ai-analyst-lab/.claude/skills/archive-analysis

Skill: Archive Analysis

Purpose

Save a completed analysis to the knowledge system's analysis archive for future recall. Captures key findings, metrics used, agents invoked, and output file paths so that past work can be referenced in future sessions.

When to Use

• After completing an L3+ analysis (post-validation)
• After /run-pipeline completes successfully
• User says "save this analysis" or "archive this"
• Automatically triggered at the end of Step 18 (Close the Loop)

Instructions

Step 1: Gather Analysis Metadata

Collect from the current session:

1. Title: Derive from the original question or business context
2. Question: The original user question
3. Question level: From the Question Router classification (L1-L5)
4. Dataset ID: From .knowledge/active.yaml
5. Key findings: Extract top 3-5 findings from the analysis output or validation report
6. Metrics used: List metric IDs referenced during analysis (match against metric dictionary if available)
7. Agents used: List agent names that were invoked
8. Output files: List paths to files in outputs/ and working/
9. Tags: Auto-generate from question keywords + metric names
10. Confidence: From the validation agent's confidence score, if available

Step 2: Create Archive Entry

Generate a unique ID: analysis_{YYYYMMDD}_{HHMMSS}

Build the entry dict following .knowledge/analyses/_schema.yaml.

Step 3: Append to Index

1. Read .knowledge/analyses/index.yaml
2. Append the new entry to the analyses list
3. Increment total_analyses
4. Update last_updated to current date
5. Write back to index.yaml

Step 4: Update Dataset Stats

1. Read .knowledge/datasets/{active}/manifest.yaml
2. Increment analysis_count
3. Update last_used to current date
4. Write back

Step 5: Confirm

Report to user:

Analysis archived: {title}
ID: {id}
Findings: {count} key findings captured
Use `/history` to browse past analyses.

Step 6: Capture to Query Archaeology (Optional)

After archiving, check if the analysis produced reusable patterns worth saving to .knowledge/query-archaeology/curated/ via helpers/archaeology_helpers.py.

1. SQL patterns — If validated SQL queries could be reused for future analyses:

   • Offer to capture via capture_cookbook_entry(title, sql, dataset, tables, tags)
   • Only capture queries that passed tie-out or validation checks

2. Table knowledge — If the analysis revealed useful table metadata:

   • Offer to capture/update via capture_table_cheatsheet(table_name, dataset, grain, primary_key, common_filters, gotchas, common_joins)
   • Include grain, primary key, common filters, gotchas, and common joins

3. Join patterns — If the analysis used non-obvious joins:

   • Offer to capture via capture_join_pattern(tables, join_sql, cardinality, validated, dataset)
   • Record cardinality and whether the join was validated

Rules for this step:

• Ask the user: "Would you like to save any SQL patterns from this analysis?"
• If the user declines or there are no reusable patterns, skip silently
• Only capture patterns from analyses with confidence grade B or better
• Never auto-capture without user confirmation

Rules

1. Never overwrite an existing archive entry — always append
2. Key findings should be one sentence each, factual, with numbers
3. Tags should be lowercase, no spaces (use hyphens)
4. If validation was not run, set confidence to null and note it
5. Archive even partial analyses — mark as partial: true

Edge Cases

• No outputs exist: Archive with metadata only, note "no output files"
• Pipeline was interrupted: Archive what's available, mark as partial
• Duplicate question: Still archive — different runs may find different things
• Analysis index doesn't exist: Create it from template