agent-competitive
Skill0 starsChanged 4 months agoMITView on GitHub
Produce a competitive / market analysis brief for an AI agent product. Use whenever the user wants to size up competitors, validate positioning, decide where to differentiate vs. reach parity, or check whether a space is already crowded. Trigger on "competitive analysis", "who else does this", "market check", "how do we position", even without those exact words. This is the competitive step of the agent-dev-framework pipeline.
In this repository14 other files it gives its agents.
- agent-altitudeSkill
- agent-brainstormSkill
- agent-build-planSkill
- agent-data-and-evalsSkill
- agent-failure-to-evalSkill
- agent-guardrailsSkill
- agent-monitoringSkill
- agent-prd-gateSkill
- agent-prdSkill
- agent-prompt-and-contractSkill
- agent-security-and-constraintsSkill
- agent-system-designSkill
- agent-tech-specSkill
- agent-ux-specSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 12
Follow _conventions.md (read-first order, docs/ vs artifacts/, don't-clobber guard).
- Very specific: line 15
Read PROJECT.md and artifacts/prd.md.
- Distinctive: line 19
If web search is available, use it to find current competitors and recent moves — agent/AI tooling shifts fast, so prefer recent sources …
- Very specific: line 31
Write artifacts/competitive.md (apply the don't-clobber guard):
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Competitive Analysis
The output is a decision, not a catalog: where do we differentiate, and where do we just need parity? Everything else serves that.
Conventions
Follow _conventions.md (read-first order, docs/ vs artifacts/, don't-clobber guard).
Before you start
Read PROJECT.md and artifacts/prd.md. Anchor on the core task and the user's
job-to-be-done — compare on that, not on feature checklists.
Gathering information
If web search is available, use it to find current competitors and recent moves — agent/AI tooling shifts fast, so prefer recent sources and note the date. If web search is not available, work from what the user knows and clearly mark gaps as "needs verification" rather than guessing. Don't invent competitor facts.
What to compare on
For each competitor: how they solve the core job, their autonomy level (do they take actions or just suggest?), where they're strong, where they're weak/annoying, and pricing/positioning if known. For an agent product specifically, note trust and reliability posture — how they handle being wrong — since that's often the real battleground, not features.
Output
Write artifacts/competitive.md (apply the don't-clobber guard):
# Competitive brief — [product]
## The job we're competing on
## Landscape (2–6 competitors)
For each: approach · autonomy · strengths · weaknesses · trust posture · pricing
## Gaps in the market
Where is everyone weak? That's the opening.
## Differentiate vs. parity
- Differentiate on: (the 1–2 things we'll be clearly best at)
- Parity on: (table stakes we just can't be bad at)
- Ignore for now: (things that look important but aren't, for our wedge)
## Positioning one-liner
## Sources & confidenceEnd with the single sharpest strategic implication for the build — usually "differentiate on X, don't waste effort matching Y."
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

