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 desirecore/market --skill discover-agentgit clone --depth 1 https://github.com/desirecore/marketWrote 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/desirecore/market/discover-agent)<a href="https://agentmods.dev/skills/desirecore/market/discover-agent"><img src="https://agentmods.dev/badge/skills/desirecore/market/discover-agent.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.01667 |
| Opus 5 | $0.00024 | $0.00834 |
| Sonnet 5 | $0.00010 | $0.00333 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
discover-agent 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 7d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
discover-agent skill
L0: One-Sentence Summary
Match and recommend the most suitable Agent among registered Agents based on the user's need description.
L1: Overview
Procedural skill: understand the need → retrieve with ManageAgent(action='list') → semantic match scoring → rank and present → guide selection; on no match, hand off to create-agent automatically. Applies when the user doesn't know which Agent to pick, wants to browse available Agents, is a new user getting oriented, or is unhappy with the current Agent and wants an alternative. Its value is what the tool can't give: semantic need matching (not keyword search), candidate ranking and presentation, and the create hand-off on no match. list / get are read-only, approval-free.
L2: Detailed Spec
Flow: need understanding → retrieval → match evaluation → ranking → presentation → guided selection.
Stage 1: Need Understanding
Trigger (any): user says "find me a… / is there a… / who can help me…", describes a task without naming an Agent, "which Agents are there", or the system detects a need that mismatches the current Agent. Extract dimensions from the description: domain (legal/finance/tech/education), task_type (consult/review/analyze/create), keywords (contract/report/code/paper…), urgency (routine/urgent).
Stage 2: Retrieval
ManageAgent(action='list') fetches all registered Agents (returns a compact list with name / id / status / description). list/get are read-only ManageAgent operations and can be called directly; they do not require loading create, update, clone, or delete skills first. Filtering: by default show non-offline Agents; offline ones appear only as a fallback when there's no better candidate; exclude internal system Agents (e.g. DesireCore itself) unless the user explicitly asks.
Stage 3: Match Evaluation
Judge match with LLM semantic understanding (not a formula): relevance of description / persona to the need, association of skills to the task type, domain fit, status availability (online preferred). Presentation tiers: strong match → mark "recommended", partial → "possibly relevant", no clear relation → don't show.
What ships with it
3 files 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.
- 7d ago First seen · 106 lines · 48 tokens per session scan A e22eca251b6d
discover-agent is a skill published in the GitHub repository desirecore/market (2 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 1,667 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
salva
Salva Runtime is a discovery runtime for agents, CLI tools, and services. Use it when you need structured retrieval, route selection, evidence, and persistence.
find-skills
Discover and recommend skills from the registry based on task requirements - search installed skills, suggest matching skills for current task, and browse skill categories.
trueai
Find the right AI SaaS app for any task using the TrueAI catalog (1,600+ curated apps with categories, sub-scores, pricing and real user reviews). Use whenever the user is choosing an AI tool, comparing AI products, looking up an app by name or URL, or wants pricing / features / reviews of a specific AI app.
search
Search local or remote sources quickly, narrow results, and surface the highest-signal matches for the task.
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
ensembl-database
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.