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 WillInvest/ClaudeX --skill deep-researchgit clone --depth 1 https://github.com/WillInvest/ClaudeXWrote 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/willinvest/claudex/deep-research)<a href="https://agentmods.dev/skills/willinvest/claudex/deep-research"><img src="https://agentmods.dev/badge/skills/willinvest/claudex/deep-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 5.1 | $0.00193 | $0.02542 |
| Opus 5 | $0.00097 | $0.01271 |
| Sonnet 5 | $0.00039 | $0.00508 |
| Haiku 4.5 | $0.00019 | $0.00254 |
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 7d 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deep-research
A subscription-only port of NVIDIA AI-Q's deep-research core (Apache-2.0 — see NOTICE). The
main agent is the orchestrator: it plans, fans out parallel Task researchers that use
native WebSearch/WebFetch, gap-fills, synthesizes a long-form cited report, and then runs a
deterministic citation verifier so every [n] maps to a real, captured source URL.
$SKILL_DIR below is this skill's directory; prompts live in prompts/, the verifier in
scripts/verify_citations.py. Read each prompts/*.md when you reach its step.
When to use
Researched reports, landscape/market/literature scans, cited comparisons, "deep research on …", "research X and tell me the alternatives". Skip it for trivial chit-chat or pure factual one-liners (the router will route those to a direct answer / shallow pass).
Flow
0. Route & disambiguate — read prompts/router.md. Classify meta vs research, then
shallow vs deep. For a plainly trivial question, answer directly. If the query is genuinely
ambiguous and you can't pause for the user, state ONE interpretation line at the top of the
report and proceed.
1. Set up a per-run working dir — create a fresh run directory:
<vault>/<topic>/runs/<run-id>/. Inside it create:
<run-dir>/notes/<run-dir>/yt-report/<run-dir>/report_draft.md<run-dir>/report_final.md<run-dir>/audit.json
Prior runs are never verifier input. The lane must not write to persistent shared notes/;
only the current run's <run-dir>/notes/ is scanned by the verifier.
2. Plan — read prompts/planner.md. Run 2-4 scoping WebSearch calls, then produce the
plan object: task analysis, report title, TOC (≤8 sections), constraints (acceptance
criteria), and 4-6 self-contained queries. Keep the plan in your context.
3. Research (parallel fan-out) — read prompts/researcher.md. Group the web queries into
2-3-per-bundle and launch up to 6 Task (general-purpose) subagents in a single
message so they run concurrently. Give each subagent: the researcher prompt, its bundled
questions, the relevant constraints, and an instruction to write its notes to
<run-dir>/notes/researcher_<k>.md (a dedicated notes subdir — keep researcher notes separate from the draft; the verifier only scans researcher_*.md files, so this naming is REQUIRED) and also return them. Each researcher uses only
WebSearch/WebFetch, ≤8 calls, broad→narrow, and lists ONLY real captured URLs.
What ships with it
18 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.
- .gitignore 19 B
- NOTICE 3.7 KB
- prompts/citations.md 5.2 KB
- prompts/codex_check.md 1.1 KB
- prompts/planner.md 3.5 KB
- prompts/researcher_youtube.md 2.3 KB
- prompts/researcher.md 2.5 KB
- prompts/router.md 3.1 KB
- prompts/synthesis.md 2.5 KB
- references/aiq-mapping.md 4.0 KB
- scripts/filter_yt_notes.py 20 KB runs code
- scripts/verify_citations.py 39 KB runs code
- tests/test_adversarial.py 13 KB runs code
- tests/test_filter_yt_notes.py 20 KB runs code
- tests/test_round4.py 3.0 KB runs code
- tests/test_round5.py 1.9 KB runs code
- tests/test_verify_citations.py 9.8 KB runs code
- tests/test_youtube_lane_contract.py 6.1 KB runs code
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.
- 7d ago First seen · 168 lines · 193 tokens per session scan A a4869d3db01b
deep-research is a skill published in the GitHub repository WillInvest/ClaudeX (4 stars, last pushed 3mo ago), licensed MIT. It adds 193 tokens to every session and 2,542 once invoked, about $0.0010 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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pdlc-test-setup
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