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 agentmods add skills/evokoa/polygres-skills/polygres-data-pipelinenpx skills add Evokoa/polygres-skills --skill polygres-data-pipelinegit clone --depth 1 https://github.com/Evokoa/polygres-skillsWhat 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 | $0.00183 | $0.02701 |
| Opus 5 | $0.00092 | $0.01350 |
| Sonnet 5 | $0.00037 | $0.00540 |
| Haiku 4.5 | $0.00018 | $0.00270 |
Grade A, and why
polygres-data-pipeline 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 2d 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Polygres Data Pipeline
Turn setup intent into the smallest complete working result. Adapt to what the user actually needs. Do not force every setup through schema creation, embeddings, graph, backfill, continuous capture, retrieval code, or agent instructions.
Use guidance at the right strength
Treat this skill as a decision guide, not a mandatory architecture or ordered checklist. Adapt, reorder, replace, or omit recommended components and numeric defaults when inspected evidence or the user's outcome supports a better design. Numeric defaults are starting points, including sample sizes, result counts, token budgets, recall timing, capture timing, and model rankings.
Keep only safety and correctness boundaries firm: preserve authorization and provenance, keep secrets out of plans and payloads, disclose and approve material effects, use compatible embedding contracts, avoid unsupported public interfaces and invented graph relationships, and test the important selected path before calling it operational.
Recognize setup intent
Activate for broad prompts such as "Help me set up Polygres," contextual prompts such as "look at my conversations and set up Polygres," and detailed technical specifications. Treat examples as illustrations, not required wording.
For a question such as "What can I do with Polygres?", use a personalized recommendation branch. Inspect the accessible current workspace and any uniquely resolved Polygres project with bounded, read-only checks. Look only at the data shape, existing retrieval configuration, and application or agent entry points needed to identify useful opportunities. Then give a short answer that says what was found, leads with the most useful Polygres outcome for this project, and mentions only relevant alternatives. Do not return a generic feature list, create a plan, scaffold files, or mutate anything. End with a direct next step such as:
To proceed, reply: Set up the recommended Polygres pipeline.
Treat that reply or an equivalent acceptance as setup intent. Carry the inspected source, project, outcome, and recommendation into the setup flow without repeating discovery unless the evidence is stale. This acceptance starts setup; it is not mutation approval. Prepare the implementation and show the normal consolidated review before making covered changes.
What ships with it
22 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.
- agents/openai.yaml 315 B
- assets/embedding-models.json 8.6 KB
- assets/python-pipeline/checkpoint_ledger.py 5.0 KB runs code
- assets/python-pipeline/local_embeddings.py 6.2 KB runs code
- references/context-and-retrieval.md 4.0 KB
- references/embedding-model-selection.md 4.3 KB
- references/guided-interview.md 3.9 KB
- references/pipeline-plan-contract.md 4.1 KB
- references/pipeline-runtime.md 7.7 KB
- references/schema-and-graph.md 2.9 KB
- references/security-and-approvals.md 3.9 KB
- references/source-chat-agents.md 5.9 KB
- references/source-databases.md 3.6 KB
- references/source-files-and-apis.md 2.1 KB
- references/synced-projects.md 3.8 KB
- scripts/check_embedding_device.py 13 KB runs code
- scripts/check_env.py 3.9 KB runs code
- scripts/recommend_embedding_models.py 11 KB runs code
- scripts/render_pipeline_review.py 5.0 KB runs code
- scripts/scaffold_pipeline.py 8.8 KB runs code
- scripts/update_agent_instructions.py 3.3 KB runs code
- scripts/validate_pipeline_plan.py 20 KB runs code
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.
- 2d ago First seen · 239 lines · 183 tokens per session scan A 32d69444d57a
polygres-data-pipeline is a skill published in the GitHub repository Evokoa/polygres-skills (10 stars, last pushed 14d ago), licensed Apache-2.0. It adds 183 tokens to every session and 2,701 once invoked, about $0.0009 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.
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