this skill as a gate:\r\n\r\n1. Take the exact text about to leave the agent (e.g. the email Subject + Body,\r\n   or the field values of the record).\r\n2. De-identify it with the steps above (including the name-recall pass).\r\n3. Populate the action's inputs from the **redacted** result only — never from\r\n   the original text.\r\n4. If `needs_human_review` is non-empty, **do not fire the action**; show the\r\n   redacted draft plus the review items and ask the user to confirm first.\r\n5. On success, tell the user the content was de-identified and how many\r\n   identifiers were removed. Never place the `--map` crosswalk in the outbound\r\n   content or in chat.\r\n\r\nThe maker only needs to reference this skill by name in the action's instruction;\r\nthe operational steps above live here, in the skill, so they do not have to be\r\nre-written per agent.\r\n\r\n## What it removes (HIPAA Safe Harbor, 18 identifiers)\r\n\r\nHandled **deterministically by regex** (high confidence): names in labelled\r\nfields and after titles, street addresses, city + state, geographic ZIP\r\n(truncated to 3 digits; restricted prefixes zeroed), all date elements finer\r\nthan year, ages 90+ (aggregated), phone (US, international `+country-code`, and\r\nlabelled formats), fax, email, URL, IP address, SSN (dashed, spaced, or\r\nlabelled — including run-together digits), MRN, health-plan \u002F account \u002F member\r\nnumbers, certificate \u002F license \u002F DEA \u002F NPI numbers, device serial numbers, VIN.\r\n\r\nAs a backstop, any **unlabelled digit run of 7+ characters** that looks like an\r\nidentifier the structured detectors couldn't anchor on (a bare account \u002F record\r\n\u002F SSN \u002F device or phone number) is **flagged under `needs_human_review`** — never\r\nsilently dropped, and never auto-redacted (a bare number may be a legitimate\r\nnon-PHI value, so a human confirms).\r\n\r\nHandled **best-effort** and flagged for review: person names in free prose.\r\nNames are caught by (a) title\u002Flabel patterns (\"Patient: …\", \"Dr. …\") and (b) a\r\nbundled offline **name lexicon** (`assets\u002Fname_lexicon.txt`) that catches many\r\nbare names with no title or label — all fully local, no network or extra\r\npackages, so it works in locked-down agent sandboxes (e.g. the Copilot Studio\r\nPython container where you cannot `pip install`). Names not in the lexicon can\r\nstill be missed and word-like names may be over-redacted, so **human review of\r\nfree text is required**; extend the lexicon for your population. (If a runtime\r\nalready ships spaCy it is used automatically as a bonus, but installation is\r\nnever assumed.) City\u002Fstate written without a comma or trailing ZIP may also be\r\nmissed. **Full-face photos and biometric identifiers are out of scope** — this\r\nskill processes text only; call that out if the source contains images.\r\n\r\n## Transformations (so downstream data stays useful)\r\n\r\n- **Consistent pseudonyms:** each distinct value maps to a stable token, e.g.\r\n  every occurrence of the same MRN becomes `[MRN-1]`, so linkage is preserved\r\n  without exposing the value.\r\n- **Dates → year only:** `03\u002F15\u002F1985` → `[DATE:1985]` (Safe Harbor keeps year).\r\n- **Ages:** ≤89 retained; 90+ → `[AGE:90+]`.\r\n- **ZIP → first 3 digits:** `98104` → `[ZIP:981XX]`; HHS-restricted low-population\r\n  prefixes → `[ZIP:000]`.\r\n- **Limited Data Set mode** keeps dates and city\u002Fstate\u002FZIP intact while still\r\n  removing names, contacts, SSN, MRN, and account numbers.\r\n\r\n## The audit manifest\r\n\r\n`NOTE.manifest.json` records the mode, a per-category count summary, one entry\r\nper token with a **salted hash** of the original value (never the plaintext),\r\nthe `needs_human_review` list, and explanatory notes. Use it as the evidence\r\ntrail for a reviewer — it answers \"what did the tool remove, and what still\r\nneeds eyes?\" without itself containing PHI.\r\n\r\n## Guardrails\r\n\r\n- **Never claim compliance.** Report what was removed; a qualified human makes\r\n  the Safe Harbor \u002F expert-determination call.\r\n- **Never invent or \"restore\" content.** Only redact.\r\n- **Do not paste the crosswalk into chat** or co-locate it with the output; it\r\n  re-identifies the data.\r\n- **Always relay `needs_human_review`** and remind the user to review free text —\r\n  regex cannot guarantee every bare name or unusual identifier is caught.\r\n- Text only: if the source has images\u002Fscans, say so and recommend a separate\r\n  image-redaction step.\r\n\r\n## References\r\n\r\n- `references\u002Fhipaa-safe-harbor.md` — the 18 identifiers, ZIP\u002Fdate\u002Fage rules,\r\n  and Safe Harbor vs Limited Data Set, as implemented here.\r\n","# PHI De-identifier\r\n\r\nDe-identify clinical \u002F health text to the **HIPAA Safe Harbor** standard (or a\r\n**Limited Data Set**) using the bundled, offline `scripts\u002Fdeidentify.py`. Every\r\nrun produces two things: the **redacted text** and an **audit manifest** listing\r\nwhat was removed, by category, plus anything that needs human review.\r\n\r\n## When to use\r\n\r\n- \"De-identify \u002F anonymise \u002F scrub \u002F redact this note (or file \u002F folder).\"\r\n- \"Remove patient identifiers before I share this.\"\r\n- \"Make this Safe Harbor compliant\" or \"produce a limited data set.\"\r\n- Preparing PHI-free samples for demos, test fixtures, analytics, or model prompts.\r\n\r\nDo **not** use it to answer clinical questions or to claim legal certification —\r\nit is a mechanical redactor plus an audit trail for a human to verify.\r\n\r\n## How to run it\r\n\r\nAlways call the bundled script; never improvise redaction logic.\r\n\r\n```bash\r\n# Safe Harbor (default): writes NOTE.deid.txt + NOTE.manifest.json next to input\r\npython scripts\u002Fdeidentify.py NOTE.txt\r\n\r\n# Limited Data Set: retains dates and city\u002Fstate\u002FZIP, strips direct identifiers\r\npython scripts\u002Fdeidentify.py NOTE.txt --mode limited\r\n\r\n# Inline text, machine-readable result to stdout\r\npython scripts\u002Fdeidentify.py --text \"John Doe, SSN 123-45-6789\" --json\r\n\r\n# HYBRID name recall: pass the person names YOU (the agent) spotted in free prose\r\npython scripts\u002Fdeidentify.py --text \"\u003Ctext>\" --names \"Rafael Alcaraz,Ngozi Okafor\" --json\r\n\r\n# STRUCTURED \u002F JSON input: redact by FIELD NAME (MRN, City, Employer, DOB, ...)\r\n# Use this whenever the input is a JSON object or a key\u002Fvalue record — NOT free text.\r\npython scripts\u002Fdeidentify.py intake_record.json --format record --json\r\n\r\n# ALSO emit a reversible token->value crosswalk (SENSITIVE — re-identifying)\r\npython scripts\u002Fdeidentify.py NOTE.txt --map crosswalk.json\r\n```\r\n\r\nSteps:\r\n\r\n1. Identify the input (a file path, a folder, or inline text) and the target\r\n   standard: **Safe Harbor** (default) or **Limited Data Set** (`--mode limited`).\r\n2. **Pick the right mode for the shape of the input.** If the input is a\r\n   **structured record** — a JSON object, or clear `Field: value` key\u002Fvalue pairs\r\n   (e.g. an intake record, an API payload, an exported row) — run it with\r\n   `--format record`. Record mode maps each field by its NAME (MRN, City,\r\n   Employer, DOB, SSN, …) and redacts deterministically; free-text mode will\r\n   MISS quoted\u002Fstructured identifiers like `\"MRN\": \"33108876\"` because there is\r\n   no adjacent label to anchor on. Use the default (free-text) mode only for\r\n   prose. Still run the name-recall pass on any free-text field VALUES (e.g. a\r\n   `ChiefComplaint` or `Notes` field that contains a person's name).\r\n3. **Name-recall pass (do this every time on free prose).** Before running, read\r\n   the text yourself and list every PERSON name and PLACE (city \u002F facility) you\r\n   can identify — including unusual or non-English names that a regex or the\r\n   bundled lexicon would miss (e.g. \"Rafael Alcaraz\", \"Okafor\"). This is the\r\n   model\u002Fagent contribution to the hybrid; you are the recall booster.\r\n4. Run `scripts\u002Fdeidentify.py`, passing the names from step 3 via\r\n   `--names \"Name One,Name Two\"` (or a `--names-file` with `{\"names\":[...],\r\n   \"places\":[...],\"orgs\":[...]}`). The script deterministically redacts every\r\n   occurrence, tokenizes consistently, runs its guard, and writes the manifest.\r\n   The script — not your paraphrase — is the auditable system of record.\r\n5. Return the **redacted text** exactly as produced, and summarise the\r\n   **manifest**: how many identifiers of each category were removed.\r\n6. **Surface `needs_human_review`.** Names caught heuristically, and any unknown\r\n   capitalised text flagged for review, are listed there — tell the user these\r\n   must be verified before release, and that bare names in free prose may still\r\n   be missed.\r\n7. Only write the `--map` crosswalk when the user explicitly needs to\r\n   re-identify later; warn that it is sensitive and must be stored securely,\r\n   separately from the de-identified output.\r\n\r\n## Use as a safety net before an outbound action\r\n\r\nAgents often carry actions that emit content — **Send Email, Create Record,\r\nPost Message, Export**. When an agent maker points one of those actions at this\r\nskill (e.g. an instruction like *\"before Send Email, de-identify the content\r\nusing phi-deidentifier\"*), apply this skill as a gate:\r\n\r\n1. Take the exact text about to leave the agent (e.g. the email Subject + Body,\r\n   or the field values of the record).\r\n2. De-identify it with the steps above (including the name-recall pass).\r\n3. Populate the action's inputs from the **redacted** result only — never from\r\n   the original text.\r\n4. If `needs_human_review` is non-empty, **do not fire the action**; show the\r\n   redacted draft plus the review items and ask the user to confirm first.\r\n5. On success, tell the user the content was de-identified and how many\r\n   identifiers were removed. Never place the `--map` crosswalk in the outbound\r\n   content or in chat.\r\n\r\nThe maker only needs to reference this skill by name in the action's instruction;\r\nthe operational steps above live here, in the skill, so they do not have to be\r\nre-written per agent.\r\n\r\n## What it removes (HIPAA Safe Harbor, 18 identifiers)\r\n\r\nHandled **deterministically by regex** (high confidence): names in labelled\r\nfields and after titles, street addresses, city + state, geographic ZIP\r\n(truncated to 3 digits; restricted prefixes zeroed), all date elements finer\r\nthan year, ages 90+ (aggregated), phone (US, international `+country-code`, and\r\nlabelled formats), fax, email, URL, IP address, SSN (dashed, spaced, or\r\nlabelled — including run-together digits), MRN, health-plan \u002F account \u002F member\r\nnumbers, certificate \u002F license \u002F DEA \u002F NPI numbers, device serial numbers, VIN.\r\n\r\nAs a backstop, any **unlabelled digit run of 7+ characters** that looks like an\r\nidentifier the structured detectors couldn't anchor on (a bare account \u002F record\r\n\u002F SSN \u002F device or phone number) is **flagged under `needs_human_review`** — never\r\nsilently dropped, and never auto-redacted (a bare number may be a legitimate\r\nnon-PHI value, so a human confirms).\r\n\r\nHandled **best-effort** and flagged for review: person names in free prose.\r\nNames are caught by (a) title\u002Flabel patterns (\"Patient: …\", \"Dr. …\") and (b) a\r\nbundled offline **name lexicon** (`assets\u002Fname_lexicon.txt`) that catches many\r\nbare names with no title or label — all fully local, no network or extra\r\npackages, so it works in locked-down agent sandboxes (e.g. the Copilot Studio\r\nPython container where you cannot `pip install`). Names not in the lexicon can\r\nstill be missed and word-like names may be over-redacted, so **human review of\r\nfree text is required**; extend the lexicon for your population. (If a runtime\r\nalready ships spaCy it is used automatically as a bonus, but installation is\r\nnever assumed.) City\u002Fstate written without a comma or trailing ZIP may also be\r\nmissed. **Full-face photos and biometric identifiers are out of scope** — this\r\nskill processes text only; call that out if the source contains images.\r\n\r\n## Transformations (so downstream data stays useful)\r\n\r\n- **Consistent pseudonyms:** each distinct value maps to a stable token, e.g.\r\n  every occurrence of the same MRN becomes `[MRN-1]`, so linkage is preserved\r\n  without exposing the value.\r\n- **Dates → year only:** `03\u002F15\u002F1985` → `[DATE:1985]` (Safe Harbor keeps year).\r\n- **Ages:** ≤89 retained; 90+ → `[AGE:90+]`.\r\n- **ZIP → first 3 digits:** `98104` → `[ZIP:981XX]`; HHS-restricted low-population\r\n  prefixes → `[ZIP:000]`.\r\n- **Limited Data Set mode** keeps dates and city\u002Fstate\u002FZIP intact while still\r\n  removing names, contacts, SSN, MRN, and account numbers.\r\n\r\n## The audit manifest\r\n\r\n`NOTE.manifest.json` records the mode, a per-category count summary, one entry\r\nper token with a **salted hash** of the original value (never the plaintext),\r\nthe `needs_human_review` list, and explanatory notes. Use it as the evidence\r\ntrail for a reviewer — it answers \"what did the tool remove, and what still\r\nneeds eyes?\" without itself containing PHI.\r\n\r\n## Guardrails\r\n\r\n- **Never claim compliance.** Report what was removed; a qualified human makes\r\n  the Safe Harbor \u002F expert-determination call.\r\n- **Never invent or \"restore\" content.** Only redact.\r\n- **Do not paste the crosswalk into chat** or co-locate it with the output; it\r\n  re-identifies the data.\r\n- **Always relay `needs_human_review`** and remind the user to review free text —\r\n  regex cannot guarantee every bare name or unusual identifier is caught.\r\n- Text only: if the source has images\u002Fscans, say so and recommend a separate\r\n  image-redaction step.\r\n\r\n## References\r\n\r\n- `references\u002Fhipaa-safe-harbor.md` — the 18 identifiers, ZIP\u002Fdate\u002Fage rules,\r\n  and Safe Harbor vs Limited Data Set, as implemented here.\r\n",{"name":5,"description":7},"main","3678d23ad8fb8d098c428302a98044ac9514d5a1","submissions\u002Fphi-deidentifier",120,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-13 01:39:10",{"source":8,"skillName":5,"commands":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add microsoft\u002Fcat-agent-skills --skill phi-deidentifier",{"tool":52,"command":53},"bunx","bunx skills add microsoft\u002Fcat-agent-skills --skill phi-deidentifier",{"tool":55,"command":56},"pnpm","pnpm dlx skills add microsoft\u002Fcat-agent-skills --skill phi-deidentifier","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fphi-deidentifier\u002FSKILL.md",true,[60,66,72,76,80],{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"assets\u002Fname_lexicon.txt","ASSET","资产文件",3140,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fphi-deidentifier\u002Fassets\u002Fname_lexicon.txt",{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"metadata.json","REFERENCE","参考文档",613,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fphi-deidentifier\u002Fmetadata.json",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"README.md",6544,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fphi-deidentifier\u002FREADME.md",{"path":77,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fhipaa-safe-harbor.md",3869,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fphi-deidentifier\u002Freferences\u002Fhipaa-safe-harbor.md",{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"scripts\u002Fdeidentify.py","SCRIPT","脚本",39405,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fphi-deidentifier\u002Fscripts\u002Fdeidentify.py",[87,114,139,175,193,209],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":32},"01M6X3P09R8SJH3QJGMTAA5N90","stakeholder-and-decision-map","Mines customer interactions to identify stakeholders, infer their likely roles and engagement, map the decision process, and highlight who or what may be missing. Use when asked to \"map a deal's buying group\", identify influential or missing stakeholders, \"assess who has gone quiet\", or \"explain how the customer will decide\". Do not use for phased engagement plans, contact sequences, choosing or drafting outreach to the single next contact, seller or team performance reviews, or contacting, scheduling, or changing records.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fstakeholder-and-decision-map\u002FSKILL.md",[],[94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"decision","map","decision map","stakeholder and decision map","performance","customer","team","how","decide","do","next","infer","missing","records","assess","reviews","for","single","when","sequences",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":32},"01M6X3NRES2XSZQCH015WD4JN6","sales-progress-update","Creates or refreshes a sales progress update for a manager or team about work done, deal movement, pending outcomes, supported forecast position, and help needed. Use when asked to \"create my weekly sales update\", \"refresh my sales progress deck\", \"report my forecast position\", \"where do I stand against my quarter forecast\", or \"catch my manager up on committed deals\". Do NOT use for non-sales project updates, a since-date deal change digest, momentum or blocker analysis, meeting prep, or outreach drafting.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Fsales-progress-update\u002FSKILL.md",[],[121,122,123,124,125,126,127,128,100,129,130,131,132,133,134,135,136,137,138,112],"update","sales","progress","sales progress update","change","create","meeting prep","analysis","work","up","done","digest","project","my","updates","manager","weekly","catch",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":156,"publishedAt":32},"01M6X3MSDTB1E5H6DQAFAY41YP","submission-assemble","Put the checked CSR or 510(k) sections together in template order with a reviewer-facing open-items register, as a draft package for human sign-off. Use when the user asks \"assemble the CSR draft\", \"put the module together for review\", \"build the review package with the open items on top\", \"compile the 510(k) sections in order\" or \"package this up for the submission lead\". Do NOT use for assembling an unchecked draft - use consistency-check instead. Do NOT use for loading protocol, SAP or TLF sources - use source-ingest instead. Do NOT use for compiling general documents or slide decks - use the built-in docx or pptx skill instead. Do NOT use for filing or submitting to a regulator - use the sponsor's validated gateway, operated by a human, instead. Guardrail: draft for human approval only (L1); status is draft_clean or draft_open_items, never submitted.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Freg-submission-authoring\u002Fsubmission-assemble\u002FSKILL.md",[145,150,153],{"categoryNo":146,"categoryName":147,"taxonomyType":148,"taxonomyTypeDesc":149},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":151,"categoryName":152,"taxonomyType":148,"taxonomyTypeDesc":149},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000034","行政与办公人员",[157,158,159,160,161,162,163,164,165,166,167,168,130,169,170,171,172,173,174,112],"assemble","submission","submission assemble","docx","pptx","writing","documents","healthcare","open","reviewer","never","only","clean","guardrail","slide decks","sources","filing","loading",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":32},"01M6X3MM9TVWDQ716QGNV4DS25","source-ingest","Pull every number, date and population definition out of a study's protocol, SAP, TLF tables, investigator brochure and prior approved text into one cited fact map, keyed per config\u002Ffield-dictionary.json, before any CSR or 510(k) section is written. Use when the user asks \"here's the locked TLF set and SAP, set up the CSR\", \"load the protocol and tables before we start drafting\", \"what figures do we have from the database lock?\", \"pull the disposition counts out of Table 14.1.1\" or \"ingest the IB for the 510(k)\". Do NOT use for drafting or assembling submission sections - use section-draft or submission-assemble instead. Do NOT use for reconciling a patient's medications - use the discharge-care-transition plugin instead. Guardrail: reads and cites sources only; never estimates, rounds or invents a fact.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Freg-submission-authoring\u002Fsource-ingest\u002FSKILL.md",[],[182,183,184,185,186,128,164,187,167,168,130,95,188,170,172,189,190,191,192,112],"source ingest","ingest","source","database","SET","text","patient","number","lock","definition","transition",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":32},"01M6X3M7ZH5ZNEHP56BQ74HAJR","section-plan","Work out which sections a CSR or 510(k) requires, from the templates in config, check each one against the cited fact map, and name what is missing before a word is drafted. Use when the user asks \"what do we still need before we can draft the CSR?\", \"which sections are ready to start?\", \"what's blocking the performance data section?\", \"do we have everything for the synopsis?\" or \"map our tables to the E3 structure\". Do NOT use for writing section language - use section-draft instead. Do NOT use for cross-checking a finished draft - use consistency-check instead. Do NOT use for outlining general Word documents or business reports - use the built-in docx skill instead. Only structures with a template in config are supported. Guardrail: plans only; never marks a section ready on a missing fact and never invents a structure.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Freg-submission-authoring\u002Fsection-plan\u002FSKILL.md",[199],{"categoryNo":146,"categoryName":147,"taxonomyType":148,"taxonomyTypeDesc":149},[201,202,203,160,162,98,163,128,164,129,167,168,204,205,206,95,170,207,208,112],"plan","section","section plan","ready","reports","structure","word documents","which",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":213,"categories":214,"keywords":215,"publishedAt":32},"01M6X3M0V1MJNCR653ES9XGVMS","section-draft","Write or revise the language for one planned CSR or 510(k) section - synopsis, efficacy, safety, discussion, device description - using only figures already in the cited fact map, each with its TLF or SAP citation. Use when the user asks \"draft the Synopsis from our locked tables\", \"write up safety from Table 14.3.1\", \"give me a first pass at the discussion\", \"draft the device description for the 510(k)\" or \"tighten the wording of the Discussion section\". Do NOT use for supplying a number not already in source_facts - use source-ingest instead. Do NOT use for verifying a draft's figures - use consistency-check instead. Do NOT use for appeal letters, clinical notes or discharge summaries - use the matching HLS plugin instead. Guardrail: drafts for human approval only (L1); never asserts an unsourced number or harmonises two figures.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fcat-agent-skills\u002Fblob\u002Fmain\u002Fsubmissions\u002Freg-submission-authoring\u002Fsection-draft\u002FSKILL.md",[],[216,202,217,162,218,164,219,167,168,130,220,95,221,222,223,170,224,189,225,112],"draft","section draft","notes","write","me","human","life","clinical","drafts","at"]