GSD Core is a framework that guides AI coding agents through a repeatable cycle of discussing decisions, planning, executing, verifying, and shipping software work. It is used with coding-agent runtimes to organize research and implementation in fresh-context subagents and reduce context degradation. The catalogue entries are its skills, agents, hooks, plugin, and instructions for those workflows.
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.
git clone --depth 1 https://github.com/open-gsd/gsd-coreWrote 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/agents/open-gsd/gsd-core/gsd-eval-planner.compact)<a href="https://agentmods.dev/agents/open-gsd/gsd-core/gsd-eval-planner.compact"><img src="https://agentmods.dev/badge/agents/open-gsd/gsd-core/gsd-eval-planner.compact/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/agents/open-gsd/gsd-core/gsd-eval-planner.compact"><img src="https://agentmods.dev/badge/agents/open-gsd/gsd-core/gsd-eval-planner.compact.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00072 | $0.01567 |
| Opus 5 | $0.00036 | $0.00783 |
| Sonnet 5 | $0.00014 | $0.00313 |
| Haiku 4.5 | $0.00007 | $0.00157 |
Grade A, and why
gsd-eval-planner 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 today.
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.
This is a copy
88% identical to gsd-eval-planner — 69 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<required_reading>
Read ~/.claude/gsd-core/references/ai-evals.md first — your evaluation framework.
</required_reading>
<required_reading> in prompt → read every listed file first.
<execution_flow>
Always include: safety (user-facing), task completion (agentic).
Format each rubric as:
PASS: {specific acceptable behavior in domain language} FAIL: {specific unacceptable behavior in domain language} Measurement: Code / LLM Judge / Human
Measurement approach: Code-based (schema validation, required-field presence, performance thresholds, regex) / LLM judge (tone, reasoning quality, safety-violation detection — requires calibration) / Human review (edge cases, LLM judge calibration, high-stakes sampling).
Mark each dimension: Critical / High / Medium priority.
Include Phoenix setup in AI-SPEC.md:
# pip install arize-phoenix opentelemetry-sdk
import phoenix as px
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
px.launch_app() # http://localhost:6006
provider = TracerProvider()
trace.set_tracer_provider(provider)
# Instrument: LlamaIndexInstrumentor().instrument() / LangChainInstrumentor().instrument()
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.
- today First seen · 138 lines · 72 tokens per session scan A 91ae9adf045f
gsd-eval-planner is an agent published in the GitHub repository open-gsd/gsd-core (9,319 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 1,567 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to gsd-eval-planner, differing in 69 lines, and is treated as a copy.
Other agents, from other repositories
gsd-eval-auditor
Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by /gsd:eval-review orchestrator.
gsd-eval-planner
Designs a structured evaluation strategy for an AI phase. Identifies critical failure modes, selects eval dimensions with rubrics, recommends tooling, and specifies the reference dataset. Writes the Evaluation Strategy, Guardrails, and Production Monitoring sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase…
tester
Test writing, fixing, and coverage gap identification.
gsd-integration-checker
Verifies cross-phase integration and E2E flows. Checks that phases connect properly and user workflows complete end-to-end.
gsd-ai-researcher
Researches a chosen AI framework's official docs to produce implementation-ready guidance — best practices, syntax, core patterns, and pitfalls distilled for the specific use case. Writes the Framework Quick Reference and Implementation Guidance sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase orchestrator.
gsd-nyquist-auditor
Fills Nyquist validation gaps by generating tests and verifying coverage for phase requirements.