recall-reasoning
Search past reasoning for relevant decisions and approaches
When & Why to Use This Skill
The recall-reasoning Claude skill is a powerful context-retrieval tool designed to bridge the gap between separate AI sessions. By indexing and searching through past reasoning files, build attempts, and project handoffs, it allows the agent to maintain long-term project continuity. It specifically highlights successful patterns to follow and failed approaches to avoid, making it an essential utility for complex, multi-stage software engineering and decision-making tasks.
Use Cases
- Technical Debt Reduction: Searching past reasoning to understand why a specific, non-obvious implementation choice was made to avoid breaking legacy logic.
- Efficient Debugging: Querying previous build failures and test logs to identify if a current error matches a pattern encountered and resolved in a prior session.
- Project Onboarding & Handoffs: Using the Artifact Index to quickly summarize the 'what worked' and 'what failed' history for a new contributor or a new phase of development.
- Preventing Regression: Reviewing post-mortems from previous tasks to ensure that current plans do not repeat documented mistakes or dead-end approaches.
| name | recall-reasoning |
|---|---|
| description | Search past reasoning for relevant decisions and approaches |
Recall Past Work
Search through previous sessions to find relevant decisions, approaches that worked, and approaches that failed. Queries two sources:
- Artifact Index - Handoffs, plans, ledgers with post-mortems (what worked/failed)
- Reasoning Files - Build attempts, test failures, commit context
When to Use
- Starting work similar to past sessions
- "What did we do last time with X?"
- Looking for patterns that worked before
- Investigating why something was done a certain way
- Debugging an issue encountered previously
Usage
Primary: Artifact Index (rich context)
uv run python scripts/artifact_query.py "<query>" [--outcome SUCCEEDED|FAILED] [--limit N]
This searches handoffs with post-mortems (what worked, what failed, key decisions).
Secondary: Reasoning Files (build attempts)
bash .claude/scripts/search-reasoning.sh "<query>"
This searches .git/claude/commits/*/reasoning.md for build failures and fixes.
Examples
# Search for authentication-related work
uv run python scripts/artifact_query.py "authentication OAuth JWT"
# Find only successful approaches
uv run python scripts/artifact_query.py "implement agent" --outcome SUCCEEDED
# Find what failed (to avoid repeating mistakes)
uv run python scripts/artifact_query.py "hook implementation" --outcome FAILED
# Search build/test reasoning
bash .claude/scripts/search-reasoning.sh "TypeError"
What Gets Searched
Artifact Index (handoffs, plans, ledgers):
- Task summaries and status
- What worked - Successful approaches
- What failed - Dead ends and why
- Key decisions - Choices with rationale
- Goal and constraints from ledgers
Reasoning Files (.git/claude/):
- Failed build attempts and error output
- Successful builds after failures
- Commit context and branch info
Interpreting Results
From Artifact Index:
✓= SUCCEEDED outcome (pattern to follow)✗= FAILED outcome (pattern to avoid)?= UNKNOWN outcome (not yet marked)- Post-mortem sections show distilled learnings
From Reasoning:
build_fail= approach that didn't workbuild_pass= what finally succeeded- Multiple failures before success = non-trivial problem
Process
- Run Artifact Index query first - richer context, post-mortems
- Review relevant handoffs - check what worked/failed sections
- If needed, search reasoning - for specific build errors
- Apply learnings - follow successful patterns, avoid failed ones
No Results?
Artifact Index empty:
- Run
uv run python scripts/artifact_index.py --allto index existing handoffs - Create handoffs with post-mortem sections for future recall
Reasoning files empty:
- Use
/commitafter builds to capture reasoning - Check if
.git/claude/directory exists