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 evoelsewhere/evoflux --skill deep-researchgit clone --depth 1 https://github.com/evoelsewhere/evofluxWrote 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/evoelsewhere/evoflux/deep-research)<a href="https://agentmods.dev/skills/evoelsewhere/evoflux/deep-research"><img src="https://agentmods.dev/badge/skills/evoelsewhere/evoflux/deep-research/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/evoelsewhere/evoflux/deep-research"><img src="https://agentmods.dev/badge/skills/evoelsewhere/evoflux/deep-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00085 | $0.01253 |
| Opus 5 | $0.00043 | $0.00626 |
| Sonnet 5 | $0.00017 | $0.00251 |
| Haiku 4.5 | $0.00009 | $0.00125 |
Grade A, and why
deep-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 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Orchestrate parallel research delegated team members, then write one coherent cited report. Research is parallel; writing is single-point — never let multiple agents write report sections.
Step 0 — Always first
- Run
date +%Y-%m-%dvia Bash. Never assume the current year from training data. - Triage:
- Answerable with 1-2 searches? → STOP, just use
web_searchdirectly. Do not use this skill. - Enumeration task (N items × M fields, e.g. "compare 20 frameworks")? → still this skill, but use table-oriented decomposition (one delegated team member per item batch).
- Open-ended investigation? → continue below.
- Answerable with 1-2 searches? → STOP, just use
- Pick depth (default standard; user can override with words like "quick"/"exhaustive"):
| Mode | Delegated members (round 1) | Max follow-up rounds | Sources target |
|---|---|---|---|
| quick | 2-3 | 0 | 8+ |
| standard | 3-5 | 1 | 15+ |
| deep | 5-8 | 2 | 25+ |
These are hard budgets. Reflection (Phase 4) can spend them but never exceed them.
Workspace
All state lives on disk at ./research/<slug>/ — never only in context (survives compaction):
research/<slug>/
├── brief.md # research brief — the single contract for all phases
├── findings/ # F1.md, F2.md ... one per delegated team member, structured evidence
└── REPORT.md # final deliverable
On resume: re-read brief.md + list findings/, skip completed angles, continue.
Phase 1 — Scope
Ask at most one round of clarifying questions (ask_user), only if genuinely ambiguous: audience, time frame, region, decision at stake. If the user said "just run it" or intent is clear, skip asking and write assumptions into the brief instead.
Then write brief.md: refined question, scope boundaries (in/out), assumptions, depth mode, today's date. This brief — not the raw conversation — is what every later phase measures against.
Phase 2 — Plan
Decompose the brief into 3-8 independent research angles. Pull from these lenses as applicable: core facts/definitions · recent developments (last 12 months) · quantitative data/benchmarks · counter-arguments & failure cases · practitioner experience (forums, issues) · academic work · key players/alternatives.
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.
- 2d ago First seen · 92 lines · 85 tokens per session scan A 379ece531824
deep-research is a skill published in the GitHub repository evoelsewhere/evoflux (7 stars, last pushed today), licensed Apache-2.0. It adds 85 tokens to every session and 1,253 once invoked, about $0.0004 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 2026-09-06.
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