skill-dedup-collision-reconciliation-with-content-security-scan
Reconcile duplicate/colliding workspace-hub skills without losing useful content, while avoiding pre-commit skill-content security scan regressions.
Best use case
skill-dedup-collision-reconciliation-with-content-security-scan is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Reconcile duplicate/colliding workspace-hub skills without losing useful content, while avoiding pre-commit skill-content security scan regressions.
Teams using skill-dedup-collision-reconciliation-with-content-security-scan should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/skill-dedup-collision-reconciliation-with-content-security-scan/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How skill-dedup-collision-reconciliation-with-content-security-scan Compares
| Feature / Agent | skill-dedup-collision-reconciliation-with-content-security-scan | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Reconcile duplicate/colliding workspace-hub skills without losing useful content, while avoiding pre-commit skill-content security scan regressions.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Skill dedup collision reconciliation with content security scan
Use when:
- a weekly skills audit or duplicate detector identifies duplicate skill names or leaf collisions
- implementation requires merging or deleting skill directories
- pre-commit skill-content scanning may block commits if merged content includes sensitive credential-handling examples
## Why this exists
In #2290, several duplicate pairs were not truly byte-identical. A naive "keep richer copy" strategy caused the canonical GitHub review skill to inherit credential-oriented examples from a duplicate, which then triggered the skill-content security scan and blocked commit.
The correct pattern was:
- inventory at directory level first
- preserve useful auxiliary files separately
- prefer the safer canonical body when the richer duplicate introduces security-hook violations
- merge only the safe, non-sensitive additions actually needed
## Procedure
1. Inventory every pair at directory level, not just `SKILL.md`
- compare `SKILL.md`
- list auxiliary files under references or scripts subfolders
- hash identical files when useful
2. Classify each pair before editing
- exact duplicate -> delete stale copy
- near-identical with metadata/category drift only -> copy canonical-safe text, preserve any safe auxiliary files
- divergent content -> diff carefully and decide whether the richer copy is actually safe to merge
3. Before overwriting canonical content, inspect for commit-hook-sensitive patterns
Common blockers:
- examples that explicitly show credential-bearing network requests
- examples that read secret-bearing local environment files
- token extraction snippets
- unpinned installer examples may warn but usually do not block
4. If richer duplicate contains blocked patterns
- do NOT wholesale replace canonical skill
- keep the safer canonical body
- manually port only safe additions, such as:
- better checklist text
- non-sensitive examples
- harmless references/templates
- move auxiliary reference files explicitly if they add value
5. Preserve auxiliary files with explicit moves
Examples from #2290:
- move the review output template into the canonical GitHub review skill references folder
- move the DSPy examples/modules/optimizers references into the canonical DSPy skill references folder
6. Add a regression test for the exact target findings
- assert target duplicate names are gone
- assert target leaf collisions are gone
- assert stale directories are removed
- assert canonical directories still exist
7. Validate in this order
- targeted regression test for the issue-specific findings
- broader weekly audit tests
- duplicate detector script
- weekly skills audit script
- deleted-path reference search in the repo surfaces relevant to the cleanup
8. Only then stage and commit
- if commit is blocked by skill-content scan, back out the unsafe merged text and keep the safer canonical wording
## Pitfalls
- "Richer" duplicate content may be operationally worse because it trips security hooks
- reference cleanup scope must include documentation surfaces, not just the immediate skill tree
- transient generated files can reappear and block push/rebase; restore them before final push
## Minimal heuristic
When reconciling duplicate skills:
- preserve files first
- prefer safe canonical text over richer unsafe text
- merge surgically, not wholesale
## Verification signals
Good final state looks like:
- target duplicate/collision findings cleared from weekly audit
- moved reference files present in canonical locations
- zero deleted-path references in checked surfaces
- commit passes skill-content security scan without bypassing hooksRelated Skills
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