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 skillmds/skillmd --skill researchgit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/research)<a href="https://agentmods.dev/skills/skillmds/skillmd/research"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/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/skillmds/skillmd/research"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/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 | $0.00066 | $0.01774 |
| Opus 5.5 | $0.00026 | $0.00710 |
| Sonnet 5 | $0.00013 | $0.00355 |
| Haiku 4.5 | $0.00007 | $0.00177 |
Grade A, and why
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 4d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an autonomous research-to-ideation agent. Do NOT ask the user questions. Run the full pipeline below without pausing between phases.
TARGET: $ARGUMENTS
============================================================ PHASE 1: COMPETITIVE GAP ANALYSIS (/compete)
PARALLEL EXECUTION: Use the Agent tool to run competitive analysis and feature discovery concurrently when both are independent.
- Agent A (Competitive Analysis): "Run /compete skill instructions — analyze the competitive landscape for this project. Return competitive gaps and opportunities."
- Agent B (Feature Discovery): "Run /new-features skill instructions — discover potential features from project docs and memory. Return feature candidates with priority."
- Wait for both agents to complete.
- Cross-reference findings: features that address competitive gaps get priority boost.
Follow the instructions defined in the /compete skill exactly.
Produce the full Competitive Gap Analysis output including all sections
(Product Identity, Competitive Landscape, Feature Matrix, Critical Gaps,
Strategic Gaps, Differentiators, Our Edges, Industry Trends, Recommended Roadmap, Summary).
Save the report to docs/competitive-gap-analysis.md
as specified by the /compete skill (create the docs/ directory if it doesn't exist).
Do NOT stop here. Continue immediately to Phase 2.
============================================================ PHASE 2: TECHNOLOGY TREND RESEARCH
Research current technology trends relevant to this project's domain. Use web search to find:
- Emerging Technologies — New frameworks, APIs, platforms, or paradigms that competitors or adjacent products are adopting (e.g., on-device ML, spatial computing, voice-first UX, AI-native workflows).
- Developer Ecosystem Shifts — Changes in tooling, package ecosystems, or platform capabilities that could unlock new features or reduce cost (e.g., new OS APIs, free-tier expansions, open-source alternatives).
- UX/Design Trends — Interaction patterns gaining traction in the category (e.g., progressive disclosure, ambient computing, micro-animations).
- Regulatory & Standards — Upcoming regulations, accessibility mandates, or industry standards that may force or enable product changes.
For each trend, note:
- Trend name
- Relevance (HIGH / MEDIUM / LOW) — how directly it applies to this project
- Adoption window — how soon this matters (NOW / 6 months / 12+ months)
- Opportunity — what feature or improvement it could enable
Save to docs/technology-trends.md.
Do NOT stop here. Continue immediately to Phase 3.
============================================================ PHASE 3: USER FEEDBACK ANALYSIS
Gather and analyze real user feedback from available sources:
- App Store Reviews — If the product (or key competitors) are on app stores, search for reviews. Focus on 1-3 star reviews to surface pain points, and 4-5 star reviews to find beloved features users would miss.
- GitHub Issues — If the project or competitors have public repos, scan open and recently closed issues for feature requests, common bugs, and recurring complaints.
- Community Signals — Search Reddit, forums, Twitter/X, or HackerNews for discussions about the product category. Note unmet needs users express.
Produce a summary with:
- Top 10 Pain Points — ranked by frequency/severity across all sources
- Top 5 Beloved Features — what users love and would be angry to lose
- Top 5 Feature Requests — most-requested features that don't exist yet
- Sentiment Summary — overall user sentiment toward the category
Save to docs/user-feedback-analysis.md.
Do NOT stop here. Continue immediately to Phase 4.
============================================================ PHASE 4: FEATURE IDEATION (/new-features)
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.
- 4d ago First seen · 196 lines · 66 tokens per session scan A 7ab46c1e0741
research is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 1,774 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 2026-09-19.
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poly-lint-and-format
Running poly lint / poly fmt — --fix, --format pretty|json|toon, --exclude, --config, exit codes, inline suppression, and the check → read-json → fix → re-check loop.
poly-orchestrator
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poly-mcp
The poly MCP server — the eleven tools and which are read-only vs mutating, the paths/exclude/config/format params, async Tasks for the whole-project phase, and the isError contract.
poly-tiers-and-scope
Five mechanisms decide what actually inspects a file (crates/poly-core/src/registry.rs).