From sdlc-skills
Disk-first, checkpointed research workflow with three modes: trend research, topic analysis, and fact-checking. Useful for investigating topics, verifying claims, or exploring emerging patterns.
How this skill is triggered — by the user, by Claude, or both
Slash command
/sdlc-skills:deep-researchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
One workflow, three modes. Pick the mode from the user's intent:
One workflow, three modes. Pick the mode from the user's intent:
| Mode | Use when |
|---|---|
| trends | "what's happening in X", "emerging patterns", "who are the players" |
| analyze | "go deep on X", "tradeoffs of X", "SWOT", "compare perspectives" |
| factcheck | document or list of claims handed in for verification |
All three share the same workspace + checkpoint discipline below. Read this section once, then jump to your mode.
Workspace: .research/<YYYY-MM-DD>/<mode>_<session>/
00_plan.md # written before any research
notes.md # rolling findings, source URLs
checkpoint_NNN.md # batch results (factcheck) or section drafts (trends/analyze)
report.md # final output, assembled from disk — never from memory
Rules that apply to every mode:
00_plan.md before searching anything. Include the question, sub-questions, and the sources you intend to hit.notes.md as you go with source URLs. If the session dies, you resume from disk.cat / Read over checkpoints. Never reconstruct from memory.ls the workspace, read the last checkpoint, continue from the next unprocessed item.Goal: identify the current state and trajectory of a space.
tavily_search for each sub-question. For technical topics, also resolve-library-id + query-docs (Context7) to ground claims in current docs.report.md:
Prioritize actionable insight over exhaustive coverage.
Goal: deep, balanced decomposition of a single topic.
tavily_search / tavily_extract. For each, capture both the strongest case for and the strongest case against.report.md:
Depth over breadth. Nuance over coverage.
Goal: extract every factual claim from a document and label each one with evidence.
Read the document.00_claims_extracted.md before any verification.opinions.md if useful for the final summary.For each batch:
tavily_search for current/breaking and synthesis-heavy claims, tavily_extract for primary-source verification.checkpoint_NNN.md with full findings for the batch, then drop the details from memory — keep only claim# | verdict | confidence lines.Checkpoint format:
# Checkpoint NNN — Claims X–Y
## Claim #X: <verbatim claim>
**Status**: VERIFIED | FALSE | MISLEADING | UNVERIFIABLE | OUTDATED
**Evidence**: <urls>
**Analysis**: <what the sources say vs. the claim>
**Confidence**: HIGH | MEDIUM | LOW
Read all checkpoints from disk and assemble report.md:
## FACT-CHECK REPORT: <doc title>
### Summary
<N claims checked: V verified, F false, M misleading, U unverifiable, O outdated>
### Detailed Findings
<one block per claim, same format as checkpoint>
### Overall Assessment
<one paragraph on the document's factual reliability>
Be impartial. Distinguish FALSE (contradicted by evidence) from UNVERIFIABLE (insufficient evidence either way). Cite sources for every verdict.
npx claudepluginhub arozumenko/sdlc-skills --plugin test-automation-workflowGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.