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integrate-findings

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Update the specification document with research findings, categorizing by confidence level and managing the Open Questions section.

8 stars
1.2k downloads
Updated 1/20/2026

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SKILL.md

Integrate Findings - Specification Updater

You are updating the specification document with research findings from the codebase investigation.

Input: $ARGUMENTS contains:

  • Path to spec document
  • Research findings report with confidence-rated answers

Step 1: Read Current State

Read the specification document and parse:

  • Current YAML frontmatter (phase, iteration, convergence metrics)
  • Existing High Confidence items
  • Existing Medium Confidence items
  • Current Open Questions
  • Iteration Log

Step 2: Process Research Findings

For each finding in the research report:

HIGH Confidence Findings

  • Add to "High Confidence" section
  • Include evidence reference (file:line)
  • Remove corresponding item from Open Questions
  • Format: - {statement} (verified: {file}:{line})

MEDIUM Confidence Findings

  • Add to "Medium Confidence" section
  • Note the uncertainty
  • Keep related question in Open Questions with added context
  • Format: - {statement} (likely, based on {evidence})

LOW Confidence / No Answer

  • Keep in Open Questions
  • Add context from research: what was searched, why inconclusive
  • Format: - [ ] {question} — Searched: {what}, Result: {why inconclusive}

New Questions Discovered

  • Add to Open Questions section
  • Tag as newly discovered: [NEW]
  • Format: - [ ] [NEW] {question}

Step 3: Update Convergence Metrics

Calculate new metrics:

convergence:
  questions_stable_count: {increment if same count as last iteration, else 0}
  open_questions_count: {current count of open questions}
  high_confidence_ratio: {high_items / (high_items + medium_items + open_questions)}

Step 4: Update Frontmatter

---
feature: {unchanged}
phase: {unchanged}
iteration: {increment by 1}
last_updated: {current ISO timestamp}
convergence:
  questions_stable_count: {calculated}
  open_questions_count: {calculated}
  high_confidence_ratio: {calculated}
---

Step 5: Append to Iteration Log

Add entry to Iteration Log section:

### Iteration {n} ({date})
- **Researched**: {list of questions investigated}
- **Resolved**: {count} questions answered
  - {brief list of what was resolved}
- **Added**: {count} new items
  - High Confidence: {count}
  - Medium Confidence: {count}
- **New Questions**: {count} discovered during research
  - {brief list}
- **Still Open**: {count} questions remain
- **Convergence**: {ratio}% high confidence, {stable_count} iterations stable

Step 6: Detect Convergence

Check if ANY convergence criteria met:

  1. Stability: questions_stable_count >= 2

    • Same number of open questions for 2 iterations
    • Indicates automated research has exhausted its ability
  2. Low Questions: open_questions_count <= 3

    • Few enough questions that human review is efficient
  3. High Confidence: high_confidence_ratio > 0.80

    • Spec is 80%+ verified against codebase

Step 7: Output Summary

INTEGRATION COMPLETE
Spec: {path}
Iteration: {n} → {n+1}

## Changes Made
- Added to High Confidence: {n} items
- Added to Medium Confidence: {n} items
- Resolved from Open Questions: {n} items
- New questions added: {n} items

## Convergence Status
- Open Questions: {previous} → {current}
- Stability Count: {n} iterations
- High Confidence Ratio: {ratio}%
- Criteria Met: {YES: criteria | NO}

## Recommendation
{CONTINUE: more iterations needed | CONVERGED: ready for human review}

Edit Guidelines

When editing the spec document:

  • Preserve existing formatting
  • Don't reorder items unnecessarily
  • Add new items at the end of each section
  • Use consistent bullet/checkbox formatting
  • Keep evidence references inline and concise

Error Handling

  • If spec format is unexpected, report and don't modify
  • If findings reference questions not in spec, add them
  • If metrics can't be calculated, use conservative estimates

Install

Download ZIP
Requires askill CLI v1.0+

AI Quality Score

78/100Analyzed 2/24/2026

Well-structured skill for spec document updating with confidence-level categorization and convergence tracking. Clear steps, templates, and error handling. Highly actionable for its domain but narrowly scoped to spec refinement workflow. Deep path nesting indicates internal project use.

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Metadata

Licenseunknown
Version-
Updated1/20/2026
Publisherdhofheinz

Tags

observability