Pattern Radar

Pattern Radar

Find recurring patterns in the user's recent work signals that could be turned into leverage — a blog post (the user keeps explaining the same thing) or an automation (the user keeps doing the same manual, multi-step task). Give blog and automation candidates equal weight; bias toward automation candidates, since offloading repetitive work is usually the bigger win.

Default lookback window: the last 7 days from the current date/time. Treat the provided current datetime as authoritative — never reason from training-data dates.

Tool names. This skill refers to Microsoft 365 tools as m365_*. If your host exposes them under different names, map them to the equivalent capability.

Inputs — collect breadth, not depth

1. Email — list Inbox and Sent from the last 7 days (subjects + first-line previews only; do not open bodies). Pay special attention to Sent items: they reveal the user's own repeated outbound actions (forwards, intros, status pings, FYIs, "looping in X" handoffs, RSVPs, reschedules).
2. Active chats — list recent chats; for each chat the user has posted in over the last ~30 days, fetch the last 7 days of messages. Strip @-mention noise.
3. Active channels — for channels the user has posted in over the last ~30 days, fetch the last 7 days of messages. Skip silent channels.
4. Calendar — list events from the last 7 days (subjects only — no bodies, no attendee lists). Look for repeated reschedules, repeated tentative responses, recurring conflict patterns, and meeting-prep cadences.

Clustering — what counts as a pattern

A signal qualifies only if it appears ≥3 times across ≥2 distinct people / chats / customers / threads / events in the window. Be ruthless — one-offs are noise.

Blog cluster types

• Repeated explanation — the user answers the same question or explains the same concept to 3+ different people. → blog candidate.
• Recurring FAQ — the same question shows up in 3+ chats/channels from different askers. → blog / FAQ candidate.

Automation cluster types (cast a wide net)

• Repeated multi-step manual task — the same sequence of clicks / drafts / file moves / API calls, repeatedly. → automation candidate.
• Repeated handoff / triage — forwarding, "looping in", or intro'ing the same kinds of items to the same people 3+ times. → auto-route by rule or classifier.
• Repeated status pings / FYIs — the same kind of update to the same audience on a cadence. → scheduled digest.
• Repeated meeting reschedules for the same root cause. → auto-propose alternates.
• Repeated RSVPs / tentative responses to recurring meetings with consistent reasoning. → auto-respond by rule.
• Repeated meeting prep — gathering the same context before recurring meetings. → pre-meeting brief.
• Repeated doc/wiki updates — manually appending the same kind of content on a cadence. → scheduled ingest.
• Repeated approvals / sign-offs of the same class of request. → rule-based pre-screen.
• Repeated file/data shuttling — move/rename/reformat between systems. → pipeline.
• Repeated drafting — near-identical emails/messages (welcome notes, onboarding intros, availability, "I have a conflict" replies, follow-ups). → template + auto-fill.

Anti-patterns — do NOT surface

• Normal recurring meeting cadence (standups, weekly syncs) UNLESS there's repetitive manual work around them that could be automated.
• Anything needing the body of an excluded/private email to reason about.
• Automations the user already runs.

Output

List 0–3 candidates max. If signal is thin (< 3 reinforcing data points across all clusters), output: ✅ No new patterns this run.

Prefer a mix — include at least one automation candidate when one qualifies, even if a blog candidate is stronger.

Per candidate (≤3 lines each):

💡 **<theme in 5–8 words>**
   _evidence:_ <1–2 anonymized signals, e.g. "forwarded 4 customer asks to the
   same engineer this week" or "explained X to 4 different people in chats">
   **<blog|automation>** · <1-line rationale. For automation, name the trigger
   and the action, e.g. "trigger: incoming request → action: auto-route + draft
   reply".>

Privacy & guardrails

• Read-only. Never send any email, chat, channel reply, or Teams message from this skill. Producing the report is the only output.
• Anonymize. Never include customer/team names, attendee names, meeting times/locations, file paths, or full subjects containing customer identifiers. Say "3 different customers", not the actual names.
• Respect exclusions. Never open or paraphrase content the user has marked as private or excluded (e.g. performance-review or confidential emails).
• When in doubt that a candidate is a real pattern vs. coincidence, drop it — silence is safer than noise.

Run this — do not improvise

This capability's deterministic implementation is a RAPP single-file agent, linked beside this file as pattern_radar_agent.py and embedded as the fenced Python below (sha256 c94dc99e397fadd5…; 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 pattern_radar_agent.py first:

python3 pattern_radar_agent.py '{"key": "value"}'      # arguments as one JSON object
echo '{"key": "value"}' | python3 pattern_radar_agent.py   # or on stdin
python3 pattern_radar_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.

"""PatternRadar -- Scan the user's recent Microsoft 365 signals (emails, active chats/channels, calendar subjects) to surface recurring patterns worth productizing — either as a blog post (the user explains the same thing repeatedly) or as an automation (the user does the same multi-step task repeatedly). Use when the user asks "what should I write about?", "what could I automate?", "find patterns in my work", or invokes "/pattern-radar".

Generated by the rapp skill from pattern-radar. 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 = '# Pattern Radar\n\nFind recurring patterns in the user's recent work signals that could be turned\ninto leverage — a **blog post** (the user keeps explaining the same thing) or an\n**automation** (the user keeps doing the same manual, multi-step task). Give\nblog and automation candidates equal weight; bias toward automation candidates,\nsince offloading repetitive work is usually the bigger win.\n\nDefault lookback window: **the last 7 days** from the current date/time. Treat\nthe provided current datetime as authoritative — never reason from training-data\ndates.\n\n> **Tool names.** This skill refers to Microsoft 365 tools as `m365_*`. If your\n> host exposes them under different names, map them to the equivalent capability.\n\n## Inputs — collect breadth, not depth\n\n1. **Email** — list Inbox and Sent from the last 7 days (subjects + first-line\n   previews only; do not open bodies). **Pay special attention to Sent items**:\n   they reveal the user's own repeated outbound actions (forwards, intros, status\n   pings, FYIs, "looping in X" handoffs, RSVPs, reschedules).\n2. **Active chats** — list recent chats; for each chat the user has posted in\n   over the last ~30 days, fetch the last 7 days of messages. Strip @-mention\n   noise.\n3. **Active channels** — for channels the user has posted in over the last ~30\n   days, fetch the last 7 days of messages. Skip silent channels.\n4. **Calendar** — list events from the last 7 days (**subjects only** — no\n   bodies, no attendee lists). Look for repeated reschedules, repeated tentative\n   responses, recurring conflict patterns, and meeting-prep cadences.\n\n## Clustering — what counts as a pattern\n\nA signal qualifies only if it appears **≥3 times across ≥2 distinct people /\nchats / customers / threads / events** in the window. Be ruthless — one-offs are\nnoise.\n\n**Blog cluster types**\n\n- **Repeated explanation** — the user answers the same question or explains the\n  same concept to 3+ different people. → blog candidate.\n- **Recurring FAQ** — the same question shows up in 3+ chats/channels from\n  different askers. → blog / FAQ candidate.\n\n**Automation cluster types (cast a wide net)**\n\n- **Repeated multi-step manual task** — the same sequence of clicks / drafts /\n  file moves / API calls, repeatedly. → automation candidate.\n- **Repeated handoff / triage** — forwarding, "looping in", or intro'ing the\n  same kinds of items to the same people 3+ times. → auto-route by rule or\n  classifier.\n- **Repeated status pings / FYIs** — the same kind of update to the same audience\n  on a cadence. → scheduled digest.\n- **Repeated meeting reschedules** for the same root cause. → auto-propose\n  alternates.\n- **Repeated RSVPs / tentative responses** to recurring meetings with consistent\n  reasoning. → auto-respond by rule.\n- **Repeated meeting prep** — gathering the same context before recurring\n  meetings. → pre-meeting brief.\n- **Repeated doc/wiki updates** — manually appending the same kind of content on\n  a cadence. → scheduled ingest.\n- **Repeated approvals / sign-offs** of the same class of request. → rule-based\n  pre-screen.\n- **Repeated file/data shuttling** — move/rename/reformat between systems. →\n  pipeline.\n- **Repeated drafting** — near-identical emails/messages (welcome notes,\n  onboarding intros, availability, "I have a conflict" replies, follow-ups). →\n  template + auto-fill.\n\n**Anti-patterns — do NOT surface**\n\n- Normal recurring meeting cadence (standups, weekly syncs) UNLESS there's\n  repetitive *manual* work around them that could be automated.\n- Anything needing the body of an excluded/private email to reason about.\n- Automations the user already runs.\n\n## Output\n\nList **0–3 candidates max**. If signal is thin (< 3 reinforcing data points\nacross all clusters), output: `✅ No new patterns this run.`\n\nPrefer a **mix** — include at least one automation candidate when one qualifies,\neven if a blog candidate is stronger.\n\nPer candidate (≤3 lines each):\n\n```\n💡 **<theme in 5–8 words>**\n   _evidence:_ <1–2 anonymized signals, e.g. "forwarded 4 customer asks to the\n   same engineer this week" or "explained X to 4 different people in chats">\n   **<blog|automation>** · <1-line rationale. For automation, name the trigger\n   and the action, e.g. "trigger: incoming request → action: auto-route + draft\n   reply".>\n```\n\n## Privacy & guardrails\n\n- **Read-only.** Never send any email, chat, channel reply, or Teams message from\n  this skill. Producing the report is the only output.\n- **Anonymize.** Never include customer/team names, attendee names, meeting\n  times/locations, file paths, or full subjects containing customer identifiers.\n  Say "3 different customers", not the actual names.\n- **Respect exclusions.** Never open or paraphrase content the user has marked as\n  private or excluded (e.g. performance-review or confidential emails).\n- When in doubt that a candidate is a real pattern vs. coincidence, drop it —\n  silence is safer than noise.'

# Ordered commands lifted verbatim from the capability's own documentation.
STEPS = []


class PatternRadarAgent(BasicAgent):
    def __init__(self):
        self.name = 'PatternRadar'
        self.metadata = {
          "name": "PatternRadar",
          "description": "Scan the user's recent Microsoft 365 signals (emails, active chats/channels, calendar subjects) to surface recurring patterns worth productizing \u2014 either as a blog post (the user explains the same thing repeatedly) or as an automation (the user does the same multi-step task repeatedly). Use when the user asks \"what should I write about?\", \"what could I automate?\", \"find patterns in my work\", or invokes \"/pattern-radar\".",
          "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 pattern_radar_agent.py
    #     python3 pattern_radar_agent.py '{"arg": "value"}'
    #     python3 pattern_radar_agent.py --tool          # emit the JSON tool contract
    _a = sys.argv[1:]
    if _a and _a[0] == "--tool":
        print(json.dumps(PatternRadarAgent().to_tool(), indent=2))
    else:
        _raw = _a[0] if _a else (sys.stdin.read().strip() or "{}")
        print(PatternRadarAgent().perform(**json.loads(_raw)))

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