pinecone-io
Registry indexed
cultivar
Drive the cultivar CLI to test whether an agent skill improves behavior — scaffold tasks, run with/without the skill across Claude/Copilot/Gemini (locally or on Modal), grade against a rubric, and read the results.
Overview
Drive the cultivar CLI to test whether an agent skill improves behavior — scaffold tasks, run with/without the skill across Claude/Copilot/Gemini (locally or on Modal), grade against a rubric, and read the results.
Read full documentation
Source documentation, not instructions for this website. Review permissions before running any commands.
cultivar
cultivar is a CLI that measures whether an agent skill actually improves an agent's behavior. For each task it runs the agent with the skill and without it (and optionally with the source docs), then an LLM grader scores each run against a natural-language rubric. Use this skill when the user wants to create, run, or interpret cultivar evals.
The loop
cultivar init <skill>— scaffoldtasks/<skill>.yaml+ aSKILL.mdstub.- Edit the task file (intent + PASS/FAIL criteria) and the skill.
cultivar run -s <skill> -r <runner> --grade— run all variants and grade.cultivar report/cultivar show latest -t <task>— read the outcome.- Iterate on the skill; re-run; compare.
Always confirm the install first with cultivar hello (or cultivar hello --no-grade
when no ANTHROPIC_API_KEY is available) — it runs a packaged smoke task end-to-end.
Commands
cultivar init <skill> [--skills-dir DIR]— scaffold task YAML + SKILL.md stub.cultivar run -s <skill> -r <claude|copilot|gemini>— run. Key flags:-t <task>one task ·-v <with-skill|without-skill|with-docs>one variant--remoterun in isolated Modal sandboxes ·-n Nrepeat ·-p Nparallelism--gradegrade after running ·--title NAMElabel the run ·--dry-runprint the prompt + command without calling anything ·--timeout Sper-call budget (default 90)
cultivar grade <run|latest> -s <skill> [--report]— (re)grade an existing run.--modelpicks the grading model (any current Claude model works, including the "-5" generation) ·--max-tokensraises the per-reply budget if evidence/reasoning truncate.cultivar report [run]— summary table across runners/variants.cultivar show <run|latest> -t <task> [--grader|--conversation-only|--workdir]— inspect one run.
--dry-run is the safe way to preview exactly what will be sent before spending tokens.
Variants (the controls)
- with-skill — skill loaded; prompt prefixed
Use the /<skill>. - without-skill — no skill; identical otherwise. The baseline.
- with-docs — no skill, but the task's
ground_truth.context_refsfiles are prepended. Only runs for tasks that declarecontext_refs.
Read two deltas: with-skill vs without-skill ("does the skill do anything?") and with-skill vs with-docs ("is the distilled skill better than dumping the raw docs?").
Tasks
tasks/<skill>.yaml holds one or more tasks. Each task:
tasks:
- id: a-short-id
intent: "what you'd ask the agent to do"
category: cli # or: code-gen
# setup / teardown / verify: optional shell hooks
# env: ["SOME_KEY"] # required env vars, checked upfront
ground_truth:
criteria: |
PASS requires <2-3 concrete, checkable things>.
FAIL if <a common failure mode>.
# context_refs: [docs/ref.md] # activates the with-docs variant
Guidance:
- For code-gen tasks, the intent must say "write a file … in the current directory." Anything the agent writes to its cwd is captured and shown to the grader. A code-gen task that produces no file auto-fails.
- Write criteria as crisp PASS conditions + at least one concrete FAIL mode — vague criteria produce vague grades.
Where skills live
cultivar tests exactly one skill per run (the -s one). It resolves the skills root
as: --skills-dir flag → CULTIVAR_SKILLS_DIR env → ./.claude/skills. Keep
skills-under-test outside .claude/ (e.g. ./skills, via CULTIVAR_SKILLS_DIR=skills)
if you don't want your interactive coding agent to auto-load them.
Local vs remote
- Local (default) — uses the runner CLI installed on your machine + its auth.
--remote— each (task, variant, repeat) runs in its own Modal sandbox: clean isolation, parallelism, reproducibility. Requires a Modal account (modal token new) and a secret holding the agent'sANTHROPIC_API_KEY(default secret nameeval-sandbox-secrets; override withCULTIVAR_MODAL_SECRET). Prefer--remotefor rigorous comparisons. The grader always runs locally and needsANTHROPIC_API_KEY.
Reading results
results/<timestamp>[__title]/ holds per-run .json (stats), .md (readable trace),
.jsonl (raw events), and .workdir/ (files the agent wrote). grades.json holds the
verdicts. Use cultivar report for the table and cultivar show … --grader for the
grader's reasoning + suggestions on a failure.
Gotchas
- Grading needs
ANTHROPIC_API_KEY(loaded from a.envin the cwd).hello --no-gradeandrun --dry-runneed no key. - A grader call that fails is recorded as a FAIL for that conversation and the run continues. Auth and permission errors abort the whole grading run immediately.
tasks/,examples/, andresults/are cwd-relative and user-owned.- One run is a sample. Use
-n 3(or more) for anything you'll act on.
File metadata
name: cultivar description: Drive the cultivar CLI to test whether an agent skill improves behavior — scaffold tasks, run with/without the skill across Claude/Copilot/Gemini (locally or on Modal), grade against a rubric, and read the results.
View original text
---
name: cultivar
description: Drive the cultivar CLI to test whether an agent skill improves behavior — scaffold tasks, run with/without the skill across Claude/Copilot/Gemini (locally or on Modal), grade against a rubric, and read the results.
---
# cultivar
cultivar is a CLI that measures whether an agent **skill** actually improves an agent's
behavior. For each task it runs the agent **with the skill** and **without** it (and
optionally **with the source docs**), then an LLM grader scores each run against a
natural-language rubric. Use this skill when the user wants to create, run, or interpret
cultivar evals.
## The loop
1. `cultivar init <skill>` — scaffold `tasks/<skill>.yaml` + a `SKILL.md` stub.
2. Edit the task file (intent + PASS/FAIL criteria) and the skill.
3. `cultivar run -s <skill> -r <runner> --grade` — run all variants and grade.
4. `cultivar report` / `cultivar show latest -t <task>` — read the outcome.
5. Iterate on the skill; re-run; compare.
Always confirm the install first with `cultivar hello` (or `cultivar hello --no-grade`
when no `ANTHROPIC_API_KEY` is available) — it runs a packaged smoke task end-to-end.
## Commands
- `cultivar init <skill> [--skills-dir DIR]` — scaffold task YAML + SKILL.md stub.
- `cultivar run -s <skill> -r <claude|copilot|gemini>` — run. Key flags:
- `-t <task>` one task · `-v <with-skill|without-skill|with-docs>` one variant
- `--remote` run in isolated Modal sandboxes · `-n N` repeat · `-p N` parallelism
- `--grade` grade after running · `--title NAME` label the run · `--dry-run` print the
prompt + command without calling anything · `--timeout S` per-call budget (default 90)
- `cultivar grade <run|latest> -s <skill> [--report]` — (re)grade an existing run.
`--model` picks the grading model (any current Claude model works, including the "-5"
generation) · `--max-tokens` raises the per-reply budget if evidence/reasoning truncate.
- `cultivar report [run]` — summary table across runners/variants.
- `cultivar show <run|latest> -t <task> [--grader|--conversation-only|--workdir]` — inspect one run.
`--dry-run` is the safe way to preview exactly what will be sent before spending tokens.
## Variants (the controls)
- **with-skill** — skill loaded; prompt prefixed `Use the /<skill>`.
- **without-skill** — no skill; identical otherwise. The baseline.
- **with-docs** — no skill, but the task's `ground_truth.context_refs` files are prepended.
Only runs for tasks that declare `context_refs`.
Read two deltas: with-skill vs without-skill ("does the skill do anything?") and
with-skill vs with-docs ("is the distilled skill better than dumping the raw docs?").
## Tasks
`tasks/<skill>.yaml` holds one or more tasks. Each task:
```yaml
tasks:
- id: a-short-id
intent: "what you'd ask the agent to do"
category: cli # or: code-gen
# setup / teardown / verify: optional shell hooks
# env: ["SOME_KEY"] # required env vars, checked upfront
ground_truth:
criteria: |
PASS requires <2-3 concrete, checkable things>.
FAIL if <a common failure mode>.
# context_refs: [docs/ref.md] # activates the with-docs variant
```
Guidance:
- For **code-gen** tasks, the intent must say "write a file … in the current directory."
Anything the agent writes to its cwd is captured and shown to the grader. A code-gen
task that produces no file auto-fails.
- Write criteria as crisp PASS conditions + at least one concrete FAIL mode — vague
criteria produce vague grades.
## Where skills live
cultivar tests exactly **one** skill per run (the `-s` one). It resolves the skills root
as: `--skills-dir` flag → `CULTIVAR_SKILLS_DIR` env → `./.claude/skills`. Keep
skills-under-test outside `.claude/` (e.g. `./skills`, via `CULTIVAR_SKILLS_DIR=skills`)
if you don't want your interactive coding agent to auto-load them.
## Local vs remote
- **Local** (default) — uses the runner CLI installed on your machine + its auth.
- **`--remote`** — each (task, variant, repeat) runs in its own Modal sandbox: clean
isolation, parallelism, reproducibility. Requires a Modal account (`modal token new`)
and a secret holding the agent's `ANTHROPIC_API_KEY` (default secret name
`eval-sandbox-secrets`; override with `CULTIVAR_MODAL_SECRET`). Prefer `--remote` for
rigorous comparisons. The grader always runs locally and needs `ANTHROPIC_API_KEY`.
## Reading results
`results/<timestamp>[__title]/` holds per-run `.json` (stats), `.md` (readable trace),
`.jsonl` (raw events), and `.workdir/` (files the agent wrote). `grades.json` holds the
verdicts. Use `cultivar report` for the table and `cultivar show … --grader` for the
grader's reasoning + suggestions on a failure.
## Gotchas
- Grading needs `ANTHROPIC_API_KEY` (loaded from a `.env` in the cwd). `hello --no-grade`
and `run --dry-run` need no key.
- A grader call that fails is recorded as a FAIL for that conversation and the run
continues. Auth and permission errors abort the whole grading run immediately.
- `tasks/`, `examples/`, and `results/` are cwd-relative and user-owned.
- One run is a sample. Use `-n 3` (or more) for anything you'll act on.
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- MIT
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 37 GitHub stars
- Stars/forks activity: 37 stars, 2 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- pinecone-io/cultivar
- License
- MIT
- Version
- Unknown
- Last GitHub push
- Sep 8, 2026
- Registry updated
- Sep 10, 2026
- Instruction path
- skills/cultivar/SKILL.md @ 5bd78f30cdf8
Version reported in registry metadata; check source releases before relying on it.
Quality
54/100
Needs review
Trust
61/100
Sandbox only
Audit
70/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 37 GitHub stars
- Stars/forks activity: 37 stars, 2 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"endpoints": {
"web": "https://www.openagentskill.com/skills/pinecone-io-cultivar",
"api": "https://www.openagentskill.com/api/agent/skills/pinecone-io-cultivar",
"audit": "https://www.openagentskill.com/skills/pinecone-io-cultivar/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=pinecone-io-cultivar&task=Use%20cultivar%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20cultivar%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20cultivar%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/pinecone-io-cultivar/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/pinecone-io-cultivar"
}
}For the creator
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- pinecone-io
- Source
- pinecone-io/cultivar
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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This Registry indexed listing is attributed to pinecone-io but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
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[](https://www.openagentskill.com/skills/pinecone-io-cultivar?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/pinecone-io-cultivar/audit)
[](https://www.openagentskill.com/skills/pinecone-io-cultivar?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Community signal
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