/topic

name: topic
description: "Research State of the Art (SOTA) literature for an Artificial Intelligence / Machine Learning (AI/ML) topic, method, or architecture. Finds relevant papers, builds a comparison table, recommends the best implementation strategy for the current codebase, and optionally produces a phased implementation plan mapped to the codebase. Owns broad SOTA search end-to-end via foundry:web-explorer; delegates codebase mapping to foundry:solution-architect."
argument-hint: "<topic> [--team] | plan [<output.md>] [--keep \"<items>\"]"
allowed-tools: Read, Write, Bash, Grep, Glob, Agent, WebSearch, WebFetch, TaskCreate, TaskUpdate, AskUserQuestion, TaskList
disable-model-invocation: true
effort: medium

<objective>

Research AI/ML topic literature. Return actionable findings: SOTA methods, best fit, concrete implementation plan. Skill = orchestrator — gathers codebase context, delegates literature search to researcher agent, packages results into structured report.

NOT for deep single-paper analysis or experiment design — use `research:scientist` directly for hypothesis generation, ablation design, experiment validation.

</objective>

<inputs>

• **$ARGUMENTS**: one of:
• `<topic>` — topic, method name, or problem description (e.g. "object detection for small objects", "efficient transformers", "self-supervised pretraining for medical images")
• `plan` — produce phased implementation plan from most recent research output (auto-detected from `.temp/`)
• `plan <path-to-output.md>` — produce plan from specific existing research output file
• `--team` — multi-agent mode; spawns 2–3 researcher teammates for topics with 3+ competing method families and no SOTA consensus; ~7× token cost vs single-agent mode

</inputs>

<compaction>

Key boundary: end of Step 2 — SOTA literature gathered and written to AGENT_OUT; before Step 3 report synthesis. Preserve: AGENT_OUT path (TMPDIR key), BRANCH (TMPDIR key), DATE (TMPDIR key), REPORT_OUT target path, topic string from ARGUMENTS. Clear at Step 1 start (stale prior run) and at follow-up gate (terminal action).

</compaction>

<workflow>

<!-- Agent resolution: see _RESEARCH_SHARED/agent-resolution.md -->

Agent Resolution

**Agent resolution**: load and follow the protocol below. Contains: foundry check + fallback table. Foundry not installed → substitute each `foundry:X` with `general-purpose` per table. Agents this skill uses: `foundry:solution-architect`.

# loads: compaction-contract.md
_RESEARCH_SHARED=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/cc_research}/bin/resolve_shared.py" 2>/dev/null)  # timeout: 5000
[ -z "$_RESEARCH_SHARED" ] && { echo "! Plugin path resolution failed — ensure research plugin installed and CLAUDE_PLUGIN_ROOT set, or invoke from project root."; exit 1; }
cat "$_RESEARCH_SHARED/agent-resolution.md"

**Task hygiene**: Before creating tasks, call `TaskList`. For each found task:

• status `completed` if work clearly done
• status `deleted` if orphaned / no longer relevant
• keep `in_progress` only if genuinely continuing

**Task tracking**: per CLAUDE.md, create tasks (TaskCreate) for each major phase — paper collection, researcher analysis, report generation. Mark in_progress/completed throughout. Always create **"Print report header"** as its own task (all paths — single-agent Step 3, `--team`, `plan`) — `in_progress` right after the report file is written (by the lead directly, or by a spawned consolidator's returned envelope); `completed` only once the `---` header has actually appeared in this response. This task exists because a sibling skill (oss:review) had an incident where a report was written correctly but the terminal print step got silently skipped while the hard-enforced `AskUserQuestion` fired anyway — tracking the print as its own task makes it as trackable as the tool calls around it. The shared `## Follow-up gate` below must not fire while this task is `pending`/`in_progress`.

Step 1: Understand the codebase context

Read current project before searching, extract constraints:

• Framework (PyTorch, JAX, TensorFlow, scikit-learn)?
• Task (classification, detection, generation, regression)?
• Constraints (latency, memory, dataset size, compute budget)?

**Case-insensitive flag/mode normalization** — normalize before parsing so `--PLAN`, `--Team`, `Plan`, etc. accepted. Each Bash tool call runs fresh shell, so lowercased copy does NOT persist across blocks — re-derive inline from `$ARGUMENTS` (harness-substituted every block) wherever dispatch check needs it, e.g. `echo "$ARGUMENTS" | tr '[:upper:]' '[:lower:]' | …`. Preserve original `$ARGUMENTS` only where literal substitution into prompts required (e.g. topic string).

**Unsupported flag check** (runs BEFORE any mode dispatch to catch unknown flags in all modes): load and follow the protocol below. Supported flags for this skill: `--team`, `--keep`.

# loads: unsupported-flag-protocol.md
_RESEARCH_SHARED=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/cc_research}/bin/resolve_shared.py" 2>/dev/null)  # timeout: 5000
cat "$_RESEARCH_SHARED/unsupported-flag-protocol.md"

# Extract --keep quoted value (compaction-contract.md §keep semantics)
KEEP_ITEMS=""
if [[ "$ARGUMENTS" =~ --keep[[:space:]]\"([^\"]+)\" ]]; then
    KEEP_ITEMS="${BASH_REMATCH[1]}"
fi
export CSID="${CLAUDE_CODE_SESSION_ID:-$PPID}"
# Clear stale contract from any prior incomplete run (compaction-contract.md §Lifecycle)
rm -f .temp/state/skill-contract.md  # timeout: 5000
echo "${KEEP_ITEMS:-}" > "${TMPDIR:-/tmp}/topic-keep-items-${CSID}"  # persist for Step 2 contract write

UNKNOWN_FLAGS=$(echo "$ARGUMENTS" | tr '[:upper:]' '[:lower:]' | grep -oE -- '--[a-z][a-z0-9-]+' | grep -v -- '--team' || true)  # timeout: 5000

**Early dispatch for `--team` and `plan` modes** — check BEFORE Steps 2-3. Priority: `--team` wins over `plan` (`plan --team` → Team Mode, topic string = "plan"):

FIRST_WORD=$(echo "$ARGUMENTS" | tr '