Claude Code Skills Marketplace is a collection and marketplace of skills, plugins, agents, and instructions that extend Claude Code with specialized development workflows. It is for developers who want to install existing workflows or create, validate, and package their own Claude Code skills.
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 daymade/claude-code-skills --skill deep-researchgit clone --depth 1 https://github.com/daymade/claude-code-skillsWrote 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/daymade/claude-code-skills/deep-research)<a href="https://agentmods.dev/skills/daymade/claude-code-skills/deep-research"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/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/daymade/claude-code-skills/deep-research"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 522 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00063 | $0.07466 |
| Opus 5.5 | $0.00025 | $0.02986 |
| Sonnet 5.5 | $0.00013 | $0.01493 |
| Haiku 4.5 | $0.00006 | $0.00747 |
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 6d 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 — 595 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Create high-fidelity research reports with strict format control, evidence mapping, source governance, and multi-pass synthesis.
Architecture: Lead Agent + Subagents
Lead Agent (coordinator — minimizes raw search context)
|
P0: Environment + source policy setup
|
P1: Question and claim map (decision questions, evidence routes, stop rules)
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Dispatch ──→ Subagent A ──→ writes task-a.md ──┐
──→ Subagent B ──→ writes task-b.md ──┤ (parallel)
──→ Subagent C ──→ writes task-c.md ──┘
| |
| research-notes/ <────────────────────────┘
|
P2: Dispatch + investigate → evidence packets
P3: Citation registry + source governance
P4: Evidence-mapped outline with counter-evidence and unknowns
P5: Draft from evidence packets; reopen decisive originals
P6: Counter-review (claims, confidence, alternatives)
P7: Verify → asset check and catalog registration
Context discipline: Keep raw search-result noise in task workspaces. Pass evidence packets to the lead agent, including locators and short source excerpts. Notes are routing aids, not authorities: the lead agent must open the original source for every load-bearing claim, conflicting claim, and exact figure/date/quotation used in the report.
Run and asset contract — every invocation
Before external retrieval, read research-asset-contract.md. Create or resume a durable project study for single-route and multi-route research alike. Search its explicit prior-study catalog, open relevant earlier originals and record reuse/adapt/reject decisions. Draft the decision questions and provider × actual-mode lanes in study.json. Write one exact dispatch_context with the user-supplied seed URL, named entities, and verified codes or other identifiers needed for the query. Include that string in every lane prompt; research_assets.py start rejects an omission. Capture the user's named seed article or document as an original before external dispatch, and follow the provider handoff when a provider cannot read its URL. A model workspace may not see the user's link or local files just because the coordinator does. Then run provider_runs.py plan. Keep every user-requested mode in the plan; a route that adds no value, is unavailable or lacks paid authorization gets a reasoned deferred event. A report assembled from direct web search and internal subagents still needs a direct-source lane and the same archive.
What ships with it
22 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.
- references/completeness_review_checklist.md 1.1 KB
- references/counter_review_team_guide.md 6.4 KB
- references/enterprise_analysis_frameworks.md 6.9 KB
- references/enterprise_quality_checklist.md 3.6 KB
- references/enterprise_research_methodology.md 10 KB
- references/formatting_rules.md 918 B
- references/parallel-provider-ops.md 8.4 KB
- references/provider-run-contract.md 16 KB
- references/quality_gates.md 5.3 KB
- references/report_template_v6.md 2.4 KB
- references/report-file-collection.md 3.0 KB
- references/research_notes_format.md 3.6 KB
- references/research_plan_checklist.md 1.9 KB
- references/research_report_template.md 1.2 KB
- references/research-asset-contract.md 16 KB
- references/source_accessibility_policy.md 3.7 KB
- references/source_quality_rubric.md 3.9 KB
- references/subagent_prompt.md 3.5 KB
- scripts/provider_runs.py 25 KB runs code
- scripts/research_assets.py 38 KB runs code
- tests/test_provider_runs.py 25 KB runs code
- tests/test_research_assets.py 49 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.
- 6d ago Changed 4cbb96dfda45
- 7d ago Changed · +23 lines f3dbcfa02799
- 10d ago Changed · +4 lines f451d2cefd97
- 21d ago Changed · +14 lines · -25 tokens per session 98fe044e2e06
- 22d ago Changed · +18 lines · +3 tokens per session 8a189a2c41e9
- 24d ago Changed · -3 lines · -63 tokens per session 36dc7a979b9c
- 27d ago Changed · +2 lines · +32 tokens per session 59bd1c5f93df
- 1mo ago Changed · -8 lines · -39 tokens per session db96428c47be
- 1mo ago First seen · 545 lines · 155 tokens per session scan A ae34edc0b037
deep-research is a skill published in the GitHub repository daymade/claude-code-skills (1,473 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 7,466 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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