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 ne11nn/cantos-plugin --skill market-researchgit clone --depth 1 https://github.com/ne11nn/cantos-pluginWrote 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/ne11nn/cantos-plugin/market-research)<a href="https://agentmods.dev/skills/ne11nn/cantos-plugin/market-research"><img src="https://agentmods.dev/badge/skills/ne11nn/cantos-plugin/market-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/ne11nn/cantos-plugin/market-research"><img src="https://agentmods.dev/badge/skills/ne11nn/cantos-plugin/market-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00032 | $0.01566 |
| Opus 5 | $0.00016 | $0.00783 |
| Sonnet 5 | $0.00006 | $0.00313 |
| Haiku 4.5 | $0.00003 | $0.00157 |
Grade A, and why
market-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 9d 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research Skill
A comprehensive market research methodology that leverages web search to gather real market data, analyze competitors, and synthesize actionable insights.
When to Use
- Researching a new market or industry
- Analyzing competitors before product launch
- Understanding target audience demographics and behaviors
- Identifying market opportunities and gaps
- Assessing risks before market entry
- Validating business ideas or product concepts
Research Framework
Phase 1: Market Overview
Gather high-level market intelligence:
Market Size & Growth:
- Total addressable market (TAM)
- Serviceable addressable market (SAM)
- Market growth rate (CAGR)
- Key market segments
Industry Trends:
- Recent industry developments
- Emerging technologies
- Regulatory changes
- Economic factors
Data Sources:
- Industry reports (Statista, IBISWorld, Grand View Research)
- News articles and press releases
- Government statistics
- Trade publications
Phase 2: Target Audience Analysis
Profile the ideal customer:
| Factor | Analysis Points |
|---|---|
| Demographics | Age, location, income, education, occupation |
| Psychographics | Values, interests, lifestyle, attitudes |
| Needs | Pain points, desires, unmet needs |
| Behaviors | Purchase patterns, channels, decision factors |
| Segments | High-potential customer groups |
Research Methods:
- Search for industry consumer studies
- Analyze social media discussions
- Review competitor customer bases
- Identify buying triggers and barriers
Phase 3: Competitor Landscape
Map the competitive environment:
Competitor Identification:
- Direct competitors (same product/market)
- Indirect competitors (different approach, same need)
- Potential entrants (adjacent markets)
Analysis Framework:
| Competitor | Market Position | Strengths | Weaknesses | Recent Moves |
|---|---|---|---|---|
| [Name] | [Leader/Challenger/Niche] | [List] | [List] | [News] |
What ships with it
1 file 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.
- 9d ago First seen · 230 lines · 32 tokens per session scan A 516e6c1f1827
market-research is a skill published in the GitHub repository ne11nn/cantos-plugin (1 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 1,566 once invoked, about $0.0002 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.
Other skills, from other repositories
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows.
run
Run the agents-concerto multi-agent development pipeline for a task — classify by complexity, implement in isolated worktrees (TDD + Tidy First), review under a two-party boundary, and stop at open PRs for human merge. Invoke explicitly with a task description; a weak or empty description is shaped interactively first…
shape
Turn a rough idea into a well-formed task the pipeline can run — interrogate the ambiguity, derive testable acceptance criteria, flag contradictions, and write .agent-workspace/feature-request.md in the exact shape /run consumes. The upstream step before /agents-concerto:run. Invoke explicitly with a rough idea.
setup
Bootstrap agents-concerto in the current project by interviewing the user for every configurable option — target repos (with per-repo PR host), tasksource, base branch, commit convention, and fix-cycle cap — then writing a filled .agent-workspace/config.md. Detects sensible defaults and asks for confirmation rather…
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.