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.
npx agentmods add skills/borda/ai-rig/topicnpx skills add Borda/AI-Rig --skill topicgit clone --depth 1 https://github.com/Borda/AI-RigWhat 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.
| Model | Per session | Once 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 |
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.
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
completedif work clearly done - status
deletedif orphaned / no longer relevant - keep
in_progressonly 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.
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.
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.
- 2d ago First seen · 313 lines · 92 tokens per session scan A 0caca9d09108
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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