recall-reasoning

Search through previous sessions to find relevant decisions, approaches that worked, and approaches that failed. Queries two sources:

parcadei/continuous-claude-v3Updated Apr 8, 2026

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Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill recall-reasoning

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Installation Guide

How to use recall-reasoning on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add recall-reasoning
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill recall-reasoning

Fetches recall-reasoning from parcadei/continuous-claude-v3 and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/recall-reasoning

Restart Cursor to activate recall-reasoning. Access via /recall-reasoning in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Recall Past Work

Search through previous sessions to find relevant decisions, approaches that worked, and approaches that failed. Queries two sources:

  1. Artifact Index - Handoffs, plans, ledgers with post-mortems (what worked/failed)
  2. 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/core/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_PROJECT_DIR/.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/core/artifact_query.py "authentication OAuth JWT"

# Find only successful approaches
uv run python scripts/core/artifact_query.py "implement agent" --outcome SUCCEEDED

# Find what failed (to avoid repeating mistakes)
uv run python scripts/core/artifact_query.py "hook implementation" --outcome FAILED

# Search build/test reasoning
bash "$CLAUDE_PROJECT_DIR/.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 work
  • build_pass = what finally succeeded
  • Multiple failures before success = non-trivial problem

Process

  1. Run Artifact Index query first - richer context, post-mortems
  2. Review relevant handoffs - check what worked/failed sections
  3. If needed, search reasoning - for specific build errors
  4. Apply learnings - follow successful patterns, avoid failed ones

No Results?

Artifact Index empty:

  • Run uv run python scripts/core/artifact_index.py --all to index existing handoffs
  • Create handoffs with post-mortem sections for future recall

Reasoning files empty:

  • Use /commit after builds to capture reasoning
  • Check if .git/claude/ directory exists

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use when

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid when

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.526 reviews
  • A
    Arjun WangDec 28, 2024

    Registry listing for recall-reasoning matched our evaluation — installs cleanly and behaves as described in the markdown.

  • G
    Ganesh MohaneDec 12, 2024

    recall-reasoning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • S
    Sophia KimNov 19, 2024

    recall-reasoning reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • R
    Rahul SantraNov 3, 2024

    Useful defaults in recall-reasoning — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • P
    Pratham WareOct 22, 2024

    Registry listing for recall-reasoning matched our evaluation — installs cleanly and behaves as described in the markdown.

  • A
    Arjun PatelOct 10, 2024

    recall-reasoning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • S
    Sophia MensahSep 17, 2024

    Keeps context tight: recall-reasoning is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • P
    Piyush GSep 1, 2024

    recall-reasoning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • A
    Amelia ZhangSep 1, 2024

    Registry listing for recall-reasoning matched our evaluation — installs cleanly and behaves as described in the markdown.

  • S
    Shikha MishraAug 20, 2024

    recall-reasoning has been reliable in day-to-day use. Documentation quality is above average for community skills.

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