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 skills add ericrisco/rsc-harness --skill research-opsgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ericrisco/rsc-harness/research-ops)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/research-ops"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/research-ops.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.02733 |
| Opus 5 | $0.00053 | $0.01367 |
| Sonnet 5 | $0.00021 | $0.00547 |
| Haiku 4.5 | $0.00011 | $0.00273 |
Grade A, and why
research-ops 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 4d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research-ops — the deep-research operating procedure
You are the method, not the topic. Someone hands you "go find out about X" and you hand back a memo where every load-bearing claim traces to a source, a date, and a confidence tier — a document that survives someone reading it adversarially. The topic changes every time; the procedure does not.
Two hard rules, stated up front because everything else hangs off them:
- One search pass is not research. A single query plus reasoning over the snippets is a guess with footnotes. Real research is an iterative loop — a deep-research run typically reads 20–100+ sources, re-querying as gaps appear, until coverage holds or a budget caps it.
- An unsourced claim is not a finding. If you can't attach a source and a date, it's an assumption — label it as one or cut it. Human review catches AI errors in roughly 15–20% of research reports, so structure the output so a reviewer can check each claim, never so they have to trust it.
The loop
Research is an ordered loop, not a lookup. Run it in this order; each step has a reason.
- Scope — pin the question down before spending a single search (see Scope first). Why: a fuzzy question burns the budget on the wrong sources.
- Plan queries — write 3–6 distinct query angles, not one phrasing repeated. Cover the claim, the counter-claim, and the primary source. Why: you can't triangulate what you only searched one way.
- Fan out — run the searches in parallel; collect candidate sources. Why: breadth first exposes disagreement you'd miss going one source deep.
- Fetch & read — open the actual pages, not the result snippets. Read the primary source, not the blog summarizing it. Why: snippets drop caveats, dates, and numbers.
- Re-query on gaps — every read surfaces a new unknown or a contradiction; feed it back as a new query. Why: this is the part that makes it a loop instead of a list.
- Triangulate — confirm each load-bearing claim across ≥2 independent sources; record where they disagree. Why: triangulation is the foundation of a credible finding.
- Synthesize — write the memo answer-first, every claim carrying
[source, date, confidence], with an explicit "couldn't verify" section. Why: the memo is the deliverable; the searches were just inputs.
What ships with it
5 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.
- 4d ago First seen · 201 lines · 105 tokens per session scan A 6005a5cbc971
research-ops is a skill published in the GitHub repository ericrisco/rsc-harness (70 stars, last pushed today), licensed MIT. It adds 105 tokens to every session and 2,733 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 2026-09-03.
Other skills, from other repositories
verify-before-done
Proves work with tests, diffs, and logs before sign-off.
cite-specific-section-numbers
How to enforce Section-Anchored Citations when analyzing manuals, legal contracts, and compliance policies to make every AI claim instantly verifiable.
Verification & Quality Assurance
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
Web Research Report
Synthesize fetched web sources into a structured research report on a topic: key findings, themes, tradeoffs, and a recommendation, with citations. Use for web research, literature review, and report writing.
todos
This chat has a shared, live TODO plan — your tasks for the conversation, which the user also edits. Read this skill and reach for the todo tools whenever a request takes more than a couple of steps. It covers the plan model (group = task, items = its steps; loose items are the user's lane), how to work it: propose…
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.