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 intertwine/dspy-agent-skills --skill dspy-advanced-workflowgit clone --depth 1 https://github.com/intertwine/dspy-agent-skillsWrote 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/intertwine/dspy-agent-skills/dspy-advanced-workflow)<a href="https://agentmods.dev/skills/intertwine/dspy-agent-skills/dspy-advanced-workflow"><img src="https://agentmods.dev/badge/skills/intertwine/dspy-agent-skills/dspy-advanced-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/intertwine/dspy-agent-skills/dspy-advanced-workflow"><img src="https://agentmods.dev/badge/skills/intertwine/dspy-agent-skills/dspy-advanced-workflow.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00097 | $0.01662 |
| Opus 5 | $0.00048 | $0.00831 |
| Sonnet 5 | $0.00019 | $0.00332 |
| Haiku 4.5 | $0.00010 | $0.00166 |
Grade A, and why
dspy-advanced-workflow 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 4d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy Advanced Workflow (2026)
This skill runs the seven-step loop that turns a natural-language task description into an optimized, saved, deployable DSPy program. Use the relevant steps in order. Stop at a validated baseline for a prototype; optimizer runs require an appropriate authorized budget and evidence of need. Exporting a local artifact does not authorize deployment.
The seven steps
1. Spec
Rephrase the user's task in one sentence. Identify inputs, outputs, the quality axis that matters, and any constraints (latency, cost, tool access, context size). Pick predictor shape:
| Task shape | Predictor |
|---|---|
| Single-step structured I/O | dspy.Predict / dspy.ChainOfThought |
| Tool use / multi-step | dspy.ReAct |
| Code execution | dspy.ProgramOfThought |
| Long context / codebase | dspy.RLM → dspy-rlm-module |
2. Program
Write the typed dspy.Signature + dspy.Module subclass per dspy-fundamentals. No hard-coded prompts. Keep predictors named so GEPA can target them.
3. Data
Build trainset and separate valset as dspy.Example(...).with_inputs(...). For GEPA, maximize trainset size and keep validation just large enough to represent downstream behavior; held-out testset is reported on at the end only. See dspy-evaluation-harness.
4. Rich metric
Write rich_metric(gold, pred, trace=None, pred_name=None, pred_trace=None) returning dspy.Prediction(score=0..1, feedback="natural-language critique"). The feedback is load-bearing — it's what GEPA's reflection LM learns from. A dict with the same fields crashes dspy.Evaluate; only dspy.Prediction aggregates correctly. See dspy-evaluation-harness.
5. Baseline
evaluator = dspy.Evaluate(devset=valset, metric=rich_metric,
num_threads=8, display_progress=True,
provide_traceback=True,
save_as_json="runs/baseline.json")
baseline = evaluator(program)
print("Baseline:", baseline.score)
What ships with it
2 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.
- 4d ago Changed · +3 tokens per session a8b2523699af
- 11d ago First seen · 150 lines · 94 tokens per session scan A 7b4a3b42ed41
dspy-advanced-workflow is a skill published in the GitHub repository intertwine/dspy-agent-skills (277 stars, last pushed 4d ago), licensed MIT. It adds 97 tokens to every session and 1,662 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.