Quick Ref: Trace design decisions through CASS sessions, handoffs, git, and artifacts. Output: .agents/research/YYYY-MM-DD-trace-*.md
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontraceExecute the skills CLI command in your project's root directory to begin installation:
Fetches trace from boshu2/agentops 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 trace. Access via /trace 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.
Submit your Claude Code skill and start earning
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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Quick Ref: Trace design decisions through CASS sessions, handoffs, git, and artifacts. Output:
.agents/research/YYYY-MM-DD-trace-*.md
YOU MUST EXECUTE THIS WORKFLOW. Do not just describe it.
For knowledge artifact lineage (learnings, patterns, tiers), use /provenance instead.
CLI dependencies: cass (session search). If cass is unavailable, skip transcript search and rely on git log, handoff docs, and .agents/ artifacts for decision tracing.
Given /trace <concept>:
Determine what kind of provenance to trace:
IF target is a file path (contains "/" or "."):
→ Use /provenance (artifact lineage)
IF target is a git ref (sha, branch, tag):
→ Use git-based tracing (Step 2b)
ELSE (keyword/concept):
→ Use design decision tracing (Step 2a)
Launch 4 parallel search agents (CASS, Handoff, Git, Research) and wait for all to complete.
Backend: Agents use Task(subagent_type="Explore") which maps to task(subagent_type="explore") in OpenCode. See skills/shared/SKILL.md ("Runtime-Native Spawn Backend Selection") for the shared contract.
Read references/discovery-patterns.md for agent definitions and prompts.
Read references/discovery-patterns.md for git-based tracing commands.
Merge results from all sources into a single chronological timeline (oldest first). Deduplicate same-day/same-session events. Every claim needs a source citation.
For each event in timeline, identify:
Write to: .agents/research/YYYY-MM-DD-trace-<concept-slug>.md
Read references/report-template.md for the full report format and deduplication rules.
Tell the user:
Read references/edge-cases.md for handling: no CASS results, no handoffs, ambiguous concepts (>20 results), and all-sources-empty scenarios. General principle: continue with remaining sources and note gaps in the report.
.agents/research/ artifact| Skill | Purpose | Input | Output |
|---|---|---|---|
/provenance |
Artifact lineage | File path | Tier/promotion history |
/trace |
Design decisions | Concept/keyword | Timeline of evolution |
Use /provenance for: "Where did this learning come from?"
Use /trace for: "How did we decide on this architecture?"
# Trace a design decision
/trace "three-level architecture"
# Trace a role/concept
/trace "Chiron"
# Trace a pattern
/trace "brownian ratchet"
# Trace a feature
/trace "parallel wave execution"
User says: /trace "agent team protocol"
What happens:
.agents/research/2026-02-13-trace-agent-team-protocol.md with full timeline and citationsResult: Complete evolution timeline showing how agent team protocol developed across 7 sessions with source citations.
User says: /trace abc1234
What happens:
git log --grep to find related work.agents/ for contemporary research/plansResult: Trace report links commit to broader design context from surrounding artifacts.
| Problem | Cause | Solution |
|---|---|---|
| CASS returns no results | Session search not installed or query too specific | Check which cass. If missing, skip CASS and rely on handoffs/git/research. Try broader query terms. |
| Timeline has gaps | Not all decisions documented in searchable artifacts | Note gaps in report. Suggest interviewing team members or checking Slack/email archives for missing context. |
| Too many results (>50 matches) | Very broad concept or high-frequency term | Read references/edge-cases.md for ambiguous concept handling. Narrow query or filter by date range. Ask user for more specific aspect to trace. |
| Empty trace report (all sources failed) | Concept genuinely undocumented or typo | Verify spelling. Try synonyms. Report to user: "No documented history found. This may be a new concept or may need different search terms." |
Make 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
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
trace fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
trace has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for trace matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: trace is focused, and the summary matches what you get after install.
trace has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for trace matched our evaluation — installs cleanly and behaves as described in the markdown.
trace reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: trace is focused, and the summary matches what you get after install.
trace fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend trace for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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