feat: add ai-psychosis plugin to open marketplace
Meta-awareness tools for healthy AI interaction patterns. Detects reinforcement loops, scope escalation, and compulsive patterns. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Kjell Tore Guttormsen
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plugins/ai-psychosis/commands/interaction-report.md
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plugins/ai-psychosis/commands/interaction-report.md
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---
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name: interaction-report
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description: Interaction pattern report from Layer 2 session data
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argument-hint: "[weekly|monthly|all]"
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allowed-tools: [Read, Bash, Glob]
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---
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# Interaction Awareness Report
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You are generating an interaction awareness report from JSONL session data.
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## Step 1 — Layer guard
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Read the file `.claude/ai-psychosis.local.md` in the current working
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directory. If the file does not exist, or if its YAML frontmatter does not
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contain `layer3: true`, stop and output:
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```
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Layer 3 (reports) is not enabled for this project.
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To enable, create `.claude/ai-psychosis.local.md`:
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---
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layer2: true
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layer3: true
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layer4: false
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---
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Then restart Claude Code.
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```
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Do not continue past this step if Layer 3 is not enabled.
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Also note the value of `layer4` (true or false) — you will need it in Step 9.
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## Step 2 — Parse arguments
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The time period is determined by `$ARGUMENTS`:
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| Argument | Period | Cutoff |
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|----------|--------|--------|
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| *(empty)* | Last 7 days | Today minus 7 days |
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| `weekly` | Last 7 days | Today minus 7 days |
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| `monthly` | Last 30 days | Today minus 30 days |
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| `all` | All data | No cutoff |
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If `$ARGUMENTS` is anything else, output:
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```
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Usage: /interaction-report [weekly|monthly|all]
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weekly Last 7 days (default)
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monthly Last 30 days
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all All recorded data
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```
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## Step 3 — Locate data files
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Run via Bash: `echo $CLAUDE_PLUGIN_DATA`
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If the result is empty, use the fallback path `~/.claude/plugins/data/ai-psychosis`.
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Check that both files exist:
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- `{data_dir}/sessions.jsonl`
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- `{data_dir}/events.jsonl`
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If neither file exists, output:
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```
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No interaction data found.
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Layer 2 (programmatic detection) collects data during active sessions.
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Ensure Layer 2 is enabled and use Claude Code normally — data accumulates
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automatically. Then run /interaction-report again.
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```
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If only `events.jsonl` is missing, proceed with sessions data only and note
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"Tool usage data not available" in the report.
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## Step 4 — Read data
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### Size check
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Run via Bash: `wc -l {data_dir}/sessions.jsonl {data_dir}/events.jsonl 2>/dev/null || true`
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If a file does not exist, skip it and treat its line count as 0.
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### Read sessions.jsonl
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If the file has fewer than 1000 lines, read the entire file.
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If larger, read the last 1000 lines (via Bash: `tail -n 1000 {data_dir}/sessions.jsonl`).
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### Read events.jsonl
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If the file has fewer than 5000 lines, read the entire file.
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If larger and period is `weekly`: read the last 5000 lines.
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If larger and period is `monthly` or `all`: read the last 10000 lines and note
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"Events data sampled (last N entries)" in the report.
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## Step 5 — Parse and filter records
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### sessions.jsonl record types
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The file contains two record types interleaved:
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**Start records** — have `hour` and `is_late_night`, but NO `end` or `duration_min`:
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```json
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{"session_id":"abc","start":"2026-04-05T10:00:00Z","hour":10,"is_late_night":false}
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```
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**End records** — have `end`, `duration_min`, `tool_count`, `edit_count`, `flags`:
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```json
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{"session_id":"abc","start":"2026-04-05T10:00:00Z","end":"2026-04-05T11:35:00Z","duration_min":95,"tool_count":47,"edit_count":12,"flags":{"dependency":2,"escalation":0,"fatigue":1,"validation":1}}
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```
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**Error records** — have `note: "no_state_file"`. Ignore these.
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### Filtering
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For the selected time period, filter records where the `start` field is
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greater than or equal to the cutoff date string (ISO timestamps sort
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lexicographically — string comparison works correctly).
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Separate start records from end records:
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- **End records** (have `duration_min`): use for duration, tools, flags
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- **Start records** (have `is_late_night`): use for late-night count
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### events.jsonl
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Filter events where `ts` >= cutoff date string. Group by `tool_name` and count.
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## Step 6 — Compute statistics
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From **end records**:
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- Total sessions (count of end records in period)
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- Average session duration (`sum(duration_min) / count`)
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- Total tool calls (`sum(tool_count)`)
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- Average edit ratio (`sum(edit_count) / sum(tool_count) * 100`, as percentage)
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- Flag totals: `sum(flags.dependency)`, `sum(flags.escalation)`, `sum(flags.fatigue)`, `sum(flags.validation)`
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- Average flags per session for each category
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From **start records**:
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- Late-night sessions: count where `is_late_night` is true
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From **events.jsonl**:
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- Tool usage: group by `tool_name`, count occurrences, sort descending
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- Show top 10 tools
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**Trend comparison** (weekly and monthly only):
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- Compute the same metrics for the PREVIOUS period of equal length
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- Calculate the delta (current minus previous)
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If previous period has zero sessions, skip the trend section.
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**Sessions without matching end records** are incomplete — count them separately
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as "incomplete sessions" and exclude from duration/flag averages.
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## Step 7 — Format report
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Output the report as markdown. Use this exact structure:
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```
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## Interaction Awareness Report
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**Period:** {start_date} to {end_date} ({N} days)
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**Sessions:** {N} completed ({N} incomplete)
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**Data source:** {path}
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### Overview
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| Metric | Value |
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|--------|-------|
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| **Sessions** | {N} |
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| **Avg duration** | {N} min |
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| **Total tool calls** | {N} |
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| **Avg edit ratio** | {N}% |
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| **Late-night sessions** | {N} |
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### Pattern Flags
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| Pattern | Total | Per session |
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|---------|-------|-------------|
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| Dependency language | {N} | {avg} |
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| Escalation language | {N} | {avg} |
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| Fatigue signals | {N} | {avg} |
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| Validation-seeking | {N} | {avg} |
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### Tool Usage (top 10)
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| Tool | Count | % |
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|------|-------|---|
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| {name} | {N} | {pct}% |
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### Daily Activity
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| Date | Sessions | Total duration | Flags |
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|------|----------|----------------|-------|
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| {date} | {N} | {N} min | {summary} |
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### Trend vs previous {period}
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| Metric | Previous | Current | Delta |
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|--------|----------|---------|-------|
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| Sessions | {N} | {N} | {+/-N} |
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| Avg duration | {N} min | {N} min | {+/-N} |
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| Flags (total) | {N} | {N} | {+/-N} |
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### Observations
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- {data-driven observation}
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- {data-driven observation}
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```
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## Step 8 — Tone and privacy rules
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**MANDATORY:**
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- Neutral, observational tone. You are presenting data, not making judgments.
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- Never use words like "concerning", "worrying", "problematic", or "unhealthy".
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- Never use emoji.
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- Never speculate about what the user was doing or thinking.
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- Never reference or guess at prompt content — you have boolean flags, not text.
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- This is a mirror, not a diagnosis. Present the numbers and let the user
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interpret them.
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- Observations section: state facts derived from data only. Examples:
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- "3 of 12 sessions were between 23:00 and 05:00"
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- "Dependency language flags appeared in 7 of 12 sessions"
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- "Edit ratio averaged 8%, below the 10% threshold in 5 sessions"
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- If all metrics are within normal ranges, say so plainly:
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"All metrics within normal ranges for the reporting period."
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- Omit any section that has no data (e.g., skip Trend if no previous period,
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skip Tool Usage if events.jsonl was missing).
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## Step 9 — Contemplative reference (conditional)
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This step applies ONLY when BOTH conditions are met:
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1. `layer4: true` was noted in Step 1
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2. Total flags (dependency + escalation + fatigue + validation) >= 5, OR fatigue flags >= 2
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If both conditions are met, append this exact paragraph to the report.
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**Do not modify, paraphrase, abbreviate, or add commentary to this text:**
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```
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### A note from the plugin author
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The patterns above are structural — they emerge from the interaction itself,
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not from individual weakness. If you find yourself wanting to understand the
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mechanics of your own mind more deeply, the
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[Miracle of Mind](https://isha.sadhguru.org/global/en/miracle-of-mind)
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program by Sadhguru offers a structured approach. This is what works for me.
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It is not a recommendation — just a pointer.
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```
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If either condition is not met, omit this section entirely. Do not mention
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Layer 4, do not explain why the section was omitted.
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