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 VioletCranberry/coco-search --skill cocosearch-quickstartgit clone --depth 1 https://github.com/VioletCranberry/coco-searchWrote 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/violetcranberry/coco-search/cocosearch-quickstart)<a href="https://agentmods.dev/skills/violetcranberry/coco-search/cocosearch-quickstart"><img src="https://agentmods.dev/badge/skills/violetcranberry/coco-search/cocosearch-quickstart.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.01560 |
| Opus 5 | $0.00019 | $0.00780 |
| Sonnet 5 | $0.00008 | $0.00312 |
| Haiku 4.5 | $0.00004 | $0.00156 |
Grade A, and why
cocosearch-quickstart scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:11434/api/tags | head -c 200 How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CocoSearch Quick Start
Get a project indexed and searchable in under 2 minutes. This skill checks prerequisites, indexes the codebase, and verifies everything works.
Step 1: Check Infrastructure
Before indexing, verify the required services are running. The checks depend on which embedding provider is configured.
First, determine the provider: Check cocosearch.yaml for embedding.provider (or the COCOSEARCH_EMBEDDING_PROVIDER env var). If neither is set, the default is ollama. Also check for embedding.baseUrl (or COCOSEARCH_EMBEDDING_BASE_URL).
All providers need PostgreSQL:
- PostgreSQL (pgvector):
docker ps --filter "name=postgres" --format "{{.Names}} {{.Status}}"- If not running:
docker compose up -d(from the CocoSearch project root)
- If not running:
Then check the embedding provider:
2a. If provider is ollama (default):
curl -s http://localhost:11434/api/tags | head -c 200
- If not running:
docker compose --profile ollama up -dorollama serve - If running but no model:
ollama pull nomic-embed-text - If
baseUrlis set, check that URL instead of localhost:11434
2b. If provider is openai or openrouter:
- Without
baseUrl: VerifyCOCOSEARCH_EMBEDDING_API_KEYis set:
If not set:echo ${COCOSEARCH_EMBEDDING_API_KEY:+"API key is set"}export COCOSEARCH_EMBEDDING_API_KEY=sk-...No Ollama needed. - With
baseUrl: Verify the custom endpoint is reachable:
API key is not required (local OpenAI-compatible servers typically don't need one).curl -s <baseUrl>/v1/models | head -c 200
If PostgreSQL + provider checks pass: Proceed to Step 2.
If nothing is running and user has CocoSearch cloned (Ollama provider):
cd /path/to/cocosearch && docker compose --profile ollama up -d
This starts both PostgreSQL and Ollama in one command.
Step 2: Index the Project
Resolve index name (use the resolved name for all operations):
- Try
cocosearch.yamlforindexNamefield -- if found, use it - If no config file, call
list_indexes()and match the current project's directory name against available indexes. The MCP tools auto-derive index names from directory paths (e.g.,my-project/->my_project), so a match is likely if the repo was indexed without a config file. - If no match found, the project is genuinely not indexed -- proceed to create one below. Do NOT abandon CocoSearch tools just because
cocosearch.yamlis missing.
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.
- 8d ago First seen · 154 lines · 39 tokens per session scan A ddbfb7ef8cec
cocosearch-quickstart is a skill published in the GitHub repository VioletCranberry/coco-search (37 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 1,560 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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