session-isolation
Prevents file collisions in multi-artifact workflows using isolated session directories.
Install
mkdir -p .claude/skills/session-isolation && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/9162" && unzip -o skill.zip -d .claude/skills/session-isolation && rm skill.zipInstalls to .claude/skills/session-isolation
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use when orchestrating workflows that generate multiple files (designs, reviews, reports) to prevent file collisions across concurrent or sequential sessions with unique session directories.Key capabilities
- →Generate unique session directories for artifact storage.
- →Prevent file overwrites in concurrent or sequential workflows.
- →Organize generated files by session and artifact type.
- →Pass session paths to sub-agents for consistent file saving.
- →Update session metadata upon workflow completion.
How it works
The orchestrator generates a unique session path and creates a corresponding directory structure. This path is then passed to sub-agents, which use it to save all generated artifacts, ensuring isolation.
Inputs & outputs
When to use session-isolation
- →Isolating files for concurrent AI runs
- →Preventing overwrite in multi-phase tasks
- →Organizing report artifacts by session
- →Managing temporary workflow data
About this skill
Session Isolation Pattern
Session-based artifact isolation for multi-artifact workflows. Use when orchestrating workflows that generate multiple files (designs, reviews, reports) to prevent file collisions across concurrent or sequential sessions.
Problem
When multiple workflows run (even sequentially), artifacts with the same name collide:
Session 1 (SEO): writes ai-docs/plan-review-grok.md
Session 2 (API): writes ai-docs/plan-review-grok.md <-- OVERWRITES!
Solution
Use unique session folders to isolate artifacts:
ai-docs/sessions/agentdev-seo-20260105-143022-a3f2/
├── session-meta.json # Session tracking
├── design.md # Primary artifact
├── reviews/
│ ├── plan-review/ # Plan review phase
│ │ ├── internal.md
│ │ ├── grok.md
│ │ └── consolidated.md
│ └── impl-review/ # Implementation review phase
│ ├── internal.md
│ └── consolidated.md
└── report.md # Final report
Implementation Pattern
1. Session Initialization (Orchestrator)
Add to Phase 0 of your orchestrator command:
# Generate unique session path
TARGET_SLUG=$(echo "${TARGET_NAME:-workflow}" | tr '[:upper:] ' '[:lower:]-' | sed 's/[^a-z0-9-]//g' | head -c20)
SESSION_BASE="${WORKFLOW_TYPE}-${TARGET_SLUG}-$(date +%Y%m%d-%H%M%S)-$(head -c4 /dev/urandom | xxd -p | head -c4)"
SESSION_PATH="ai-docs/sessions/${SESSION_BASE}"
# Create directory structure
mkdir -p "${SESSION_PATH}/reviews/plan-review" \
"${SESSION_PATH}/reviews/impl-review" || {
echo "Warning: Cannot create session directory, using legacy mode"
SESSION_PATH="ai-docs"
}
# Create session metadata (if not legacy mode)
if [[ "$SESSION_PATH" != "ai-docs" ]]; then
cat > "${SESSION_PATH}/session-meta.json" << EOF
{
"session_id": "${SESSION_BASE}",
"type": "${WORKFLOW_TYPE}",
"target": "${USER_REQUEST}",
"started_at": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
"status": "in_progress"
}
EOF
fi
2. Pass SESSION_PATH to Sub-Agents
Include in all agent prompts:
SESSION_PATH: ${SESSION_PATH}
{actual task description}
Save output to: ${SESSION_PATH}/{artifact_path}
3. Sub-Agent SESSION_PATH Detection
Add to agent <critical_constraints>:
<session_path_support>
**Check for Session Path Directive**
If prompt contains `SESSION_PATH: {path}`:
1. Extract the session path
2. Use it for all output file paths
3. Primary artifact: `${SESSION_PATH}/{type}.md`
4. Reviews: `${SESSION_PATH}/reviews/{phase}/{model}.md`
**If NO SESSION_PATH**: Use legacy paths (ai-docs/)
</session_path_support>
4. Session Completion
Update metadata when workflow completes:
if [[ -f "${SESSION_PATH}/session-meta.json" ]]; then
jq '.status = "completed" | .completed_at = (now | strftime("%Y-%m-%dT%H:%M:%SZ"))' \
"${SESSION_PATH}/session-meta.json" > "${SESSION_PATH}/session-meta.json.tmp" && \
mv "${SESSION_PATH}/session-meta.json.tmp" "${SESSION_PATH}/session-meta.json"
fi
Artifact Path Mapping
| Artifact Type | SESSION_PATH Format | Legacy Format |
|---|---|---|
| Design/Context | ${SESSION_PATH}/design.md | ai-docs/agent-design-{name}.md |
| Plan Review | ${SESSION_PATH}/reviews/plan-review/{model}.md | ai-docs/plan-review-{model}.md |
| Impl Review | ${SESSION_PATH}/reviews/impl-review/{model}.md | ai-docs/impl-review-{model}.md |
| Consolidated | ${SESSION_PATH}/reviews/{phase}/consolidated.md | ai-docs/{phase}-consolidated.md |
| Final Report | ${SESSION_PATH}/report.md | ai-docs/{workflow}-report-{name}.md |
Backward Compatibility
Legacy Mode Triggers:
SESSION_PATHnot provided in prompt- Directory creation fails (permissions)
- Explicit
LEGACY_MODE: truein prompt
Behavior:
- Fall back to flat
ai-docs/paths - Log warning about legacy mode
- All features still work, just without isolation
Session Metadata Schema
{
"session_id": "agentdev-seo-20260105-143022-a3f2",
"type": "agentdev",
"target": "SEO agent improvements",
"started_at": "2026-01-05T14:30:22Z",
"completed_at": "2026-01-05T15:45:30Z",
"status": "completed",
"phases_completed": ["init", "design", "plan-review", "implementation", "quality-review"],
"models_used": ["claude-embedded", "x-ai/grok-code-fast-1", "google/gemini-3-pro"],
"artifacts": {
"design": "design.md",
"plan_reviews": ["reviews/plan-review/internal.md", "reviews/plan-review/grok.md"],
"impl_reviews": ["reviews/impl-review/internal.md", "reviews/impl-review/gemini.md"],
"report": "report.md"
}
}
Plugins Using Session Isolation
| Plugin | Command | Session Pattern |
|---|---|---|
| agentdev | /develop | agentdev-{target}-{timestamp}-{random} |
| frontend | /review, /implement | review-{timestamp}-{random} |
| seo | /review, /alternatives | seo-review-{timestamp}-{random} |
| multimodel | /team | team-{task-slug}-{timestamp}-{random} |
Team Session Example
The /team command creates a session for multi-model blind voting:
ai-docs/sessions/team-stats-validation-20260209-143022-a3f2/
├── task.md # Raw task description (shared by all models)
├── grok-result.md # Grok's investigation findings
├── gemini-result.md # Gemini's investigation findings
├── deepseek-result.md # DeepSeek's investigation findings
├── internal-result.md # Internal Claude's findings
└── verdict.md # Aggregated verdict with vote breakdown
Key difference from other plugins: Team sessions contain results from multiple AI models investigating the same task independently. Each model writes to its own result file to prevent conflicts during parallel execution.
Best Practices
- Always initialize early: Session creation should happen in Phase 0
- Include SESSION_PATH in all prompts: Sub-agents need it for output paths
- Use descriptive slugs: Include workflow type and target in folder name
- Update metadata on completion: Track status changes
- Fallback gracefully: Never fail the workflow due to session creation issues
When not to use it
- →When `SESSION_PATH` is not provided in the prompt.
- →When directory creation fails due to permissions.
- →When `LEGACY_MODE: true` is explicitly set in the prompt.
Limitations
- →The skill falls back to legacy mode if the session directory cannot be created.
- →The skill falls back to legacy mode if `SESSION_PATH` is not provided in the prompt.
- →The skill falls back to legacy mode if `LEGACY_MODE: true` is explicitly set.
How it compares
This pattern automatically creates and manages unique directories for each workflow run, unlike manual approaches that risk file collisions from identically named outputs.
Compared to similar skills
session-isolation side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| session-isolation (this skill) | 0 | 6mo | Review | Intermediate |
| pptx | 393 | 6mo | Review | Advanced |
| nano-pdf | 63 | 2mo | Review | Beginner |
| video-downloader | 101 | 7mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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