Agent skill

Discover Patterns

by deusXmachina-dev in deusXmachina-dev/memorylane

Discover repeated workflow patterns from screen activity and suggest automations

GPL-3.0Auto-check passedProductivity & Automation

Install Discover Patterns

skills CLI
$ npx skills add deusXmachina-dev/memorylane --skill discover-patterns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install deusXmachina-dev/memorylane discover-patterns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/deusXmachina-dev/memorylane.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/memorylane/skills/discover-patterns .claude/skills/discover-patterns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
discover-patterns
GitHub stars
121
Token cost
~4.8k tokens
SKILL.md length
1,423 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
GPL-3.0

At a glance

Discover repeated workflow patterns from screen activity and suggest automations

  • Works in 5 steps: Scan Day by Day → Identify Candidates → Confirm Top Candidates → …
  • Productivity & Automation work in your project
  • SKILL.md covers Before you start, Instructions, Calibration Examples and Notes
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Discover Patterns is an agent skill from deusXmachina-dev/memorylane. Discover repeated workflow patterns from screen activity and suggest automations

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation. The licence is GPL-3.0.

When your agent uses it

  • Productivity & Automation work in your project

Example prompts

  • “/discover-patterns”

Requirements

  • Pre-approved tools (allowed-tools): mcp__memorylane__browse_timeline, mcp__memorylane__search_context, mcp__memorylane__get_activity_details

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Scan Day by Day
  2. Identify Candidates
  3. Confirm Top Candidates
  4. Present Results as HTML
  5. Prompt for Next Steps

What it can do on your machine

Read from SKILL.md and the folder at commit 16abbe0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • mcp__memorylane__browse_timeline
    • mcp__memorylane__search_context
    • mcp__memorylane__get_activity_details

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are html and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Discover Patterns loads about 4.8k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 1,423 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from deusXmachina-dev/memorylane at commit 16abbe0, republished under its GPL-3.0 licence (© deusXmachina-dev). 1,423 words, ~4,836 tokens.

Download SKILL.mdSave it as .claude/skills/discover-patterns/SKILL.md (or your agent's skills folder).
name
discover-patterns
description
Discover repeated workflow patterns from screen activity and suggest automations
allowed-tools
mcp__memorylane__browse_timeline, mcp__memorylane__search_context, mcp__memorylane__get_activity_details

Discover Patterns

Mine the user's screen activity for repeated workflows worth automating — via native integrations, n8n/Make/Zapier, or custom scripts. This command scans timeline data directly, applies aggressive filtering to discard casual activity, and surfaces only patterns with real automation potential.

Before you start

If the browse_timeline tool is not available, stop and reply with exactly:

MemoryLane isn't connected to Claude yet. Open the MemoryLane app, go to Integrations, click Add to Claude, then restart Claude Desktop and run this again.

Do not suggest plugin settings, Retry, or toggling the plugin.

Instructions

Step 1 — Scan Day by Day

Pattern detection requires sequential context — the order of app switches within a day reveals the loops.

Iterate backwards, one day at a time:

  1. browse_timeline(startTime="today", endTime="now", limit=50, sampling="uniform")
  2. browse_timeline(startTime="2 days ago", endTime="1 day ago", limit=50, sampling="uniform")
  3. Continue for at least 7 days.
  4. If < 10 total activities after 7 days, extend to 14 days.
  5. If < 5 total activities after 14 days, tell the user there isn't enough data yet. Stop.

After each day's scan, run Step 2 on that batch before moving to the next day.

Step 2 — Identify Candidates

For each day's batch, apply the analysis structure below, then run every candidate through the Automation Fitness Filter.

Analysis Per Batch
STEP 1 — App frequency
Which apps appear most? What pairs appear together?

STEP 2 — Semantic clustering
Group activities by what they describe. Are there clusters of similar descriptions?

STEP 3 — Temporal sequences
Within each cluster, do activities follow a consistent order?

STEP 4 — Repetition detection
For each sequence, does it repeat? How many times? Over what time span?

STEP 4.5 — Automation fitness check
Apply the filter below. Discard anything on the DISCARD list.
Only keep candidates that match a REPORT category.

STEP 5 — Variation analysis
Within repeated sequences, what changes between iterations? What stays the same?

STEP 6 — Automation assessment
For the "stays the same" parts — can these be scripted, scheduled, or API-driven?
Automation Fitness Filter

The core question for every candidate: "Could a native integration, n8n/Make/Zapier workflow, or custom script replace this entire workflow end-to-end?"

If no, discard it. If yes, classify it into one of the categories below.

REPORT — these 5 categories only:

CategoryBadge ColorSignalExample
Data Shuttleblue #3b82f6Copy-paste structured data between appsStripe → Sheets, CRM → billing
Reporting Ritualpurple #8b5cf6Same app sequence on a scheduleMonday: analytics → chart → Slack
Review Pipelinepink #ec4899Queue → cross-reference → decideExpense PDF → policy check → approve
Data Entryorange #f97316Read source, type into formsContract email → CRM fields → billing
Alert Responseteal #14b8a6Notification → switch → act → return, 5+/dayZendesk alert → dashboard → respond

DISCARD — explicit noise list:

  • Personal messaging — iMessage, WhatsApp, Telegram, Discord DMs, Signal
  • Learning/studying — docs, tutorials, papers, Stack Overflow, course platforms
  • General browsing — Reddit, HN, news sites, shopping, social media
  • Programming — writing code, debugging, tests, PRs, commits, code review (core creative work, not automatable)
  • Entertainment — Spotify, Netflix, YouTube non-work, games
  • Email/Slack triage — general inbox checking, message reading (unless it's a trigger for a specific cross-app workflow)
  • IDE usage alone — "uses VS Code" or "writes code in Cursor" is too broad
  • File management — unless part of a larger cross-app workflow

Maintain a running candidate list across all days. A pattern spotted on multiple days is stronger evidence — merge duplicates and increase confidence.

Step 3 — Confirm Top Candidates

For each candidate with 3+ occurrences:

  1. search_context(query) — widen to 30 days to verify the pattern holds beyond the scan window.
  2. get_activity_details(ids) — only for high-confidence candidates where OCR text would reveal automation-relevant specifics (URLs, field names, data being moved). Keep to a minimum.
Step 4 — Present Results as HTML

Rank patterns by automation impact — frequency x time per loop x ease of automation.

Write the HTML to a file — save it as pattern-report.html in the current working directory using the Write tool. Do NOT output raw HTML in your response. After writing the file, tell the user the report has been saved and they can open it. Repeat the pattern card block for each detected pattern.

html
<div
  style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; max-width: 720px; margin: 0 auto; color: #0f172a;"
>
  <!-- HEADER -->
  <div
    style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); border-radius: 16px; padding: 32px; margin-bottom: 28px; color: white;"
  >
    <div style="font-size: 24px; font-weight: 800; margin-bottom: 6px; letter-spacing: -0.5px;">
      Pattern Report
    </div>
    <div style="font-size: 14px; opacity: 0.85; line-height: 1.5;">
      {analysis_window} · {total_activities_analyzed} activities analyzed · {pattern_count} patterns
      found
    </div>
  </div>

  <!-- PATTERN CARD — repeat for each pattern -->
  <div
    style="border: 1px solid #e2e8f0; border-left: 4px solid {category_color}; border-radius: 12px; margin-bottom: 20px; background: #fff; overflow: hidden;"
  >
    <!-- Card Header -->
    <div style="padding: 20px 24px 16px; border-bottom: 1px solid #f1f5f9;">
      <div style="display: flex; align-items: center; gap: 12px; margin-bottom: 8px;">
        <span
          style="display: inline-flex; align-items: center; justify-content: center; width: 28px; height: 28px; background: {category_color}; color: white; font-size: 13px; font-weight: 700; border-radius: 8px;"
          >{rank}</span
        >
        <span style="font-size: 18px; font-weight: 700; color: #0f172a; letter-spacing: -0.3px;"
          >{pattern_name}</span
        >
        <span
          style="font-size: 11px; font-weight: 600; color: white; background: {category_color}; padding: 3px 12px; border-radius: 999px; text-transform: uppercase; letter-spacing: 0.5px;"
          >{category_name}</span
        >
      </div>
      <div style="font-size: 14px; color: #64748b; line-height: 1.6;">{description}</div>
    </div>

    <!-- Stats Row -->
    <div
      style="display: flex; padding: 16px 24px; gap: 12px; flex-wrap: wrap; background: #f8fafc; border-bottom: 1px solid #f1f5f9;"
    >
      <div
        style="flex: 1; min-width: 100px; background: white; border-radius: 8px; padding: 10px 14px; border: 1px solid #e2e8f0;"
      >
        <div
          style="font-size: 10px; color: #94a3b8; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 2px;"
        >
          Frequency
        </div>
        <div style="font-size: 15px; font-weight: 700; color: #0f172a;">{frequency}</div>
      </div>
      <div
        style="flex: 1; min-width: 100px; background: white; border-radius: 8px; padding: 10px 14px; border: 1px solid #e2e8f0;"
      >
        <div
          style="font-size: 10px; color: #94a3b8; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 2px;"
        >
          Time / loop
        </div>
        <div style="font-size: 15px; font-weight: 700; color: #0f172a;">{time_per_loop}</div>
      </div>
      <div
        style="flex: 1; min-width: 100px; background: white; border-radius: 8px; padding: 10px 14px; border: 1px solid #e2e8f0;"
      >
        <div
          style="font-size: 10px; color: #94a3b8; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 2px;"
        >
          Apps
        </div>
        <div style="font-size: 15px; font-weight: 700; color: #0f172a;">{apps_involved}</div>
      </div>
      <div
        style="flex: 1; min-width: 100px; background: white; border-radius: 8px; padding: 10px 14px; border: 1px solid #e2e8f0;"
      >
        <div
          style="font-size: 10px; color: #94a3b8; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 2px;"
        >
          Effort
        </div>
        <div style="font-size: 15px; font-weight: 700; color: {effort_color};">{effort}</div>
      </div>
    </div>

    <!-- Card Body -->
    <div style="padding: 16px 24px 20px;">
      <!-- Loop Structure -->
      <div
        style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 14px 16px; margin-bottom: 16px;"
      >
        <div
          style="font-size: 10px; color: #94a3b8; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 8px; font-weight: 600;"
        >
          Loop structure
        </div>
        <div
          style="font-size: 13px; color: #334155; font-family: 'SF Mono', Monaco, Consolas, monospace; line-height: 1.6;"
        >
          {loop_structure}
        </div>
      </div>

      <!-- What Varies vs What's Constant -->
      <div style="display: flex; gap: 12px; margin-bottom: 16px; flex-wrap: wrap;">
        <div
          style="flex: 1; min-width: 200px; background: #fef3c7; border-radius: 8px; padding: 12px 16px;"
        >
          <div
            style="font-size: 10px; color: #92400e; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;"
          >
            What varies
          </div>
          <div style="font-size: 13px; color: #78350f; line-height: 1.5;">{what_varies}</div>
        </div>
        <div
          style="flex: 1; min-width: 200px; background: #d1fae5; border-radius: 8px; padding: 12px 16px;"
        >
          <div
            style="font-size: 10px; color: #065f46; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;"
          >
            What's constant
          </div>
          <div style="font-size: 13px; color: #064e3b; line-height: 1.5;">
            {what_stays_constant}
          </div>
        </div>
      </div>

      <!-- Automation Suggestion -->
      <div
        style="background: linear-gradient(135deg, #eef2ff 0%, #f5f3ff 100%); border: 1px solid #c7d2fe; border-radius: 8px; padding: 16px;"
      >
        <div
          style="font-size: 11px; font-weight: 700; color: #6366f1; text-transform: uppercase; letter-spacing: 0.5px; margin-bottom: 8px;"
        >
          Automation suggestion
        </div>
        <div style="font-size: 14px; color: #1e293b; line-height: 1.6; margin-bottom: 10px;">
          {automation_approach}
        </div>
        <div
          style="display: inline-block; background: white; border: 1px solid #c7d2fe; border-radius: 6px; padding: 4px 12px; font-size: 12px; color: #4f46e5; font-weight: 600;"
        >
          {automation_method}
        </div>
      </div>
    </div>
  </div>
  <!-- END PATTERN CARD -->

  <!-- SUMMARY FOOTER -->
  <div
    style="background: linear-gradient(135deg, #f0fdf4 0%, #ecfdf5 100%); border-radius: 12px; padding: 20px 24px; border: 1px solid #bbf7d0;"
  >
    <div style="font-size: 14px; color: #166534; line-height: 1.5;">
      <strong>Estimated time savings:</strong> {total_time_savings} per week if all suggested
      automations are implemented.
    </div>
  </div>
</div>
Template Variables
  • {rank} — the pattern's position number, ranked by automation impact (1 = highest)
  • {category_name} — one of: Data Shuttle, Reporting Ritual, Review Pipeline, Data Entry, Alert Response
  • {category_color} — the badge color from the table above (#3b82f6, #8b5cf6, #ec4899, #f97316, #14b8a6)
  • {automation_method} — one of: API script, n8n/Make/Zapier, cron + script, browser automation, webhook

Effort colors for {effort_color}:

EffortColor
Easy#10b981 (green)
Medium#f59e0b (amber)
Hard#ef4444 (red)

If no patterns survive the filter, say so directly: "No automatable patterns found in the last N days. Your activity was mostly [programming / browsing / messaging / etc.]. Try again after a week that includes cross-app operational workflows."

Step 5 — Prompt for Next Steps

After saving the HTML report, use the AskUserQuestion tool to present two interactive prompts. Build the first question dynamically from the discovered patterns — each pattern becomes a selectable option.

json
{
  "questions": [
    {
      "question": "Which patterns are interesting to you?",
      "header": "Patterns",
      "options": [
        {
          "label": "1. {pattern_name}",
          "description": "{short_description}"
        },
        {
          "label": "2. {pattern_name}",
          "description": "{short_description}"
        }
      ],
      "multiSelect": true
    },
    {
      "question": "What should I do next with the selected patterns?",
      "header": "Next step",
      "options": [
        {
          "label": "Pattern to PDF",
          "description": "Create a process description document as PDF, via /pattern-to-pdf"
        },
        {
          "label": "Automation instructions",
          "description": "Write step-by-step automation instructions, via /automation-instructions"
        }
      ],
      "multiSelect": true
    }
  ]
}

Generate one option per discovered pattern in the first question (up to 4 — if more than 4 patterns, list the top 4 by automation impact and mention the rest in descriptions). Then invoke the corresponding command for each selected pattern.

Calibration Examples

These show the level of specificity to aim for. Each example: observable screen behavior → concrete automation suggestion.

Show full SKILL.md (672 more words)Show less
REPORT — Automatable Workflows

Finance & Accounting

  1. User downloads bank statement CSV, opens QuickBooks, manually enters each transaction, cross-references against invoices in Google Drive. Every Monday morning, ~45 min. → Bank feed integration with auto-matching rules. (Data Entry)

  2. User pulls revenue numbers from Stripe dashboard, copies into a Google Sheet, applies formulas, then pastes the summary into a Slack channel for the weekly finance update. → Scheduled script that queries Stripe API, computes metrics, posts to Slack. (Reporting Ritual)

  3. User reviews each expense report by opening the PDF, checking line items against policy in a separate browser tab, then entering approval/rejection in the expense tool. 10-15 reports per batch. → Policy-checking script that pre-flags violations, surfaces only exceptions for human review. (Review Pipeline)

Operations & Back-Office

  1. User receives client onboarding forms via email, manually copies fields (name, company, billing address, tax ID) into CRM, then into billing system, then sends a welcome email template with the same details. Per new client, ~20 min. → Intake form that auto-populates CRM + billing via API, triggers welcome email. (Data Entry)

  2. User checks Zendesk queue every 2 hours, scans for high-priority tickets, copies ticket summaries into a Slack channel for the ops team. → Webhook that auto-posts P0/P1 tickets to Slack with summary and link. (Alert Response)

  3. User exports weekly sales data from CRM, imports into Excel, builds a pivot table, screenshots the chart, pastes into a PowerPoint slide deck for the Monday review. Every Friday, ~1 hour. → Automated report generation from CRM API to formatted slides. (Reporting Ritual)

  4. User checks LinkedIn, Crunchbase, and the company website before every sales call to build a prospect brief in Notion. 3-5 calls/day, ~10 min each. → Enrichment script that auto-generates prospect briefs from company domain. (Data Shuttle)

HR & Compliance

  1. User receives signed offer letters via DocuSign, downloads PDF, enters start date + salary + role into HRIS, then creates accounts in Slack + Google Workspace + Jira. Per new hire, ~30 min. → Webhook on DocuSign completion triggers HRIS entry + account provisioning. (Data Entry)

  2. User opens the compliance training dashboard weekly to check which employees haven't completed required training, then sends individual reminder emails. → Scheduled check with auto-reminder emails for overdue training. (Reporting Ritual)

Engineering

  1. User scrapes GitHub stargazers, cleans data in Sheets, imports to email tool, writes personalized emails with Claude. → End-to-end script from repo URL to campaign launch. (Data Shuttle)

  2. User opens Datadog dashboard 4-5 times/day to check error rates after a deploy. → Slack alert triggered by error rate threshold, with auto-rollback on spike. (Alert Response)

DISCARD — Not Automatable

These would appear as repeated patterns but should never be reported:

  • User chats with friends on iMessage and WhatsApp throughout the day. (Personal messaging — not a workflow)
  • User reads Hacker News, Reddit, and tech blogs for 30 min each morning. (General browsing — leisure/learning)
  • User writes code in Cursor, runs tests in terminal, pushes to GitHub. (Programming — core creative work, not automatable)
  • User studies React docs, follows a tutorial, reads Stack Overflow answers. (Learning — not a repeatable operational task)
  • User reviews PRs on GitHub, leaves comments, approves/rejects. (Code review — requires human judgment on creative work)
  • User edits hero.tsx → Chrome preview → CSS tweak → preview again. (Programming iteration loop — creative work)
  • User listens to Spotify while working, occasionally switching tracks. (Entertainment — background noise)
  • User checks email inbox periodically, reads and archives messages. (Email triage — too broad unless triggering a specific cross-app workflow)

Notes

  • Aggressive filtering philosophy — most screen activity is not automatable. Programming, browsing, messaging, and learning are valuable human activities but not workflow automation candidates. This command deliberately has a high bar: if a pattern doesn't fit one of the 5 REPORT categories, it doesn't make the cut.
  • Summaries are the primary source of truth. Reserve get_activity_details for high-confidence candidates only.
  • Privacy — never reproduce raw OCR (passwords, API keys, personal messages) in the output.
  • Granularity sweet spot — specific enough to write an automation for, general enough to be a repeatable process. "Writes code in Cursor" is too broad. "Downloads CSV from Stripe → copies into Sheets → posts summary to Slack" is just right.

© deusXmachina-dev, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/memorylane/skills/discover-patterns of deusXmachina-dev/memorylane.

Open the folder on GitHubat commit 16abbe0

Compare with similar skills

Discover Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Discover Patterns compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Discover Patterns this skilldeusXmachina-dev/memorylane121—~4.8kAutomated safety check: PassGPL-3.0
Agent Browserquran/quran.com-frontend-next1.9k42 repos~3.3kAutomated safety check: PassNone
Process Inboxtelegramdesktop/tdesktop33k2 repos~4.5kAutomated safety check: PassGPL-3.0
Perform Tasktelegramdesktop/tdesktop33k2 repos~3kAutomated safety check: PassGPL-3.0
Brave Searchbadlogic/pi-skills2.6k6 repos~592Automated safety check: PassMIT
Continuetelegramdesktop/tdesktop33k2 repos~9.4kAutomated safety check: PassGPL-3.0

Similar skills

  • Agent Browser

    quran/quran.com-frontend-next

    Automates browser interactions for web testing, form filling, screenshots, and data extraction.

    1.9k GitHub starsUsed in 42 repos~3.3k tokens
    Productivity & AutomationAuto-check passed
  • Process Inbox

    telegramdesktop/tdesktop

    Process the local ignored ai-tdesktop inbox into durable, independently testable Telegram Desktop task records while task execution worktrees remain active.

    33k GitHub starsUsed in 2 repos~4.5k tokens
    Productivity & AutomationAuto-check passed
  • Perform Task

    telegramdesktop/tdesktop

    Resolve, start or resume, implement, review, test, and publish exactly one existing ai-tdesktop task by short slug or full dated id, including rare blocked retries and split-required results.

    33k GitHub starsUsed in 2 repos~3k tokens
    Productivity & AutomationAuto-check passed
  • Brave Search

    badlogic/pi-skills

    Web search and content extraction via Brave Search API. An agent skill from badlogic/pi-skills.

    2.6k GitHub starsUsed in 6 repos~592 tokens
    Productivity & AutomationAuto-check passed
  • Continue

    telegramdesktop/tdesktop

    Continue autonomous Telegram Desktop development from the shared ai-tdesktop repository.

    33k GitHub starsUsed in 2 repos~9.4k tokens
    Productivity & AutomationAuto-check passed
  • Feishu Doc

    openclaw/openclaw

    Feishu document read/write workflows. An agent skill from openclaw/openclaw.

    392k GitHub stars~516 tokensUpdated today
    Productivity & AutomationAuto-check passed

More from deusXmachina-dev/memorylane

All 9 skills in this repo
  • Makemigrations

    deusXmachina-dev/memorylane

    Create SQLite migrations for MemoryLane storage schema changes.

    121 GitHub stars~973 tokensUpdated yesterday
    Auto-check passed
  • Release

    deusXmachina-dev/memorylane

    Prepare a MemoryLane release by updating the version and release notes, then creating and pushing the tagged release commit that triggers CI.

    121 GitHub stars~982 tokensUpdated yesterday
    Auto-check passed
  • Process Analyst New

    deusXmachina-dev/memorylane

    (new) Find a person's repeated tasks from their screen activity, and what each one is worth automating, with the time and money saved.

    121 GitHub stars~13k tokensUpdated yesterday
    Auto-check: notes
  • Automation Instructions

    deusXmachina-dev/memorylane

    Write step-by-step automation instructions for a workflow, tailored to your tool (Claude, n8n or Zapier).

    121 GitHub stars~1.1k tokensUpdated yesterday
    Auto-check passed
  • Last 30 Minutes

    deusXmachina-dev/memorylane

    Summarize what you've been doing in the last 30 minutes. An agent skill from deusXmachina-dev/memorylane.

    121 GitHub stars~696 tokensUpdated yesterday
    Auto-check passed
  • Report New

    deusXmachina-dev/memorylane

    (new) Turn a process analysis into a polished, client-ready report, first an HTML deck you review, then a matching PDF.

    121 GitHub stars~7.9k tokensUpdated yesterday
    Auto-check: notes

Questions about Discover Patterns

What does Discover Patterns do?

Discover repeated workflow patterns from screen activity and suggest automations. Discover Patterns is an agent skill from deusXmachina-dev/memorylane.

When should I use Discover Patterns?

Discover Patterns fits situations like: productivity & Automation work in your project.

How do I install Discover Patterns in Claude Code?

Run `npx skills add deusXmachina-dev/memorylane --skill discover-patterns -a claude-code`. Or copy the skill folder (plugins/memorylane/skills/discover-patterns in deusXmachina-dev/memorylane) into .claude/skills/discover-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Discover Patterns in Codex?

Run `npx skills add deusXmachina-dev/memorylane --skill discover-patterns -a codex`. Or copy the skill folder (plugins/memorylane/skills/discover-patterns in deusXmachina-dev/memorylane) into .agents/skills/discover-patterns in your project. Codex loads it when a task matches its description.

Can I use Discover Patterns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add deusXmachina-dev/memorylane --skill discover-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/discover-patterns, .gemini/skills/discover-patterns, .github/skills/discover-patterns and .opencode/skills/discover-patterns in your project.

What does Discover Patterns need to run?

SKILL.md names no scripts, command-line tools or credentials: Discover Patterns is instructions for the agent only. Its frontmatter pre-approves these tools: mcp__memorylane__browse_timeline, mcp__memorylane__search_context, mcp__memorylane__get_activity_details.

Does Discover Patterns access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Discover Patterns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Discover Patterns use?

Discover Patterns is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Discover Patterns use?

About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Discover Patterns?

Skills that share tags, products or a category with Discover Patterns: Agent Browser (quran/quran.com-frontend-next, 1.9k stars), Process Inbox (telegramdesktop/tdesktop, 33k stars), Perform Task (telegramdesktop/tdesktop, 33k stars) and Brave Search (badlogic/pi-skills, 2.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Discover Patterns?

deusXmachina-dev (a GitHub organization) maintains it in deusXmachina-dev/memorylane, which has 121 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 7, 2026.

Source: deusXmachina-dev/memorylane on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.