Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill market-research-reportsgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/market-research-reports)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/market-research-reports"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/market-research-reports/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/k-dense-ai/scientific-agent-skills/market-research-reports"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/market-research-reports.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00054 | $0.03200 |
| Opus 5.5 | $0.00022 | $0.01280 |
| Sonnet 5.5 | $0.00011 | $0.00640 |
| Haiku 4.5 | $0.00005 | $0.00320 |
Grade A, and why
market-research-reports 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 26d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- market-research-reports — 100% identical, 23 lines differ
- market-research-reports — 92% identical, 23 lines differ
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research Reports
Purpose
Create decision-focused market reports whose claims, calculations, assumptions, and uncertainties can be audited. Match depth and format to the question and evidence. There is no required length, chapter count, visual count, or output format.
Do not:
- imitate or imply affiliation with a consulting, analyst, or research brand;
- invent citations, quotes, market shares, or paid-market figures;
- present TAM/SAM/SOM or a forecast as one certain truth;
- treat a framework, chart, or fluent narrative as evidence;
- provide investment, legal, antitrust, tax, accounting, or regulatory advice.
Operating principles
- Define before sizing. Fix product, customer, geography, channel, period, measure, unit, denominator, currency/base year, and taxonomy.
- Map every claim. Every factual or quantitative claim has a claim ID and exact source IDs.
- Separate statement types. Distinguish facts, estimates, calculations, forecasts, opinions, and recommendations.
- Prefer primary evidence. Use official statistics, regulator records, filed company disclosures, and transparent original studies before secondary synthesis.
- Preserve uncertainty. Retain source conflicts, revisions, scenario ranges, sensitivity, and limitations.
- Keep methods reproducible. Use local structured inputs and deterministic calculations when practical.
- Collect lawfully and ethically. No deception, PII disclosure, access circumvention, confidential material, or trade-secret acquisition.
Workflow
1. Establish the research contract
Clarify:
- decision, audience, deadline, and materiality threshold;
- formal market definition and adjacent exclusions;
- buyer, payer, user, transaction, and value-chain level;
- geography and treatment of imports, exports, and channels;
- historical period, forecast period, and retrieval cutoff;
- revenue/expenditure, gross output/value added, units, capacity, users, or another measure;
- stock/flow, gross/net, taxes, and denominator;
- currency, base year, and nominal/real/current/constant basis;
- industry and product classification with version;
- permitted data sources, primary research, confidentiality, and output format.
What ships with it
25 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.
- assets/claims_ledger_template.csv 957 B
- assets/competitor_feature_matrix_template.csv 683 B
- assets/consistency_check_template.csv 432 B
- assets/forecast_sensitivity_template.json 1.8 KB
- assets/FORMATTING_GUIDE.md 5.8 KB
- assets/market_report_template.tex 9.8 KB
- assets/market_research.sty 6.3 KB
- assets/market_sizing_scenarios_template.json 3.1 KB
- assets/report_manifest_template.json 882 B
- assets/source_ledger_template.csv 1.3 KB
- references/data_analysis_patterns.md 9.8 KB
- references/evidence_model.md 6.0 KB
- references/methods_and_ethics.md 7.9 KB
- references/official_data_sources.md 11 KB
- references/report_structure_guide.md 8.4 KB
- references/sources.md 10 KB
- references/visual_generation_guide.md 5.1 KB
- scripts/_common.py 11 KB runs code
- scripts/audit_claim_citations.py 11 KB runs code
- scripts/calculate_market_sizing.py 13 KB runs code
- scripts/check_unit_consistency.py 7.0 KB runs code
- scripts/forecast_sensitivity.py 11 KB runs code
- scripts/generate_report_scaffold.py 15 KB runs code
- scripts/validate_competitor_matrix.py 7.6 KB runs code
- scripts/validate_evidence_ledger.py 9.0 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.
- 26d ago Changed · +13 lines · +1 tokens per session 8fba27d8509b
- 27d ago Changed · +6 lines e858bb1ae55d
- 1mo ago First seen · 355 lines · 53 tokens per session scan A d0e4899ac905
market-research-reports is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 3,200 once invoked, about $0.0002 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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