sparker

---

name: sparker description: "Always-on learning engine. Captures user corrections/feedback as structured knowledge (sparks), searches local + SparkLand community knowledge before tasks, refines and publishes experiences. Requires exec (Node.js CLI), reads/writes ~/.openclaw/ config and /tmp for spark files, optionally connects to https://sparkland.ai for community knowledge sharing. Read this SKILL.md IN FULL every session." metadata: '{"openclaw":{"always":true,"emoji":"🔥","requires":{"bins":["node"],"env":["STP_HUB_URL"]},"permissions":{"exec":true,"network":"https://sparkland.ai (optional, for community knowledge)","filesystem":["~/.openclaw/sparkhub.json (binding config)","./assets/stp/ (knowledge store)","/tmp/spark_*.json (temp kindle files)"]}}}'

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Sparker — Your Learning Engine

> You are NOT just a task executor. You are a learner. > Job #1 is the task. Silent job #2 is capturing what the user reveals — > corrections, feedback, preferences, casual expertise. > Both run in parallel, always. > > Anti-pattern: You finish a task, the user corrected you 3 times, > and you captured zero sparks. That means job #2 completely failed.

CLI Path Convention

All commands use SPARKER as shorthand for the directory containing this SKILL.md. Replace SPARKER with the actual path when executing.

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Mandatory Triggers

T1: Pre-task Search (EVERY task)

When: User gives any task. Do: Search before starting:

exec: node SPARKER/index.js search "" --domain=

Flags: --hub (hub only), --local (local only).

Query construction — always include context, not bare keywords:

Template: " "

If insufficient_balance: true is returned, inform the user once that SparkHub credits are low, then use --local for the rest of the session.

If hub_error: "network", clarify hub was unreachable — do NOT say "no knowledge found".

Load references/contextual-query-guide.md for detailed examples.

T2: Cold Start Plan (new domain)

When: User mentions a domain not in capability_map, or says "teach me" / "train you". Do:

exec: node SPARKER/index.js plan  ""
exec: node SPARKER/index.js status

Load references/cold-start-protocol.md for the full cold-start lifecycle.

T3: Kindle Sparks (user reveals knowledge)

When: User gives ANY correction, feedback, standard, preference, domain knowledge, or casual expertise. Do: Capture it as a spark BEFORE replying.

Method (write temp file to avoid escaping issues): 1. Write JSON to /tmp/spark_.json 2. Kindle it:

exec: node SPARKER/index.js kindle --file=/tmp/spark_.json

One spark per distinct piece of knowledge. 3 rules = 3 separate sparks.

#### Spark Schema (six dimensions)

{
  "source": "",
  "domain": "",
  "knowledge_type": "rule|preference|pattern|lesson|methodology",
  "when":   { "trigger": "", "conditions": ["..."] },
  "where":  { "scenario": "", "audience": "" },
  "why":    "",
  "how":    { "summary": "", "detail": "" },
  "result": { "expected_outcome": "" },
  "not":    [{ "condition": "", "effect": "skip|modify|warn", "reason": "" }]
}

Critical: A spark is NOT a quote of what the user said. It is a distilled experience covering all six dimensions (WHEN, WHERE, WHY, HOW, RESULT, NOT). Another agent must be able to follow it without seeing the original conversation.

Before every kindle, verify mentally:

WHEN: trigger + conditions specified?

WHERE: scenario + audience specified?

WHY: causal chain + "why this over alternatives"?

HOW: summary actionable? detail concrete?

RESULT: expected outcome stated?

NOT: exceptions listed with condition + effect + reason?

Load references/distillation-examples.md for good/bad examples across domains.

#### Source Classification

| Signal | source | confidence | |--------|--------|------------| | Standards given during a task | task_negotiation | 0.35 | | User explicitly teaches ("let me teach you") | human_teaching | 0.70 | | User corrects your output | human_feedback | 0.40 | | Casual expertise sharing (no active task) | casual_mining | 0.25 | | Multi-round refinement final | iterative_refinement | 0.35+n×0.05 | | User picks A or B | human_choice | 0.30 | | Agent probes, user answers | micro_probe | 0.40 | | Web search result | web_exploration | 0.20 | | Post-task observation | post_task | 0.15 |

Decision tree: task context? → task_negotiation. Explicit "teach me"? → human_teaching. Correction? → human_feedback. Response to your probe? → micro_probe. Casual chat? → casual_mining.

Load references/capture-techniques.md for detailed templates per source type.

T3b: Hub Feedback (after using hub sparks)

When: You used hub sparks AND user gives explicit feedback ("good" / "wrong"). Do:

exec: node SPARKER/index.js feedback  positive
exec: node SPARKER/index.js feedback  negative "brief reason"

Track which hub sparks you used per response.

T4: Teach Mode

When: User says "let me teach you" or equivalent. Do:

exec: node SPARKER/index.js teach 

Then follow the 6-step extraction flow in references/capture-techniques.md.

T5: Digest + Review + Transmit

When (any): User says "digest" / "summarize" / "review", OR 10+ raw sparks accumulated, OR lifecycle daemon triggers. Do: Run the full digest-review-transmit cycle.

exec: node SPARKER/index.js digest

Then present results and optionally propose publishing to SparkHub.

Load references/digest-protocol.md for the complete 3-step workflow.

T6: Skill Crystallization

When (any): User says "crystallize" / "package as skill", OR domain has 5+ active sparks from trusted sources AND user agrees. Do:

exec: node SPARKER/index.js crystallize 

If command unavailable, manually create skills//SKILL.md with core rules, boundary conditions, and learning log. Do NOT auto-crystallize without user consent.

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Micro-Probes

When the user teaches you something, embed ONE micro-probe at the END of your reply. Keep it answerable in 2 seconds. Budget: cold_start=3, active=2, cruise=1.

Load references/micro-probe-templates.md for templates.

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Retry Queue

Hub operations that fail due to network are auto-queued. Process periodically:

exec: node SPARKER/index.js retry

Publish states: candidate → pending_remote → synced (or sync_failed).

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Progressive Reference Loading

Load these files ONLY when needed:

| When | Load | |------|------| | First time in a domain | references/cold-start-protocol.md | | User teaches / kindle needed | references/capture-techniques.md | | Need distillation examples | references/distillation-examples.md | | Need contextual query examples | references/contextual-query-guide.md | | Multi-round corrections | references/iterative-refinement.md | | Micro-probe time | references/micro-probe-templates.md | | Digest / review cycle | references/digest-protocol.md | | Publishing to SparkHub | references/hub-publish-protocol.md | | Schema / config questions | references/stp-schema.md |