topic

A research workflow for finding and comparing recent methods for an artificial-intelligence or machine-learning topic. It turns the literature into a recommendation for a specific codebase.

In plain words
What is it for?
Use it to research a topic or architecture, compare state-of-the-art methods, choose a suitable approach, and create a phased implementation plan.
Why use it?
It saves time when many papers and competing approaches exist, and connects published methods to a practical implementation plan.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/borda/ai-rig/topic
Any agent
npx skills add Borda/AI-Rig --skill topic
Clone the repo
git clone --depth 1 https://github.com/Borda/AI-Rig

Made for: Claude Code, Codex.

Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,329 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00092 $0.05329
Opus 5 $0.00046 $0.02665
Sonnet 5 $0.00018 $0.01066
Haiku 4.5 $0.00009 $0.00533

Measured 2d ago against content hash 0caca9d09108, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

topic scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/cc_research/skills/topic/SKILL.md · 313 lines

How it starts

The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

  • $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
  • 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).

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:web-explorer, foundry:solution-architect.

# loads: compaction-contract.md
export CSID="${CLAUDE_CODE_SESSION_ID:-$PPID}"
_RESEARCH_SHARED=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/cc_research}/bin/resolve_shared.py" 2>/dev/null)  # timeout: 5000
[ -z "$_RESEARCH_SHARED" ] && { echo "! Plugin path resolution failed — ensure research plugin installed and CLAUDE_PLUGIN_ROOT set, or invoke from project root."; exit 1; }
echo "$_RESEARCH_SHARED" > "${TMPDIR:-/tmp}/research-shared-${CSID}"  # cold resolve — every later site reads this sentinel instead of re-running python
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.

Read the full file on GitHub · 313 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 313 lines · 92 tokens per session scan A 0caca9d09108

Subscribe to this mod's changes

topic is a skill published in the GitHub repository Borda/AI-Rig (25 stars, last pushed 9d ago), licensed Apache-2.0. It adds 92 tokens to every session and 5,329 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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