# Style Analyzer > Analyze the user's communication style across their Teams chats and emails to build a reusable mimicry profile — greetings, tone, length, punctuation, sign-offs, common phrases, and quirks. Use this skill when the user asks to capture or analyze their writing style, or before configuring an assistant (e.g. an OOO auto-responder) that should write in their voice. - Skill: `kody-w/style-analyzer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add kody-w/style-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/kody-w/style-analyzer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: kody-w (https://skillmd.com/u/kody-w) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kody-w/style-analyzer --- # Style Analyzer Analyze the user's communication patterns across Teams and Outlook and build a style profile that other skills or automations (e.g. an out-of-office auto-responder) can use to write in the user's voice. Save the profile to memory so it's available across sessions. > **Tool names.** This skill refers to Microsoft 365 tools as `m365_*` and to > memory as remember/recall tools. If your host exposes these under different > names, map them to the equivalent capability. ## Data collection ### 1. Gather sent emails (20–30 samples) - List the last ~30 emails in the **Sent** folder. - For emails with real body content (not just meeting accepts/declines), fetch the full text body. ### 2. Gather Teams chat messages - List recent chats (~50). - For each relevant chat (prioritize active 1:1 and group chats), fetch the last ~30 messages. - Filter to messages **from the current user** (match the `from` field to the user's display name). ### 3. Sample diversity Aim for: - 10+ sent emails with body content - 50+ Teams messages across **20–25 different chats** - A mix of 1:1, group, and meeting chats - Both internal and external conversations where available ## Analysis framework Analyze the collected messages across these dimensions: - **A. Greetings** — how they address people (first name, "Hi [Name]", "Hey", formal titles); patterns by relationship type (internal vs external). - **B. Tone & formality** — professional/casual/mixed; direct vs hedging; warmth indicators. - **C. Message length** — average sentence count; frequency of one-word replies; when they write longer messages. - **D. Punctuation & grammar** — consistency; common typos (e.g. lowercase "i"); emoji usage (none / occasional / frequent). - **E. Sign-offs** — email signature style; Teams message endings; closing phrases ("Thanks", "Regards", etc.). - **F. Common phrases** — frequently used expressions for agreement ("sounds good", "makes sense"), requests ("can you", "would you mind"), availability, and FYI/context-setting. - **G. Technical communication** — how they explain technical concepts; level of detail; hedging vs confidence. - **H. Action patterns** — how they delegate, loop others in, and schedule meetings. ## Output ### 1. Display a summary Present findings as a formatted table: ```markdown ## Communication Style Profile for [Name] | Dimension | Pattern | |-----------|---------| | Greetings | ... | | Tone | ... | | Length | ... | | Emojis | ... | | Sign-offs | ... | | Technical | ... | ### Common phrases - "..." - "..." ### Quirks & notes - ... ``` ### 2. Save to memory Store the style guide in memory. Use two entries to stay within any per-fact length limits: - **Entry 1** — greetings, tone, brevity, punctuation, emojis. - **Entry 2** — common phrases, delegation style, technical communication, quirks. Tag both as a preference so they persist and can be recalled later. ### 3. Confirm storage Tell the user: - The style profile has been saved to memory. - It can be recalled with a query like "writing style". - It's available to other assistants and automations that write in their voice. ## Usage notes - Re-run periodically (e.g. quarterly) to keep the profile current. - Pairs well with an OOO / auto-responder skill that should mimic the user's voice. ## Privacy All analysis happens inside the user's own agent environment against their own Microsoft 365 data. No communication content is sent to any third party. The saved profile describes *how* the user writes, not *what* they wrote — do not store verbatim private message content in the profile. ## Run this — do not improvise This capability's deterministic implementation is a RAPP single-file agent, linked beside this file as `style_analyzer_agent.py` and embedded as the fenced Python below (sha256 3cb0e67c9a1ad1bd…; a byte-exact copy is also vaulted in the capsule comment at the end of this file). On a host with sandbox execution, run the linked file directly — if it is missing, write the fence contents verbatim to `style_analyzer_agent.py` first: ```bash python3 style_analyzer_agent.py '{"key": "value"}' # arguments as one JSON object echo '{"key": "value"}' | python3 style_analyzer_agent.py # or on stdin python3 style_analyzer_agent.py --tool # emit the JSON tool contract ``` Treat stdout as a tool result. If it reports missing or unresolved inputs, stop and collect them. If it returns `steps`, execute those steps in order exactly as returned; if it returns `instructions`, follow them with the supplied inputs. Otherwise use the result verbatim. Do not invent behavior beyond that output. On a host without code execution, treat the Parameters schema and the code below as the exact specification and never paraphrase a step. Never edit inside the generated markers; a converter-equipped host can instead restore the original file checksum-verified with the installed `rapp-agent-converter/scripts/toast.py convert SKILL.md --to agent`. ````python # rapp:deterministic """StyleAnalyzer -- Analyze the user's communication style across their Teams chats and emails to build a reusable mimicry profile — greetings, tone, length, punctuation, sign-offs, common phrases, and quirks. Use this skill when the user asks to capture or analyze their writing style, or before configuring an assistant (e.g. an OOO auto-responder) that should write in their voice. Generated by the rapp skill from style-analyzer. The RCI capsule at the bottom of this file carries the full original; `toast.py convert` restores it byte-exact.""" import json import re import sys try: from agents.basic_agent import BasicAgent except ImportError: # running OUTSIDE a brainstem -- stay executable anyway. class BasicAgent: # noqa: D101 - minimal stand-in, same contract def __init__(self, name=None, metadata=None): if name: self.name = name if metadata: self.metadata = metadata def perform(self, **kwargs): return "Not implemented." def system_context(self): return None def to_tool(self): return {"type": "function", "function": { "name": self.name, "description": self.metadata.get("description", ""), "parameters": self.metadata.get("parameters", {})}} # The procedural layer, verbatim from the source capability. INSTRUCTIONS = '# Style Analyzer\n\nAnalyze the user's communication patterns across Teams and Outlook and build a\nstyle profile that other skills or automations (e.g. an out-of-office\nauto-responder) can use to write in the user's voice. Save the profile to memory\nso it's available across sessions.\n\n> **Tool names.** This skill refers to Microsoft 365 tools as `m365_*` and to\n> memory as remember/recall tools. If your host exposes these under different\n> names, map them to the equivalent capability.\n\n## Data collection\n\n### 1. Gather sent emails (20–30 samples)\n\n- List the last ~30 emails in the **Sent** folder.\n- For emails with real body content (not just meeting accepts/declines), fetch\n the full text body.\n\n### 2. Gather Teams chat messages\n\n- List recent chats (~50).\n- For each relevant chat (prioritize active 1:1 and group chats), fetch the last\n ~30 messages.\n- Filter to messages **from the current user** (match the `from` field to the\n user's display name).\n\n### 3. Sample diversity\n\nAim for:\n\n- 10+ sent emails with body content\n- 50+ Teams messages across **20–25 different chats**\n- A mix of 1:1, group, and meeting chats\n- Both internal and external conversations where available\n\n## Analysis framework\n\nAnalyze the collected messages across these dimensions:\n\n- **A. Greetings** — how they address people (first name, "Hi [Name]", "Hey",\n formal titles); patterns by relationship type (internal vs external).\n- **B. Tone & formality** — professional/casual/mixed; direct vs hedging; warmth\n indicators.\n- **C. Message length** — average sentence count; frequency of one-word replies;\n when they write longer messages.\n- **D. Punctuation & grammar** — consistency; common typos (e.g. lowercase "i");\n emoji usage (none / occasional / frequent).\n- **E. Sign-offs** — email signature style; Teams message endings; closing\n phrases ("Thanks", "Regards", etc.).\n- **F. Common phrases** — frequently used expressions for agreement ("sounds\n good", "makes sense"), requests ("can you", "would you mind"), availability,\n and FYI/context-setting.\n- **G. Technical communication** — how they explain technical concepts; level of\n detail; hedging vs confidence.\n- **H. Action patterns** — how they delegate, loop others in, and schedule\n meetings.\n\n## Output\n\n### 1. Display a summary\n\nPresent findings as a formatted table:\n\n```markdown\n## Communication Style Profile for [Name]\n\n| Dimension | Pattern |\n|-----------|---------|\n| Greetings | ... |\n| Tone | ... |\n| Length | ... |\n| Emojis | ... |\n| Sign-offs | ... |\n| Technical | ... |\n\n### Common phrases\n- "..."\n- "..."\n\n### Quirks & notes\n- ...\n```\n\n### 2. Save to memory\n\nStore the style guide in memory. Use two entries to stay within any per-fact\nlength limits:\n\n- **Entry 1** — greetings, tone, brevity, punctuation, emojis.\n- **Entry 2** — common phrases, delegation style, technical communication,\n quirks.\n\nTag both as a preference so they persist and can be recalled later.\n\n### 3. Confirm storage\n\nTell the user:\n\n- The style profile has been saved to memory.\n- It can be recalled with a query like "writing style".\n- It's available to other assistants and automations that write in their voice.\n\n## Usage notes\n\n- Re-run periodically (e.g. quarterly) to keep the profile current.\n- Pairs well with an OOO / auto-responder skill that should mimic the user's\n voice.\n\n## Privacy\n\nAll analysis happens inside the user's own agent environment against their own\nMicrosoft 365 data. No communication content is sent to any third party. The\nsaved profile describes *how* the user writes, not *what* they wrote — do not\nstore verbatim private message content in the profile.' # Ordered commands lifted verbatim from the capability's own documentation. STEPS = [] class StyleAnalyzerAgent(BasicAgent): def __init__(self): self.name = 'StyleAnalyzer' self.metadata = { "name": "StyleAnalyzer", "description": "Analyze the user's communication style across their Teams chats and emails to build a reusable mimicry profile \u2014 greetings, tone, length, punctuation, sign-offs, common phrases, and quirks. Use this skill when the user asks to capture or analyze their writing style, or before configuring an assistant (e.g. an OOO auto-responder) that should write in their voice.", "parameters": { "type": "object", "properties": {}, "required": [] } } super().__init__(name=self.name, metadata=self.metadata) def perform(self, **kwargs): # toaster:generated-perform return json.dumps({"status": "ok", "instructions": INSTRUCTIONS, "inputs": kwargs, "note": "Prose-only capability: follow INSTRUCTIONS " "with the given inputs."}, indent=2) if __name__ == "__main__": # echo '{"arg": "value"}' | python3 style_analyzer_agent.py # python3 style_analyzer_agent.py '{"arg": "value"}' # python3 style_analyzer_agent.py --tool # emit the JSON tool contract _a = sys.argv[1:] if _a and _a[0] == "--tool": print(json.dumps(StyleAnalyzerAgent().to_tool(), indent=2)) else: _raw = _a[0] if _a else (sys.stdin.read().strip() or "{}") print(StyleAnalyzerAgent().perform(**json.loads(_raw))) # rci-capsule:v1:H4sIAAAAAAAC/4VZZ5OjWLL9K0RNxHs9pepCgGx3xEZIyIC8QRJia2MbczHCXAQXITSz+9tfXpDKzG7E6/kwwDXpTmaeVP3xpGfExcnTjygLgpcnC6Vm4sXEw9HTj6depAfFDTHERUyWouR/U8bEYZhFnqnTLUxKigAxupngNKW7vIRRkB7CNlcnKaNHFoNC3QtgETNG5gUWozMJylLdgHOhF3pmUjBxgm0P3t8yvs41GCdBiHiRk77AqQi9MAGKHOK+MHEWmSQrJb8wqedE37Ftwy6qEygTu4meIninYs+Zl/jpK7NLqfpeyqS+FwRM7qLo3RxGT/1SM1OPSZYgBsOnD5PBmDzxqCaVnS903UA2hp0mjmzPyRK6qEdwUeqlRI8I8w29Oq/003K5ZMC3+HuC0hhHFkp+h0t1wqQuzsAP9GrEeNFd0gV7Jnp9enlCVz2MA5Q+/fj7P16ePHh+xMaLUpJkJjUfVp9+Y7al9+9BSt6it+j/DVisE4KSKH3ErIoWddgyIwHGfvl8j9RbVMX3EZ9SfQx3J5U309JhYGNYXp5+GI8zArGh4QGr3qK/+sGELRkNDP7ihofKlS+YrX6pLHmXj5kQhTgpQDHMeAS26hdAVwmmu0EAgJTq8krd8Tfm+VnBOGAiPUTp6/Mzo3xAIUE2Ssrwzz16FtuEEVpN+IDBMj1lfoXw+s/nX6VLCKbXVeLpYoLg2UAJmyBTh9vKU6+MbDMFzhLGxSlh0DXGoA+1AYzNqO2M5dkgFkWEXleq9cKEekz3hFQXai8C8F50QD2hyNQNL/BIURr022/MQCc6BDUIUImE6utvDPfKjPUqNPTcPeu+8fUyqQShzqQVrn6nJ74zM8BrKSzQ4eHfsH4/cg/F8/MW7gGP2TgAvV/pmRGE+74r94gLPtADxsBWQdOBULHfIkyYUwY3hlUOQ1hMFJOUtZAZeBGIf2FsREz3LWJKOXZGnYeupLzo9WEO/27ORz2BO9NUd1D6yQLwfummstx8+3ez/vuHprpJVQzQRb/vYL7FiYdpSt8oXogH+OJ+cGV8nQRncXXPQ8V391BdqYce8isRXgCZVGGy+gw+sxMclsfMLKFBLhENTvwGGXK/8Bfd84uxPRRY94DT++/Qt7w0DvSiRMbv794QaDLQ6MH6BUALcCiz3QshPMmPyh9cvfYl9mWIPkeHbmrCpsqj71rfE+f5+YEVvvmB0sojz8/0bA8K9pXBNvXZS+WwqtY+Yl3upTv7UCQASLTSAELKLnC9v4Au1IJ7wYByDMX0PYnvEC+rGBRUxk7ADTlO/L/Wtjv+kfUfZlS5Znkhiso6cHfO83MPEPVoLBCRe69xcU6PQEpbFhSolIkRpn7+ZnsJwIuG4YV5e5I85u8LeP7H21P5igp4oGED94dgFfEITa2fH/XVKCj2KjNdD/K7iOHWd59c0neXVIh9fu6/Mgq0O+Z/7pdCkD/0pCWwqmx6wJp6msH/IBrI+gm2QhIQeqOLLAfM+8nkehKSMsW8yKKlHyfpXYr4yswrl93b6ocMKLcJ/U5BhCKTejmLyE+IAhQk+FDQ2IOG3yEiFlgXBx5Kf1Ipj65a3Mt5gCMHcuNLvjw/D16Z1UcHBzsdCG+oJx8aADhoH6Wyfj66OjgOPzpLgHOUgPVAFZ68t6ffS+FQkk8e5A9VHeoPeJBlsAm7SmfBy11/8nD0ELLpQR4+ZJdZU7IKveQCZev7+TVZGATuBPyAcgFO4YnKv9MO5tvbk+LqkZ9WGNkgR0+s8gWKyetD+OiVEb/QlQ8NHnoGBa0GNGfi5N7NKCQYnRKjsCyzb08pxAZuB/kOxlYlMtR9RDtglCJwzgtT3peSUjXacqEzVRvzkoHAK2R0ZJV77zlYtpoS2TRrR0eZLYvHlXxPEaG5czdjDGBFpkt5RfCVYvyX3AJDAp32lU8norIt/AQQXlAAwKIiLURAiZ8PHFNIlzzLomi8C5ZemZ75hcr8F4EWVH1HJ5Q6YhxXlIV2tqpcpSYIyIKy7N5LV/rorsCC4ox8aqqDe0HWmTSjYC0L7wriQuNgexUeKCHQq7QltCgRWszKyvPr1y845Fs4r+4Xv7CxisCt7vSGxvheZWDznyD6XsSYP5lVZSzzJyx8//j38UwXPgocnHh9fS13V0Xl0/usTPvPX4Y0g76cec+PLxe9h+/9Y+Wmr4CmcXp7gg1vT18eq73rkpdD8gNTqPbSZeqnT62/Yn4fbO8t2hLKumnpryipkwEqKFepttx5fo4hQ0nioZLWASEvyi4I2/QIxgyUfLeh7b9FVeVjApg/yEeHGMLRguE+8PQfg4iRoAvNj6+TSFmAHkWuuoT/XNO+Did3bL5PTy9f0uITOsosvI8xVEVFd6Cdg9ol2OKSvpZlOsUV6mPKDKBpUYzTdDcQU5FTQCR0opLEvfMJkSZWEoIOmBb9UgCiVOxOw+9eUd49/iDhLog3ENT7FIJkfUSptF8m/yG5pCE6GILAL4Hn09r9ZawCgFRHv9B5uLeaNN5Hq2pM+TxulPPIf52j7tm8K4v2HWlUyAZ9T7KIesrDtDEGUGur1nLO9ARcFBS/U9E+QvGX0ePO50pNVzpwAyan3qqMq4Y99i/j3n3M+DzzlQPvp0mHRviLwqsEiL9ZkbsgqIZRSoRcPY6hFoCZKcX9p1kJCgs0hpL2RRcvwVHZIHRHp9Pi3Sdl8fk65FgwRbwyC/yX8fDB4720opLgCpo5kELQ8WPwEGSaQglrFfyHd6pfDQxKgaEGP38M2GVwAPR0LHjOwRPPD5oAMXlkiIXpOh02aY4DCTFAmRAuB1/Arkfvfdct+hwZOjIH4EHoeY85mZI2mI/L4voYjmEXqA8LEGMYnv94guOAAuLRQfuPf7080V4JRMqqxm7K1uAKbJyAWj3BMvQAQut7tZn2ZZRc6O4/nsow0wej1YAzUiOVe9U/kW3sVeHYNog7rXHcfC5NBxZnjDl+rcSbdb7jl3o0sHo8aibkuhttoukuKEJt4W/jCY9Sb7rCy9WOPU5qvjARVSPMF3Wjq2/dy+p2M3Mh7+y93ea0KTTbz+vSzFX6oiS0lCT0/KQpnJ32LJxYvE+ua953jhcBIQSNhHO4MXeUg3o4VEiyMtN4c7O21qhviEtxwg91P+bWY7dDkrUejK8zv7kcjMTMbHEASEl3BRLsG2yWrdiupG834e5gp82T6zQnhbBYhreQ69XCgT+vLwt04ULN5a/att3vdVqKtURyEJFLlODF7bBntyetW9tP2t39gSxqcqe+E/OQPR3ae+zUBj6vXFuuc5GJ375qLV9e2h17uzfEDsQYItLvmLZ7HHqJkK8nRBwli+P+IiQse+InsuWN/HZLiGMpG/rxSJ7Vrr163+G06a7QNH5PDsXU1eTtZTxc+Zu10dHno1Xt6O5bQ3kiXI/FZCu6ntytr9Zc2D618yvrqzJ/dFvdNBiQoe/UGs5AzVonQ+KXxTof+KfB2ZxtmtK+vvCOsxM5qOIpLwKx5vX56bE9uUz3S4scLTHsTvo7byohmGN8tOwHEdceBvvRqLs9WE3t3AhmHGcFW9WW2oNE27rmrRYLnt8+LrqrYjYSXHeoqbm1YN2OwQb2bn06jN1ZfAkNn/W6ftaeFvu4d1Cuu7qRHQaRlpPZsOZLNzzTri57lXAujRTMxcdgq/UOwuasxAI+6JuTcnN33iExRW1zbYxkZdra75eieJzUWUzWjphZG51zcv4iEJG77ruz/USsDW9Z1CCRwrKtRoeNlE44S4i9Va/GyEmaI2G3Mw7ZQGqznh4rqwbAtT91+op+O3Q2R3VzaK0FscbtebGf40MQAM5V6yAORp3juBYeernX4mR3silu7ca5bhwObnSMgnmybtfj6SiO+7k2X+EtjuvxZjXJvGVfFkcSvnBksro4K+INie81LrZ6K7qRlO8WShY1d02/Z0DBNGam3+5ejfV2tEviGtrwt0zV+NZkqdcNfzISTpNCLnicj4O6Isa1xcRYL3YtvrMfdFGvgdujumPVR0tvnGQ80hB/3nNDbjlrK9EwrO3a7V3jOpg2bqLTaTZnkb2UkgE5TG7Tpb0/omE2QfP8oip2kw+WyPAXfX6N1mOtv5z5xqG1SPF130G3UbpuKOL4MDFvpsP3UDHT53Xl2guQe7uN/Z1b66stqVAHkjzxvKnS9fus07czK1v3huLlbCxPysEcr6OhKHIDLF0SK0Nz7pgZ3aDTiIQzrmtcPV2PTw1x11zXz9o1F51JTcpTANDmbPEnxZGGtS7p3c4bPTXP2NlE3VU6ru9Y+1izVx22sd40Q7NYS2sJyXLr1FOMecfM1HO+POx7JMVkWbhuR564Gm+vZYc7aJq9V7eSt9vamibImqqvim2xCRZ9ldi9AeuMWvw230g73Eln+sJYHGV9F7QbRtK76It5nl+zaNcZq4FaV48yHrT5VIpvcU1UVU9Zc+vb+Zj2FAf39c5SEpcNyxcbA1M+E2ta83bz02qV+WKgHdqLMa91+XUQ1pLsxC9HeCNs4mZbVmbXYNhbLYTjTZqa5DiYnjBEqM3VvVG0Veb1jN/iRneDRnEoqvl232vLcTrQxvLg2iqEQjrVnGN3jts3Y7Dm19dbIeN2uJxND8061xteRHezFPfEbbHHAb701PU166Zyyu5NccDKRO0t2Hyd7BdRyDeAAaD6grcHODw1870wt7tiayV1Gi1x31xlqbedNzrpot88cN0jb0x95dCJTkXkNHs5Ge21ZtMwjcFQXQ5dW7uy2bx/O/h6kF7IaG6K++ImCN0Le5ll0/6qtx4FjXEbq35tYeJ1EFzQSkk6heCY+qVnbbnGIC7ksHM6bZuKqoTxbqDn3DwddEzvukrItiGcNqZuRuHiqlrcGM2u4cIYqG5rtp5c9K7B65rgyJqCr8Rar4AYFWneWeE6a4h2dzjV4+4NK2exx5q9gzgNa/3VeWWd400qkZ3X3XedrRPM1zVxUWNJW1o4vBV2jx09u3W3J7JrW81GrVefxBh7uJaHViityAQoTqM2cjvqtc2ejHbOzt2Vf27FHm+up9eOdTnWauginQYDddQ6iomwK/ZKmGrCUDNuU3/X0i5OI2SbUqiykrFBltcZWpotToaaPzYtuMnnk1q/3/D4faEkFwf5YgsdLLkXBRchyOoHqT+9xvWpWGu4iodk8bi0VCMeeUVgTqbYO+3rtVNfqfUzc2lNhFlzstzbmeqf8KVrT9HqMhlNptugsYikde14tguebznz28q75H7Xui5r2+Z6JFneyfSne36+6Koj6yQpZ0Onf6gw61cnUmeSnW6Dmd4PRnjc33ZWDW7Abg6CtySOcJK5dTvype3pkl3HoyyPkGnZVr6Cch3L3Q4ZmsOO7F7yzA7lInHkATsM+tAX1EFEOFmDsYg38Tw6hGpUa/itTTPPyEA62Syr1sWGJbTmzUk8Fddaze+lwniujQ+bpjom22O4YtW14vWg76uGdEw0tcbz9DfumO+MoC2RMZJ3wSB2IoVryeddlz0piJt2ZK1tys3BashZF2ljJOHJPCfWyjrZaH1Cbrs72Iod7naYNM112psfV+OmoZ2RudCkyXWD66GtYXk+nJtRk9Vnog+cpGurSjwmVtC05tCRisas23PGc7mLh0Mbr/VpsMw3gbrtqhdJFO1jemrbm7A/2C081m5uGutgCYzC07zRbXtYXY+1hdc9OK7aaU17x/Gw70XyWTnUjvUxp14XwtiLHfY6yHs9YKmU0z4I7FSezV5DC76mrs43W/Ctq7ds+M9oNixbR7xhW0a7ocMoiLi2Xe+YTbsrCI1Gq9sCX7eFllBvmEgQkG3qnGki/elfJX3FFxACsyOwXWC+uvWjJLE/Pkmsfqcl1cL3v5UjxhMw48T0QA3utU61CjIHXsop7rv+wbTTIiUo/Of9l6MHLSe6c/+jVvkTdvkHRrgHbvrX/wGUMutOhhwAAA== ````