When asked to find all files a specific author contributed to on a branch (compared to main or another upstream), follow this procedure. The goal is to produce a simple table that both humans and LLMs can consume.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionauthor-contributionsExecute the skills CLI command in your project's root directory to begin installation:
Fetches author-contributions from microsoft/vscode and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate author-contributions. Access via /author-contributions in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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When asked to find all files a specific author contributed to on a branch (compared to main or another upstream), follow this procedure. The goal is to produce a simple table that both humans and LLMs can consume.
This skill involves many sequential git commands. Delegate it to a subagent with a prompt like:
Find every file that author "Full Name" contributed to on branch
<branch>compared to<upstream>. Trace contributions through file renames. Return a markdown table with columns: Status (DIRECT or VIA_RENAME), File Path, and Lines (+/-). Include a summary line at the end.
git log --format="%an <%ae>" <upstream>..<branch> | sort -u
Match the requested person to their exact --author= string. Do not guess — short usernames won't match full display names (resolve via git log or the GitHub MCP get_me tool).
git log --author="<Exact Name>" --format="%H" <upstream>..<branch>
For each commit hash, extract touched files:
git diff-tree --no-commit-id --name-only -r <hash>
Union all results into a set (author_files).
For every commit on the branch (not just the author's), extract renames:
git diff-tree --no-commit-id -r -M <hash>
Parse lines with R status to build a map: new_path → {old_paths}.
git diff --name-only <upstream>..<branch>
These are the files that will actually land when the branch merges.
For each file in step 4:
author_files → DIRECTauthor_files → VIA_RENAMEgit diff --stat <upstream>..<branch> -- <file1> <file2> ...
Format the result as a markdown table:
| Status | File | +/- |
|--------|------|-----|
| DIRECT | src/vs/foo/bar.ts | +120/-5 |
| VIA_RENAME | src/vs/baz/qux.ts | +300 |
| ... | ... | ... |
**Total: N files, +X/-Y lines**
.py script, run it, then delete it.--author does substring matching but you must verify the right person is matched (e.g., don't match "Joshua Smith" when looking for "Josh S."). Use the GitHub MCP get_me tool or git log output to resolve the correct full name.contrib/chat/ → agentSessions/ → sessions/. The rename map must be walked transitively.import subprocess, os
os.chdir('<repo_root>')
UPSTREAM = 'main'
AUTHOR = '<Author Name>' # Resolve via `git log` or GitHub MCP `get_me`
# Step 2: author's files
commits = subprocess.check_output(
['git', 'log', f'--author={AUTHOR}', '--format=%H', f'{UPSTREAM}..HEAD'],
text=True).strip().split('\n')
author_files = set()
for h in (c for c in commits if c):
files = subprocess.check_output(
['git', 'diff-tree', '--no-commit-id', '--name-only', '-r', h],
text=True).strip().split('\n')
author_files.update(f for f in files if f)
# Step 3: rename map from ALL commits
all_commits = subprocess.check_output(
['git', 'log', '--format=%H', f'{UPSTREAM}..HEAD'],
text=True).strip().split('\n')
rename_map = {} # new_name -> set(old_names)
for h in (c for c in all_commits if c):
out = subprocess.check_output(
['git', 'diff-tree', '--no-commit-id', '-r', '-M', h],
text=True, timeout=5).strip()
for line in out.split('\n'):
if not line:
continue
parts = line.split('\t')
if len(parts) >= 3 and 'R' in parts[0]:
rename_map.setdefault(parts[2], set()).add(parts[1])
# Step 4: merge diff
diff_files = subprocess.check_output(
['git', 'diff', '--name-only', f'{UPSTREAM}..HEAD'],
text=True).strip().split('\n')
# Step 5: classify
results = []
for f in (x for x in diff_files if x):
if f in author_files:
results.append(('DIRECT', f))
else:
# walk rename chain
chain, to_check = set(), [f]
while to_check:
cur = to_check.pop()
if cur in chain:
continue
chain.add(cur)
to_check.extend(rename_map.get(cur, []))
chain.discard(f)
if chain & author_files:
results.append(('VIA_RENAME', f))
# Step 6: stats
if results:
stat = subprocess.check_output(
['git', 'diff', '--stat', f'{UPSTREAM}..HEAD', '--'] +
[f for _, f in results], text=True)
print(stat)
# Step 7: table
for kind, f in sorted(results, key=lambda x: x[1Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Registry listing for author-contributions matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: author-contributions is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: author-contributions is the kind of skill you can hand to a new teammate without a long onboarding doc.
author-contributions has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend author-contributions for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
author-contributions reduced setup friction for our internal harness; good balance of opinion and flexibility.
author-contributions reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: author-contributions is focused, and the summary matches what you get after install.
We added author-contributions from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in author-contributions — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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