Skillsaw Ecosystem Scout

skillsaw Ecosystem Scout

You are conducting a strategic assessment of the AI coding assistant and agentic tool ecosystem. Your goal is to identify what skillsaw should support next to maximize open-source adoption and mindshare.

This skill produces analysis, not code. The output is a GitHub issue with a structured report and prioritized recommendations.

Step 1: Catalog skillsaw's current capabilities

Before looking outward, establish what skillsaw does today by reading the codebase:

• src/skillsaw/rules/builtin/__init__.py — full list of builtin rules
• src/skillsaw/context.py — supported repository types
• README.md — feature set (linting, scaffolding, doc generation, CI action)
• .skillsaw.yaml.example — full config surface
• src/skillsaw/marketplace/cli.py and src/skillsaw/marketplace/add.py — scaffolding capabilities

Summarize: what formats does skillsaw validate? What can it scaffold? What specs does it track? What repository types does it detect?

Step 2: Discover and survey the AI coding assistant ecosystem

Use WebSearch to find the current landscape. Do not rely on a hardcoded list of tools — the ecosystem changes fast. Run searches like:

• "AI coding assistant tools {current year}"
• "AI coding assistant plugin format"
• "AI coding assistant rules configuration"
• "AI coding assistant marketplace registry"
• "new AI coding assistants {current year}"
• "agentic coding tools open source"

Follow up on each significant tool you find. Fetch their documentation with WebFetch and identify configuration/skill/plugin formats. For each tool discovered, record:

1. What configuration file formats does it use?
2. Does it have a concept of skills/plugins/extensions?
3. Does it have a marketplace or registry?
4. Does it support MCP? What MCP-related configuration?
5. What validation or linting exists for its formats (if any)?
6. How large/active is the community? (star counts, contributor activity, downloads)

Cast a wide net. The goal is to discover tools and formats skillsaw does not yet know about, not just to check the ones it already supports.

Step 3: Survey competing linters, scaffolding tools, and developer tooling

skillsaw is a linter, scaffolder, and doc generator. Search for anything that overlaps with or competes against these capabilities:

• "AI coding assistant linter"
• "AI agent config validator"
• "cursor rules linter"
• "MCP server linter validator"
• "AI coding assistant scaffolding tool"
• "AI agent plugin generator scaffold"
• "AI rules file generator"
• "dotfiles linter AI assistant"

For each competing tool found, determine:

• What does it lint, validate, or scaffold?
• What formats does it support?
• How mature is it? (GitHub stars, npm/pip downloads, last commit date)
• What does it do that skillsaw does not?
• What does skillsaw do that it does not?
• Is it gaining traction or stalled?

Also look for adjacent developer tooling that could inform skillsaw's roadmap: IDE extensions, CLI tools, CI actions, or registries that serve the same ecosystem.

Step 4: Survey agent protocols and emerging standards

Use WebSearch to discover what protocols and standards are gaining traction:

• "agent communication protocol standard {current year}"
• "agent-to-agent protocol"
• "MCP server registry validation"
• "AI agent interoperability standard"
• "agentic AI configuration format standard {current year}"
• "skill sharing format AI agents"

For each protocol or standard found, determine:

• What is it? Who is behind it?
• How mature is it? (spec draft, stable, widely adopted)
• Does it define file formats or configuration that a linter could validate?
• Is adoption growing or stalled?

Look for convergence trends — are multiple tools adopting the same formats? Are there interoperability initiatives emerging?

Step 5: Identify user pain points and unmet needs

Search for what problems developers are actually hitting and asking for help with. Look at forums, discussions, and issue trackers:

• "AI coding assistant rules not working"
• "cursor rules best practices"
• "claude code plugin problems"
• "MCP server configuration issues"
• "AI agent skill debugging"
• "managing AI coding assistant config across team"

Also search GitHub Issues, Discussions, and Reddit for complaints, workarounds, and feature requests related to AI assistant configuration. Look for patterns:

• What do people struggle with when writing rules or plugins?
• What breaks when teams share or standardize AI assistant config?
• What manual steps do people wish were automated?
• What quality or security concerns do people raise about skills/plugins?
• Are there common "how do I validate my X?" questions with no good answer?

The goal is to find real demand signals — problems people are already having that skillsaw could solve. These are higher-value than features nobody asked for.

Step 6: Assess skillsaw's competitive position

Compare the baseline from Step 1 against findings from Steps 2–5. For each ecosystem tool or format, classify as:

• Already supported — skillsaw validates this today. Note the current rules.
• Partially supported — skillsaw covers some aspects but is missing fields,

features, or format versions.

• Not supported but feasible — a clear, stable format exists that skillsaw

could validate with new rules.

• Not supported and unclear — the format is too new, unstable, or

undocumented for reliable validation.

• No format to validate — the tool has no configuration format a linter could

check.

Step 7: Identify highest-impact opportunities

Rank opportunities by likely impact on open-source adoption. Consider:

• User base size — how many developers use this tool? Rules for tools with

millions of users have more reach than niche tools.

• Format stability — is the format stable enough to write durable rules?

Unstable formats mean maintenance burden.

• Competitive gap — is skillsaw the only linter that could serve this format?

Being first matters.

• Implementation effort — how much work? A new RepositoryType and rule set vs.

a single new rule.

• Cross-format synergy — does supporting this format benefit existing users?

Many repos have both .cursor/rules/ and .claude/rules/.

Produce a ranked list of recommended actions, each with:

| Field | Description | |-------|-------------| | What | The format or capability to add | | Why | The strategic rationale | | Effort | Low / Medium / High | | Impact | Low / Medium / High | | Depends on | Any prerequisites |

Step 8: Check for new primitives and concepts

Beyond format validation, look for new categories of capability:

• Skill testing/evaluation — are there emerging standards for testing skills?
• Skill interoperability — can skills be shared across tools? Is a universal

format emerging?

• Security scanning — beyond MCP allowlisting, are there security concerns in

skill/plugin formats that a linter should catch?

• Dependency management — emerging patterns for skill dependencies, versioning,

or compatibility declarations?

• Agent orchestration config — new formats for multi-agent workflows?
• Quality metrics — download counts, ratings, trust signals for

skills/plugins?

Step 9: Produce the strategic report

Create a GitHub issue using gh issue create with:

• Title: [Ecosystem Scout] Strategic Assessment - {YYYY-MM-DD}
• Labels: ecosystem (create the label if it does not exist)

Use this structure for the issue body:

## Ecosystem Scout Report

**Date**: {date}
**skillsaw version**: {version from pyproject.toml}

### Current Capabilities Summary
{Brief summary of what skillsaw supports today}

### Ecosystem Landscape

#### AI Coding Assistants
| Tool | Config Format(s) | Skills/Plugins? | Marketplace? | MCP? | Community Size | skillsaw Support |
|------|------------------|-----------------|--------------|------|----------------|------------------|
| ... | ... | ... | ... | ... | ... | ... |

#### Competing Linters & Tooling
| Tool | What it does | Formats | Traction | Gap vs skillsaw |
|------|-------------|---------|----------|-----------------|
| ... | ... | ... | ... | ... |

#### Agent Protocols
| Protocol | Status | Relevance to skillsaw |
|----------|--------|----------------------|
| ... | ... | ... |

### User Pain Points & Unmet Needs
{Problems developers are hitting, demand signals from forums/issues/discussions}

### Competitive Assessment
{For each tool/format: detailed status and gap analysis}

### Prioritized Recommendations

#### High Priority
{Ranked list with What / Why / Effort / Impact}

#### Medium Priority
{Ranked list}

#### Low Priority / Watch List
{Items to monitor but not act on yet}

### New Primitives & Concepts
{Emerging patterns that could inform skillsaw's roadmap}

### Raw Research Notes

Detailed findings per tool
{Full notes from each tool surveyed}

---
Generated by the [skillsaw-ecosystem-scout](https://github.com/stbenjam/skillsaw) skill.

Step 10 (Optional): Create tracking issues

Only if the user explicitly requests it (e.g., via --create-issues or by asking in the prompt), create individual GitHub issues for each high-priority recommendation:

• Title: [Ecosystem] {brief description}
• Labels: ecosystem
• Body: The recommendation details from the report, with a link back to the

main assessment issue.

Do not create tracking issues unless explicitly asked.

Important constraints

• This skill produces analysis, not code changes. Never create PRs.
• Use WebFetch for known URLs and WebSearch for discovery. Both are needed.
• Be specific — cite URLs, version numbers, star counts, and dates.
• Do not recommend adding support for formats that are proprietary,

undocumented, or likely to change drastically within months.

• If a web fetch fails (site down, URL changed), note the failure and move on.

Do not block the entire report on one failed fetch.

• Recommendations must be actionable. Bad: "consider supporting more tools."

Good: "Add Cursor rules validation (.cursor/rules/*.mdc with YAML frontmatter) — Cursor has 2M+ users and no existing linter for this format."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: stbenjam
• Source: stbenjam/skillsaw
• License: Apache-2.0
• Homepage: https://skillsaw.org/

Install and usage instructions live in the source repository linked above.