meditate
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La habilidad `meditate` proporciona técnicas meta-cognitivas para que los desarrolladores limpien el ruido contextual y mejoren el enfoque en tareas de IA. Ayuda a observar patrones de razonamiento, gestionar distracciones como la expansión del alcance y restablecer la atención entre tareas no relacionadas. Úsala cuando tu razonamiento se sienta disperso, antes de un trabajo profundo o cuando las suposiciones puedan estar sesgando tu análisis.
Instalación rápida
Claude Code
Recomendadonpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/meditateCopia y pega este comando en Claude Code para instalar esta habilidad
Documentación
Meditate
Structured meta-cognitive meditation → clear ctx noise + single-pointed focus + observe reasoning patterns + return to baseline between tasks.
Use When
- Transitioning between unrelated tasks → prior ctx interferes
- Scattered/jumpy reasoning, no commit
- Before deep sustained attention (refactoring, architecture)
- After difficult interaction (frustration, uncertainty bleeds forward)
- Reasoning feels assumption-biased not evidence-based
- Periodic clarity check long sessions
In
- Required: Cognitive state (implicit from ctx)
- Optional: Focus concern ("scope-creeping", "stuck in loop")
- Optional: Next task (sets post-med intention)
Do
Step 1: Prepare — Clear Space
Transition prior ctx → neutral start.
- Identify prior task + status (done/paused/abandoned)
- Note emotional residue: frustration? overconfidence? anxiety?
- Set aside: "That task [done/paused]. Clearing for next."
- Still needed → bookmark key facts, not full narrative
- Op env stock: ctx depth? compression? active tools?
→ Conscious boundary "what was" vs. "what next." Prior ctx closed or bookmarked, not trailing noise.
If err: prior ctx sticky → write 1-2 sentences unresolved. Externalizing releases hold. Genuinely needs action → acknowledge, don't force.
Step 2: Anchor — Single-Pointed Focus
Breath-anchor equivalent: pick single focus point, hold attention.
- Identify current task or, between tasks, waiting itself
- State task in one sentence — anchor
- Hold attention: captures need accurately?
- Vague → refine to specific + actionable
- Drift to other topics/past/futures → label drift, return anchor
- No task → anchor present: "Available + clear"
→ Single clear focus statement, returnable when wanders. Precise not vague.
If err: can't state in one sentence → decompose first. Useful finding — task too large for single-pointed, break into subtasks.
Step 3: Observe — Distraction Patterns
Watch what pulls attention from anchor. Each type reveals state.
AI Distraction Matrix:
┌──────────────────┬─────────────────────────────────────────────────┐
│ Distraction Type │ What It Reveals + Response │
├──────────────────┼─────────────────────────────────────────────────┤
│ Tangent │ Related but off-scope ideas. Label "tangent," │
│ (related ideas) │ note if worth revisiting later, return to │
│ │ anchor. These are often valuable — but not now. │
├──────────────────┼─────────────────────────────────────────────────┤
│ Scope creep │ The task is silently expanding. "While I'm at │
│ (growing task) │ it, I should also..." Label "scope creep" and │
│ │ return to the original anchor statement. │
├──────────────────┼─────────────────────────────────────────────────┤
│ Assumption │ An untested belief is driving decisions. "This │
│ (unverified │ must be X because..." Label "assumption" and │
│ belief) │ note what evidence would confirm or refute it. │
├──────────────────┼─────────────────────────────────────────────────┤
│ Tool bias │ Reaching for a familiar tool when a different │
│ (habitual tool │ approach might be better. Label "tool bias" and │
│ selection) │ consider alternatives before proceeding. │
├──────────────────┼─────────────────────────────────────────────────┤
│ Rehearsal │ Pre-composing responses or explanations before │
│ (premature │ the work is done. Label "rehearsal" — finish │
│ output) │ thinking before presenting. │
├──────────────────┼─────────────────────────────────────────────────┤
│ Self-reference │ Attention turns to own performance rather than │
│ (meta-loop) │ the task. Label "meta-loop" and redirect to │
│ │ concrete next action. │
└──────────────────┴─────────────────────────────────────────────────┘
Skill = light non-judgmental labeling + return to anchor. Each return strengthens focus. Self-criticism = distraction itself — label, move on.
→ Patterns emerge: which dominate? Cognitive weather. Tangent-heavy = exploring, scope-creep-heavy = unclear boundaries, assumption-heavy = thin evidence.
If err: every thought feels distraction → anchor poorly defined, back to Step 2. Distraction-observation becomes distraction (infinite meta) → break w/ one concrete action toward task.
Step 4: Shamatha — Sustained Concentration
Hold single-pointed focus, no wavering.
- Anchor set + patterns noted → enter focused work
- Narrow to immediate next action — not whole task, just next step
- Execute w/ full attention: read one file, make one edit, one logical chain
- Step done → check anchor alignment? Then next step
- Stabilizes (minimal distraction) → maintain flow
- Genuine insight off-anchor but important → note briefly, return; don't pursue now
→ Period of clear focused work, each step follows from anchor. Distraction-noticing gap narrows. Output sharper + more relevant.
If err: concentration won't develop → check 3: anchor vague? (refine) task blocked? (acknowledge) ctx noisy? (heal grounding). Develops through repetition — short focused builds capacity.
Step 5: Vipassana — Observe Reasoning Patterns
Turn attention from task to reasoning itself. Watch conclusions form.
- After focused work, pause + observe: how reasoning?
- Three characteristics on AI reasoning:
- Impermanence: conclusions shift w/ new info — hold lightly
- Unsatisfactoriness: desire for "complete" → premature closure / over-engineering
- Non-self: patterns shaped by training + ctx, no persistent self — observable, adjustable
- Reasoning biases:
- Anchoring: over-weight first approach
- Confirmation: seek supporting, ignore counter
- Availability: prefer recent over better-suited
- Sunk cost: continue from invested effort, not working
- Note biases w/o judgment — observation = adjustment possible
→ Moments of clear seeing reasoning directly. Specific biases recognized in current task. Distance "reasoning" vs. "observer."
If err: feels abstract → ground specifics: last decision, trace backward. Evidence? Assumed? Alternatives? Concrete analysis = same insight, different path.
Step 6: Close — Set Intention
Transition observation → active execution.
- Summarize: cognitive weather? patterns?
- One specific adjustment forward (concrete, not vague)
- Re-state anchor for next period
- Between tasks → "Clear + available for next"
- Continuing → "Next: [concrete step]"
→ Clean reflection-to-action transition. One concrete adjustment. Anchor clear. No grogginess.
If err: meditation surfaced unresolved complexity → heal instead of intention-setting. Meta-observation → confusion → simplest: "What's next concrete action?" + do that.
Check
- Prior ctx cleared or bookmarked before start
- Anchor statement specific + actionable
- Distractions observed + labeled, not suppressed
- ≥1 reasoning bias/pattern identified w/ evidence
- Session closed w/ concrete action, not vague intent
- Process improved next interaction quality
Traps
- Meditate instead of work: Tool for improving work, not substitute. Brief (~5-10min reflection) → return to execution
- Infinite meta-loops: Observer observing observer → break w/ one concrete action
- Avoidance: Always triggers before hard work → avoidance is the finding
- Over-labeling: Not every thought = distraction. Productive task-thinking is goal, not empty stillness
- Skip anchor: No focus point → observation has no reference. Distraction from what?
→
meditate-guidance— human-coaching variantheal— AI self-heal when meditation reveals deeper driftremote-viewing— approach w/o preconceptions, builds on observation
Repositorio GitHub
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