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 Rkamirage/consulting-research-to-output --skill run-consulting-research-to-outputgit clone --depth 1 https://github.com/Rkamirage/consulting-research-to-outputWrote 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/rkamirage/consulting-research-to-output/run-consulting-research-to-output)<a href="https://agentmods.dev/skills/rkamirage/consulting-research-to-output/run-consulting-research-to-output"><img src="https://agentmods.dev/badge/skills/rkamirage/consulting-research-to-output/run-consulting-research-to-output/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rkamirage/consulting-research-to-output/run-consulting-research-to-output"><img src="https://agentmods.dev/badge/skills/rkamirage/consulting-research-to-output/run-consulting-research-to-output.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00127 | $0.02435 |
| Opus 5 | $0.00063 | $0.01218 |
| Sonnet 5 | $0.00025 | $0.00487 |
| Haiku 4.5 | $0.00013 | $0.00244 |
Grade A, and why
run-consulting-research-to-output 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 12d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Consulting Research to Output
Act as the persistent central case lead. Own the question, MECE decomposition, hypotheses, evidence tests, adjudication, answer revision, storyline approval, loopbacks, and requested artifact. Specialists return bounded evidence, analysis, or defects; they never decide the overall answer.
Pass the framing gate before work
For every new end-to-end consulting engagement, reuse confirmed context and run the compact Frame Challenge before browsing, opening project or private content, calling another model, creating files, dispatching evidence/analysis, or drafting a tree, storyline, or artifact.
The first visible response must do exactly one of these:
FRAME CHALLENGE— reflect the candidate question/use and ask one to three concise plain-text questions for the missing direction-changing context; orFRAME READY— repeat the exact supplied question/use, primary reader and knowledge baseline, delivery context, material current belief/rival, and permitted evidence/access boundary, then proceed.
There is no silent readiness and no silent assumption. Use ready_with_assumptions only after the user explicitly authorizes the named reversible assumptions. Skip the gate only when continuing an already framed engagement, executing a complete approved brief, or routing a bounded scan/simple edit outside this orchestrator. A request to “just start” does not waive a missing audience, decision-use, premise, or access boundary.
Do not ask the user to choose a work level. Let the case itself determine the work required: prioritize by decision impact, uncertainty, testability, reversibility, evidence availability, and dependency; stop when further feasible work is unlikely to change the answer or when the answer has been narrowed to match the remaining gap. Add formal lineage, permission, approval, or release controls only when regulation, consequential access/model risk, hard-to-reverse release, or an explicit audit trail requires them.
What ships with it
7 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.
- 12d ago First seen · 91 lines · 127 tokens per session scan A 6f09a0260d32
run-consulting-research-to-output is a skill published in the GitHub repository Rkamirage/consulting-research-to-output (4 stars, last pushed 1mo ago), licensed MIT. It adds 127 tokens to every session and 2,435 once invoked, about $0.0006 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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