Change impact analysis

Change Impact Analysis Skill

You are a senior release engineer performing a deterministic, graph-driven change impact analysis. You are the AI engine. The Python scripts in scripts/ handle static analysis; you provide narrative judgement, prioritisation, and actionable recommendations.

No separate LLM API key is required — the coding agent running this skill (you, whether that's Claude Code, GitHub Copilot, or Cursor) generates all AI sections.

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Step 1 — Identify Changed Files

Ask the user for the list of changed files if not already provided.

"How should I identify the changed files?"

1. Auto-detect from git (git diff main...HEAD) — (recommended)
2. I'll provide the list manually
3. Analyse a specific PR — provide the branch or PR number

If the user chooses option 1 or says nothing specific, use --from-git.

If they provide an explicit list, collect the paths.

If the project has no Git diff available

--from-git works in layers, and the engine reports which one fired via change_detection_method in the JSON output ("git", "mtime", or "explicit"):

1. git diff --name-only <base>...HEAD (PR-style diff against the base branch)
2. git diff --name-only HEAD + untracked files (uncommitted / staged / brand-new working-tree changes)
3. git diff --name-only HEAD~1 HEAD (most recent commit)
4. Filesystem fallback — if the folder is not a Git repository at all (no .git), Git is not installed, or none of steps 1–3 produced any files, the engine scans the repo for files modified within the last --since-minutes window (default 1440 = 24h) and uses those as the changed-file set.
5. If even the filesystem scan finds nothing, the script prints a warning and exits without writing any reports.

Tell the user clearly when the fallback was used — e.g.:

"This project doesn't have a usable Git diff (no .git folder / no commits ahead of main), so I used files modified in the last 24 hours instead. For a precise result, tell me the exact files you changed and I'll re-run with --changed-files."

If step 5 happens (no files at all), ask the user for an explicit file list and re-run with --changed-files.

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Step 2 — Ask for Output Location

"Where should I save the output files?"

1. ./change-impact-output/ inside the current directory — (recommended)
2. Directly in the current directory
3. A specific path — type it

Map to --output flag:

• Option 1 / no answer → omit --output
• Option 2 → --output .
• Option 3 → --output <user-supplied-path>

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Step 3 — Run the Analysis Engine

The analysis script lives at scripts/change_impact_skill.py, relative to this SKILL.md file's own folder. Resolve <skill_dir> from wherever you loaded this file from — it is whichever of these exists in the current repository:

• .claude/skills/change-impact-analysis/ (Claude Code)
• .github/skills/change-impact-analysis/ (GitHub Copilot / Cursor)
• the repository root itself (this skill's own source repo)

Check Python is available:

python --version

Run the engine (substitute the resolved <skill_dir> below):

# Option 1 — auto-detect from git
python <skill_dir>/scripts/change_impact_skill.py \
  --from-git --base-branch main

# Option 2 — explicit file list
python <skill_dir>/scripts/change_impact_skill.py \
  --changed-files src/api/users.py src/models/user.py src/services/auth.py

# Option 3 — custom output directory
python <skill_dir>/scripts/change_impact_skill.py \
  --from-git --base-branch main --output ./reports/

# Get raw JSON to stdout (for piping / CI integration)
python <skill_dir>/scripts/change_impact_skill.py \
  --from-git --json-only

If the script is not found at any of the candidate paths, run file_search/Glob for change_impact_skill.py in the repo before falling back to Manual Fallback — do not guess a path.

Wait for the script to complete. Output files produced:

File	Description
impact_report.md	Primary artifact — full structured impact report
impact_analysis.json	Machine-readable result (CI/CD integration)
deployment_checklist.md	Ready-to-use pre/post deployment checklist

If Python or the script is missing, proceed to Manual Fallback.

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Step 4 — Read the Analysis Data

Read the generated files:

{output_dir}/impact_report.md
{output_dir}/impact_analysis.json

Key fields in impact_analysis.json to study:

{
  "impact": {
    "changed_files":           [...],   // normalised paths of changed files
    "impacted_modules":        [...],   // every affected module with type + proximity
    "impacted_apis":           [...],   // API endpoint files in the blast radius
    "regression_areas":        [...],   // high-risk areas to regression-test
    "required_test_suites":    [...],   // unit / integration / e2e suites needed
    "consumer_apps":           [...],   // downstream applications affected
    "direct_impact_count":     N,
    "transitive_impact_count": N
  },
  "contract_violations": [...],         // OpenAPI / GraphQL / Protobuf violations
  "risk": {
    "score":   N,                       // 0–100
    "level":   "LOW|MEDIUM|HIGH|CRITICAL",
    "action":  "...",
    "factors": [...]                    // per-factor breakdown
  }
}

Also read:

• references/dependency-analysis.md — graph algorithm reference
• references/risk-scoring.md — risk factor reference

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Step 5 — Provide AI Analysis (You Are the AI Engine)

Think like a senior release engineer who reviewed the complete blast radius. Produce four AI-quality sections:

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5a — Impact Summary

Write a crisp executive summary (3–5 sentences):

• What changed and why it matters for this release
• Highest-risk modules and why
• Whether this deployment should proceed as-is, needs monitoring, or should be blocked
• One-line recommendation for the release manager

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5b — Blast Radius Explanation

For each directly changed file:

1. What does this module do?
2. Which other modules depend on it (transitive chain)?
3. Is there a test that covers this path?
4. What is the worst-case failure mode if this change is faulty?

For the top 3 highest-risk transitively affected modules, explain:

• Why they are affected (import chain)
• What would break if the change is incorrect
• Who owns it (from CODEOWNERS / ownership map)

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5c — API Contract Assessment

For each contract violation detected:

1. What changed — endpoint / field / type affected
2. Who is impacted — which consumers use this endpoint
3. Migration path — how consumers should adapt
4. Recommended action — deprecate gracefully / block deployment / coordinate release

If no violations: confirm that the API surface is stable and consumers are safe.

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5d — Release Recommendation

Provide a concrete, structured recommendation:

Verdict: PROCEED / PROCEED WITH MONITORING / BLOCK — REQUIRES REVIEW

Rationale: (2–3 sentences linking the risk score to the specific changes)

Before deploying:

• (numbered list of required actions)

After deploying:

• (numbered list of verification steps)

Rollback trigger: describe the exact condition that should trigger a rollback

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Step 6 — Output AI Analysis in Chat

Present the complete analysis in structured markdown:

---
## Impact Summary
[Section 5a]

---
## Blast Radius Explanation
[Section 5b]

---
## API Contract Assessment
[Section 5c]

---
## Release Recommendation
[Section 5d]

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Step 7 — Write AI Content Into Report File

Do NOT ask — automatically update the report immediately after the analysis.

Locate the report:

• Default: ./change-impact-output/impact_report.md
• Custom path: {output_dir}/impact_report.md

Append a new section ## AI Release Analysis at the end of the report containing all four AI sections. Use insert_edit_into_file to append, or create_file to overwrite if simpler.

Print confirmation:

[ok] AI analysis appended to: {report_path}

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Step 8 — Report Completion

Change Impact Analysis complete [ok]

Repository   : {repo_path}
Base branch  : {base_branch}
Changed files: {N}

Risk Score   : {score}/100 — {level}
Action       : {action}

Impact
  Direct modules     : {direct_count}
  Transitive modules : {transitive_count}
  Impacted APIs      : {api_count}
  Consumer apps      : {consumer_count}

Output files in {output_dir}:
  impact_report.md          ← open this first
  impact_analysis.json
  deployment_checklist.md

AI engine: {agent_name} (no separate API key required)

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Manual Fallback (Script Not Found)

If change_impact_skill.py is not found or Python is unavailable:

1. List all source files in the repository:

   git diff --name-only main...HEAD
   

   Or ask the user to provide the changed files.

2. For each changed file, use grep_search to find all files that import it:

   Pattern: import.*{module_name}|require.*{module_name}|from.*{module_name}
   

3. Repeat for each newly found file (manual BFS, up to 3 hops).

4. Use file_search to find CODEOWNERS and map files to owners.

5. Use file_search to find openapi.yaml, swagger.json, *.graphql for contract checks.

6. Manually calculate risk score using the table in references/risk-scoring.md.

7. Use templates/impact_report.md to produce the report.

8. Write the report to ./impact_report.md.

9. Continue with Steps 5–8.

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Notes

• No API key required — the host coding agent (Claude Code / Copilot / Cursor) is the AI engine
• Deterministic — given the same inputs the score is always the same
• Polyglot — supports Python, JS/TS, Java, C#, Go import graphs
• CODEOWNERS — automatically maps every affected file to its owner
• Contract detection — OpenAPI, Swagger, GraphQL schema, Protobuf
• CI/CD ready — --json-only flag emits structured JSON to stdout
• No-Git fallback — --from-git degrades to a filesystem mtime scan (--since-minutes, default 1440) when there's no .git directory, Git isn't installed, or git diff returns nothing; check change_detection_method in the JSON output (git / mtime / explicit)
• Script path — resolve relative to this file's own folder (see Step 3); never hardcode a single platform's install path