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/danmcinerney/orchflows/super-researchnpx skills add DanMcInerney/orchflows --skill super-researchgit clone --depth 1 https://github.com/DanMcInerney/orchflowsWrote 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/danmcinerney/orchflows/super-research)<a href="https://agentmods.dev/skills/danmcinerney/orchflows/super-research"><img src="https://agentmods.dev/badge/skills/danmcinerney/orchflows/super-research.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.00610 |
| Opus 5 | $0.00013 | $0.00305 |
| Sonnet 5 | $0.00005 | $0.00122 |
| Haiku 4.5 | $0.00003 | $0.00061 |
Grade A, and why
super-research 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 today.
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.
What it actually says
Require: one bounded question naming its live sources, its window
where it has one, a frozen as_of at or after the run's own reads, and a
hard per-step cap.
Open the frame, its goal the answered question:
tickets.py frame-open <run> --goal-file <question-goal> --workflow super-research
Acquisition — fan-out: "One do per named item, launched together
under the frame; the shape line lists them as one wave."
tickets.py do <run> --standard orch-research --skill research-acquire
--parent <frame> --goal-file <source-goal> --bound "<= 40 tool calls"
Each goal names one source, the window, the as_of and the cap. Time-bounding
is per operation — research-acquire's WINDOW_REACH table decides — so a
windowed call whose operation cannot bound time at its origin returns
window_not_honored; name that source in the goal, so the child files a gap
rather than a silence.
Coverage loop, at most two rounds.
tickets.py judge <run> --standard orch-research --parent <frame>
--artifacts evidence:<id> [--artifacts ...] --goal-file <coverage-goal>
The coverage goal asks one thing: which sub-questions no record answers,
and which typed losses came back. Round two exists only for the gaps that
judge named — one do per gap, quoting the finding and the source that
closes it, then one final judge over the enlarged set — and a gap still
open is declare-gaps: "A
gap that remains is written as a gap, [] when there is none; silence is a
defect."
Report, one call, the frame's last.
tickets.py do <run> --standard orch-content --parent <frame>
--standard html-dossier --goal-file <report-goal> --bound "<= 40 tool calls"
Its goal asks for one dossier answering the question first, then each
source's evidence dated and cited from its normalized_locator, every typed
loss, contradiction and open sub-question, and each market's own price
string with the markets that already resolved dropped.
Never: average contradicting sources, read a typed loss as an absence, or quote a community comment without its author and count.
Return: tickets.py frame-close <run> <frame> --done <verifier>, whose
done is the dossier verifier over that doc: identity — every
load-bearing claim cited and dated, every loss stated, every unanswered
sub-question declared.
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.
- today Changed 3631a8117ebc
- yesterday Changed · +48 lines · +25 tokens per session fa3ccd7247d1
- 5d ago First seen · 9 lines · 0 tokens per session scan A eb8aa88922b5
super-research is a skill published in the GitHub repository DanMcInerney/orchflows (52 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 610 once invoked, about $0.0001 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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