ascorbic/macrodata/distill

Claude Code and agent skill by ascorbic/macrodata

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Source check pending

Checking that the SKILL.md file is still in the repository.

Extract distilled actions and facts from today's conversations. Spawns sub-agents per conversation to avoid context blowup.

Install

npx vskill@latest install ascorbic/macrodata/distill

SKILL.md

Distill Conversations

Process today's conversations to extract actionable knowledge. This is the core of memory consolidation.

Important: This runs as a coordinator. Spawn sub-agents for each conversation file to avoid loading full transcripts into your context.

Process

1. Find Today's Conversations

List conversation files modified today:

bash
find ~/.claude/projects -name "*.jsonl" -mtime -1 -type f 2>/dev/null
2. Process Each Conversation

For each conversation file, spawn a sub-agent with the Task tool:

Task(subagent_type="general-purpose", prompt=`
Read the conversation at {path}.
Filter to actual conversation content:
- Include: human messages, assistant text responses
- Exclude: tool calls, tool results, system messages, thinking blocks
Extract and return as JSON:
{
"distilled_actions": [
{
"summary": "Fixed auth bug in src/auth.ts where token refresh was racing",
"files": ["src/auth.ts"],
"outcome": "Added mutex lock around refresh"
}
],
"facts": [
{
"topic": "project-name",
"content": "Uses JWT tokens with 15min expiry"
},
{
"topic": "person-name",
"content": "Prefers explicit error handling over try/catch"
}
],
"decisions": [
"Chose Redis over in-memory cache for session storage because of multi-instance deployment"
]
}
Focus on:
- What was accomplished (not just discussed)
- Decisions made and their rationale
- New information about projects, people, or preferences
- File paths and specific technical details that should survive compression
Return ONLY the JSON, no explanation.
`)
3. Collect and Write Results

After all sub-agents complete:

Write distilled actions to journal:

For each action in all results:
log_journal(topic="distilled", content=action.summary + " Files: " + action.files.join(", "))

Write overall summary to journal:

log_journal(topic="distill-summary", content="Processed N conversations. Extracted X actions, Y facts.")

Update entity files with facts:

  • Group facts by topic
  • For each topic, read existing entity file (if any)
  • Integrate new facts, removing duplicates
  • Write updated file
4. Example Sub-Agent Output
json
{
"distilled_actions": [
{
"summary": "Added /distill skill to macrodata plugin",
"files": ["plugins/macrodata/skills/distill/SKILL.md"],
"outcome": "Skill extracts facts from conversations via sub-agents"
}
],
"facts": [
{
"topic": "macrodata",
"content": "Distillation separates narrative context from retained facts for better compression"
}
],
"decisions": [
"Coordinator updates state directly to prevent race conditions from parallel sub-agents"
]
}

Notes

  • Sub-agents should be spawned in parallel for efficiency
  • If a conversation file is very large (>500KB), the sub-agent may need to sample rather than read fully
  • Empty results are fine - not every conversation has extractable knowledge
  • Facts should be concise and specific, not narrative summaries

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
112
Repository forks
8
7-day trend score
24

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 19 Mar 2026, 7 months ago.

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