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/ai-is-gonna/get-tasks-doneWrote 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/ai-is-gonna/get-tasks-done/gtd-eval-planner)<a href="https://agentmods.dev/agents/ai-is-gonna/get-tasks-done/gtd-eval-planner"><img src="https://agentmods.dev/badge/agents/ai-is-gonna/get-tasks-done/gtd-eval-planner/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/ai-is-gonna/get-tasks-done/gtd-eval-planner"><img src="https://agentmods.dev/badge/agents/ai-is-gonna/get-tasks-done/gtd-eval-planner.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 | $0.01678 |
| Opus 5 | $0.00036 | $0.00839 |
| Sonnet 5 | $0.00014 | $0.00336 |
| Haiku 4.5 | $0.00007 | $0.00168 |
Grade A, and why
gtd-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 11d 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.
This is a copy
91% identical to gsd-eval-planner — 10 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<required_reading>
Read ~/.claude/get-tasks-done/references/ai-evals.md before planning. This is your evaluation framework.
</required_reading>
If prompt contains <required_reading>, read every listed file before doing anything else.
<execution_flow>
Always include: safety (user-facing) and 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
Assign measurement approach per dimension:
- Code-based: schema validation, required field presence, performance thresholds, regex checks
- LLM judge: tone, reasoning quality, safety violation detection — requires calibration
- Human review: edge cases, LLM judge calibration, high-stakes sampling
Mark each dimension with priority: Critical / High / Medium.
If detected: use it as the tracing default.
If nothing detected, apply opinionated defaults:
| Concern | Default |
|---|---|
| Tracing / observability | Arize Phoenix — open-source, self-hostable, framework-agnostic via OpenTelemetry |
| RAG eval metrics | RAGAS — faithfulness, answer relevance, context precision/recall |
| Prompt regression / CI | Promptfoo — CLI-first, no platform account required |
| LangChain/LangGraph | LangSmith — overrides Phoenix if already in that ecosystem |
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.
- 11d ago First seen · 155 lines · 72 tokens per session scan A 8c6c2727e836
gtd-eval-planner is an agent published in the GitHub repository ai-is-gonna/get-tasks-done (9 stars, last pushed 3mo ago), licensed MIT. It adds 72 tokens to every session and 1,678 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to gsd-eval-planner, differing in 10 lines, and is treated as a copy.
Other agents, from other repositories
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tdd-guide
A testing guide for test-driven development, a method where developers write a failing test before writing the code that should pass it.
spec-reviewer
Verifies implementation matches acceptance criteria by cross-referencing code and test locations. Validates story format and Definition of Ready compliance. Simple PASS/FAIL classification per criterion.
test-writer
Generates comprehensive tests for code. Reads existing test patterns in the project and matches the style (Jest, Vitest, pytest, etc.). Covers happy paths, edge cases, error cases, and boundary conditions.
integration-tester
Independent dynamic verification agent. Runs the test suite, executes acceptance criteria commands, and verifies runtime behavior. Breaks the self-assessment cycle where the implementer verifies their own work. Reports with command output evidence.
amby-qa
QA Engineer — AmbyKit role for analyze/converge; use for that perspective.