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/catwillgh/mainframe/mainframe-research-methodnpx skills add CATWILLgh/MAINFRAME --skill mainframe-research-methodgit clone --depth 1 https://github.com/CATWILLgh/MAINFRAMEWhat 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 | $0.00053 | $0.00795 |
| Opus 5 | $0.00026 | $0.00398 |
| Sonnet 5 | $0.00011 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
mainframe-research-method 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research method
Research certainty is claim-local, not report-wide. Classify and verify each material claim independently, then combine only findings that remain compatible.
Boundary
- Work only from the question and project facts supplied by the caller plus external sources. Do not inspect the project or infer missing local state.
- Establish evidence. Do not recommend, select, or advocate for an option.
- Use English internally and in the returned package.
- Stop when decision-relevant external uncertainty is resolved. More links are not evidence when they repeat the same underlying source.
Method
- Normalize the question: entities, jurisdiction, product or dataset version, reference period, comparison baseline, units, and the date on which the answer must be current. Return a missing load-bearing input as a limitation.
- Split the question into atomic, falsifiable claims. For each material claim, assign every applicable domain rather than one label for the whole report.
- Read every applicable domain profile before researching that claim:
- Software documentation for APIs, libraries, tools, protocols, releases, compatibility, and security notices.
- Economics and quantitative data for statistics, prices, rates, money, ratios, forecasts, surveys, and calculated changes.
- News and current events for events, announcements, disputes, developing stories, and time-sensitive public claims. This is a required preparation step, not a routing hint. When a claim crosses domains, read every matching profile rather than only the first one.
- Prefer the closest authoritative primary source. Use a second independent source when the claim is disputable, interpretive, fast-changing, supplied by an interested party, or consequential enough that one source is fragile. A canonical fact may rest on one controlling primary source.
- Trace provenance. Syndicated reports, copied tables, mirrors, and articles citing the same statement are one evidence chain, not cross-confirmation.
- Match every number and date to the source before using it. Preserve units, scale, currency, timezone, reference period, publication date, revision status, and rounding. For a derived value, show the operands and formula and verify the arithmetic; if the available tools cannot verify it reliably, return the operands instead of asserting the result.
- Record contradictions without averaging or silently selecting a convenient value. Explain whether they arise from timing, definitions, versions, methodology, corrections, or a genuinely unresolved conflict.
- Before returning, check each load-bearing citation against the source content already retrieved. Fetch it again only when the earlier result was incomplete or the source could have materially changed during the research. Confirm that it supports the exact nearby claim and is current as of the research date.
- Do not repeat a failed retrieval through superficial URL variants when the returned content or error is unchanged. After two genuinely different paths fail to expose claim-supporting content, record the limitation and continue with other evidence or stop.
What ships with it
3 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 · 70 lines · 53 tokens per session scan A e03de57d84e7
mainframe-research-method is a skill published in the GitHub repository CATWILLgh/MAINFRAME (2 stars, last pushed 11d ago), licensed MIT. It adds 53 tokens to every session and 795 once invoked, about $0.0003 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 2026-08-31.
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