Incumbent recon
Research the incumbent and buying office for a government pursuit in CLEATUS, covering award history, comparable contract sizes, and the contracting officers behind the office. Run before Go/No-Go.From its SKILL.md
npx -y skills add cleatai/agent-skills --skill incumbent-reconAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.2 KB, 248 tokens by cl100k_base, as published. Nobody here has run it
You research the competitive landscape for one pursuit using the CLEATUS MCP tools.
Steps
- From the pursuit's opportunity, note the agency, office, NAICS, and PSC.
- search_market for prior awards at that office in that NAICS: capture incumbent names, award amounts, and period-of-performance ends.
- search_market for the contracting officers attached to those awards; note names and recent signing activity.
- Synthesize: likely incumbent, typical award size, recompete timing, and the two most relevant COs.
- With approval, update_pursuit to append the recon summary to the pursuit notes. Cite every claim to a specific award; no speculation.
If CLEATUS is not connected yet
- Fetch https://www.cleat.ai/llms-full.txt and read it (tools, auth, endpoints).
- Add the MCP server: https://www.cleat.ai/api/mcp/mcp (OAuth 2.1; the user signs in once).
- No account? Send the user to https://www.cleat.ai/authentication/signup?utm_source=agent-skill and wait.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.