context-mine

name	context-mine
description	Extracts structured context from conversation exports (Slack, meeting transcripts, chat logs) into _inbox for processing.
category	context-management

Context Mine

See also: context-ingest is the single public entry point for new material. If the user dropped a conversation export of unknown shape, prefer context-ingest — it dispatches here automatically when it detects a Slack/transcript/chat-log shape. Use context-mine directly only when the user explicitly names mining ("mine this transcript", "extract decisions from this Slack export"). Mine extracts → ingest classifies and routes.

Purpose

This skill IS:

• An extraction tool that reads conversation exports and pulls out actionable context
• A bridge between unstructured conversations and the CLEAR context architecture
• A preprocessor that dumps structured summaries to docs/_inbox/ for review

This skill IS NOT:

• A replacement for context-ingest (mine extracts, ingest classifies and routes)
• An automatic integration tool (everything goes to _inbox for human review)
• A real-time conversation monitor (works on exports/transcripts after the fact)

---

When to Use

• "Extract context from this Slack export"
• "Mine this meeting transcript for decisions"
• "Process this chat log into actionable items"
• "I have a conversation dump, what did we decide?"

---

Supported Input Formats

Format	Source	How to Provide
Plain text	Any conversation copy-paste	Paste directly into chat
Slack JSON export	Slack workspace export	Provide file path
Meeting transcript	Otter.ai, Fireflies, manual notes	Paste or provide file path
Chat history	Any Q&A format	Paste or provide file path

---

Extraction Categories

For each conversation, extract into these categories:

Decisions Made

Things that were decided, with enough context to understand why.

- Decision: [what was decided]
  Rationale: [why, if stated]
  Affects: [what data points this might update]

Discoveries

New information learned that wasn't known before.

- Discovery: [what was learned]
  Source: [who said it / where it came from]
  Relevance: [which data point or cluster this relates to]

Action Items

Tasks, follow-ups, commitments.

- [ ] [action item]
  Owner: [who, if mentioned]
  Deadline: [when, if mentioned]

Preferences & Positions

Opinions, stances, preferences expressed by key people.

- Preference: [what was expressed]
  Who: [who said it]
  Context: [in response to what]

Problems & Risks

Issues raised, concerns flagged, blockers identified.

- Problem: [what was flagged]
  Severity: [high/medium/low based on language used]
  Status: [resolved in conversation or still open]

---

Workflow

Step 1: Receive Input

Accept the conversation in any supported format. If pasted directly, work with it as-is. If a file path, read the file.

Step 2: Scan and Extract

Read through the conversation and extract items into the 5 categories above. Guidelines:

• Be selective — only extract items with clear business relevance
• Preserve attribution — note who said what when possible
• Link to data points — if an extracted item relates to a known data point, note which one
• Skip small talk — ignore greetings, scheduling logistics, off-topic chatter

Step 3: Write to Inbox

Create one or more files in docs/_inbox/ with frontmatter:

---
type: session-capture
source: "[slack-export | meeting-transcript | chat-log | paste]"
date: "YYYY-MM-DD"
status: raw
mined-from: "[filename or 'direct paste']"
---

Step 4: Summarize

Report to the user:

• How many items extracted per category
• Which existing data points are likely affected
• Recommend: "Run context-ingest to classify and route these items"

---

What AI Does vs What's Mechanical

Task	Who
Read and understand conversation content	AI
Identify decisions, discoveries, action items	AI
Link extracted items to existing data points	AI
Create the output file with frontmatter	AI (template is simple enough)
Deduplication check against existing _inbox files	Script (compare filenames)
Routing into data points	context-ingest skill (separate step)

---

Example Output

---
type: session-capture
source: slack-export
date: 2026-04-07
status: raw
mined-from: "product-channel-export-april.json"
---

## Decisions
- Switched to usage-based pricing for enterprise tier
  Rationale: Customer interviews showed resistance to per-seat model
  Affects: pricing-strategy.md, value-proposition.md

## Discoveries
- Competitor X launched a free tier targeting our mid-market segment
  Source: Sales team discussion in #product channel
  Relevance: competitive-positioning.md

## Action Items
- [ ] Update pricing page with new enterprise model
- [ ] Schedule competitive response meeting for Friday

## Problems
- Enterprise onboarding flow takes 3 weeks — customers complaining
  Severity: high
  Status: open — no resolution in conversation

---

Relationship to Other Skills

Skill	Relationship
context-ingest	Mine outputs TO inbox, ingest processes FROM inbox
context-audit	Mine can surface items that reveal stale data points
ecosystem-manager	Mine may discover patterns worth capturing as lessons