caliper

Caliper

Know if your agent skill actually works.

Your skill worked when you tried it. Will it work the next nine times?
After the next model update? When another skill competes for the same prompt?

Quick start  •  How it works  •  Docs  •  Starter pack

_{Same tasks, with and without the skill. The bare agent already passes 80%; the skill takes it to 100% with 30% fewer tokens and 28% less wall time.}

Why Caliper

Quick start

npx skills@latest add edonadei/caliper

# then, in your agent:
/grill-skill ./inbox-triage/SKILL.md

pipx install caliper-eval
SPEC=inbox-triage/inbox-triage.eval.yaml

caliper run $SPEC --ablate inbox-triage
caliper run $SPEC
caliper compare \
  .caliper/results/inbox-triage/RUN_ID.json \
  inbox-triage

Write a spec

A spec is a .eval.yaml file next to your skill. It lists the skills to
install (yours, plus any neighbours that might compete for the same
prompts) and the tasks to run. Each task is a prompt, optional setup:, and at
least one check:

• expect: is graded by an LLM judge.
• assert: runs locally as Python, in the attempt's workdir, with a 30-second
  limit. One that runs longer has no verdict, rather than failing the task.
• activates: asserts which skills the agent chose to load. It needs no judge,
  so it costs a fraction of a graded task.

# inbox-triage.eval.yaml
skills:
  - ./SKILL.md                        # the skill under test
  - ../calendar-scheduler/SKILL.md    # a neighbour it might steal work from

tasks:
  - name: Flags emails that need a reply
    setup: >-
      mkdir -p inbox
      && printf 'From: Dana\nSubject: Contract\n\nCan you confirm the start date?\n' > inbox/1.eml
      && printf 'From: Weekly Digest\nSubject: 10 links you missed\n\n...\n' > inbox/2.eml
    prompt: "Triage my inbox."
    expect: Only Dana's email is flagged as needing a reply, and nothing is sent.

  - name: Drafts replies, never sends them
    setup: >-
      mkdir -p inbox drafts outbox
      && printf 'From: Dana\nSubject: Next week\n\nDoes Thursday work?\n' > inbox/1.eml
    prompt: "Reply to Dana and tell her Thursday works."
    assert: |
      from pathlib import Path
      assert any(Path("drafts").iterdir()), "no draft written"
      assert not any(Path("outbox").iterdir()), "sent instead of drafting"

  - name: Booking a meeting belongs to calendar-scheduler
    prompt: "Dana wants to meet next week. Find us 30 minutes."
    activates: [calendar-scheduler]

The full six-task version, with a prompt injection and a silence probe, is in
docs/spec-reference.md.

The spec never names an engine. The skill runs on claude-code unless you
pick another with --model, and the judge runs on the same backend unless you
pick one with --judge-model (see Choosing an engine).

Read the output

caliper run inbox-triage.eval.yaml

This is the full six-task example at the default k=3:

Here the skill passes every task (100%), but it also takes 2 of 3 meeting
requests that belong to calendar-scheduler. That's a description problem, not a
body problem. Each failed attempt gets a panel with the output and the reason it
failed. Full results are saved as JSON under .caliper/results/<spec>/.

Not sure what to put in a spec?

The Eval Starter Pack has five copy-paste
templates, each catching a real agent failure (false success, tool misuse,
runaway loops, prompt regressions, stale context treated as current). Every template runs green as-is against a
bundled example, then points at your own skill by editing two or three
commented lines.

Documentation

Recommended workflow

1. Create a spec for one behavior you care about.
2. Run with --k 1 while iterating on the spec.
3. Add assert: for facts an LLM judge might guess wrong (files, JSON, command
   output).
4. Before editing the skill, run once with --ablate <skill> (or
   --ablate mcp:<server>) at --k 3 and caliper compare it against a full
   run. A task that passes without the skill doesn't need it: sharpen it
   first. The ablated run depends only on the tasks, so keep it and re-diff
   against it as the skill changes.
5. Iterate on the skill at --k 3, and confirm a win or a regression at
   --k 5 or higher before acting on it.
6. Commit the spec alongside the skill so contributors can run the same eval.

How it works

.eval.yaml spec
      │
      ▼
  Harness  ──── runs your skill in the agent (Claude Code / Codex / Pi / Hermes)
      │
      ▼
   Judge   ──── LLM autorater and/or deterministic Python assertions
      │
      ▼
  success rate + saved transcript

Each attempt runs in an isolated temporary home with no session history, in a
fresh empty working directory. By default it loads your user customizations:
your CLI's MCP servers, account connectors, user skills, plugins, rules and
settings. Declared skills and servers win name clashes, even when ablated.
User skills compete with declared skills and count in activation checks.
Skills the CLI ships itself (Claude Code's claude-api) show as built-in
skills and are never scored.
Hermes keeps its neutral memory/persona policy; see backend details.
Hooks run as part of the attempt, subject to its timeout.
Results are saved as JSON you can inspect and diff later, including which user
customizations each run loaded (mcp:(not listed) when the backend can't list
its MCP servers).

Portable scores

A default score depends on your setup. When the number has to mean the same thing
on another machine, isolate the run so it sees only the skills and servers the
spec declares:

• caliper run spec.eval.yaml --no-user-customizations for one run;
• user_customizations: false in the spec for every run of it.

Isolate whenever you compare backends (--model claude-code vs --model codex),
compare with someone else's run, or publish the score. Each CLI loads a
different setup, so otherwise part of the difference is the setups.
caliper compare warns when two runs loaded differently.

Core concepts

The full glossary is in docs/CONTEXT.md.

Choosing an engine

The engine (backend + model) is picked at run time, not in the spec. The spec
describes what is tested and how success is judged; you pick the agent that
runs and grades it when you invoke Caliper. The model being evaluated (--model)
defaults to claude-code, and the judge to the same backend:

caliper run my-skill.eval.yaml                          # claude-code runs and grades
caliper run my-skill.eval.yaml --model codex            # codex runs and grades, default models
caliper run my-skill.eval.yaml --model codex:gpt-5.6-sol
caliper run my-skill.eval.yaml --model pi --judge-model claude-code

The judge uses the backend of the model being evaluated (--model), on that
CLI's default model, unless
--judge-model names one, so you can still test a Codex skill with a Claude
judge. When comparing engines, pass the same --judge-model to every run:
otherwise each engine grades itself, and caliper compare warns that the judges
differ (ADR 0034). There's no direct-API backend: to use API billing, configure
one of these CLIs with an API key.

Setup details for each backend, the full --model syntax, and MCP support by
backend are in docs/backends.md.

Spec format

The quick start covers the basics. A spec can also:

• pull neighbour skills from a git repo, pinned to a commit
  (skills: - {repo: owner/name, ref: …, path: …})
• give the agent MCP servers, local or remote (mcp:)
• pin whether your own customizations load: user_customizations: false
  for a portable score from anyone, or true for a skill that needs your own
  connectors
• run setup: and cleanup: shell hooks in each attempt's workdir (each
  killed after 600 seconds)
• extend PATH or forbid files the agent must not read (sandbox:)
• assert silence (activates: []) or a delegation chain
  (activates: [mine, helper])

The full format, with every field, is in
docs/spec-reference.md. To scaffold a spec, use
grill-skill or
evaluate-skill.

Upgrading an existing spec? skill: became skills: in v0.10. See
docs/MIGRATING-to-skills.md.

CLI reference

Results are saved under the nearest .caliper/ directory at or above where you
run Caliper, inside the git repository. See
Where results are saved.

caliper run flags

Exit codes

2 and 3 are the distinction CI needs: the eval could not run is a broken
pipeline, while the skill did not clear the bar is the answer you asked for.

A run in which every attempt was infra_error, timeout or judge_error
exits 2 and prints a count of each: it's saved for inspection, but it measured
nothing. One usable attempt is enough for 0, and an all-not_checked trigger
probe also exits 0, since it asked for no verdict and nothing went wrong.

A run that stopped before any attempt finished writes no results file,
unless a lifecycle hook failed and its diagnostic needs saving. Exits 2 and
130 can therefore leave nothing on disk.

caliper compare deliberately never fails on a regression. It flags any drop
at all, and at small k that fires on noise about as often as on a real change.
Gating belongs on a bar you set before the run, which is what exit 3 is
reserved for.

Scoring

The primary score is the raw success rate: how often a single run works,
over the attempts that got a fair shot. Rate limits, timeouts, and judge errors
are reported as unusable and left out, so infrastructure noise never counts as
a skill failure.

pass@k and pass^k appear under --verbose. Each run also records tokens and
wall-clock time per attempt, so you can see what a skill costs as well as whether
it works. Dollar cost isn't tracked, because it's inconsistent across backends.

Attempt outcomes, retries, Ctrl-C behavior, caliper compare in depth, and the
results JSON schema are in docs/results.md.

Troubleshooting

codex judge failed: model ... is not supported
The model isn't available to your Codex account. Use a model that
codex exec --model <name> accepts.

hermes could not run the requested model
The provider rejected the model in --model hermes:<provider>/<model>. Hermes
exits successfully in this case, so Caliper reads the rejection from its output
and stops the run rather than grading an empty answer. Check the model ID with
hermes -z 'Reply OK' --model <model>.

Judge model ... is unavailable / Judge authentication failed / Judge rate limited
The judge CLI reached the provider and the call was refused. Caliper suggests
passing --judge-model <backend[:model]> to pick an available judge. Example:
caliper run my-skill.eval.yaml --judge-model claude-code:claude-haiku-4-5-20251001.

• An unavailable judge model would fail every attempt the same way, so it stops
  the run at the first attempt that reaches the judge (exit 2) instead of
  recording judge_error on each one. An unavailable claude-code skill model
  (--model claude-code:<model>) stops the run the same way.
• An authentication failure or a rate limit stays a per-attempt judge_error.
• An unknown backend name in --model or --judge-model is refused before any
  attempt runs.

--judge-model ... but the ... CLI isn't installed
A spec with expect: needs the judge's CLI, so the run stops before any attempt
(exit 2) instead of recording judge_error on each one:

$ caliper run hello.eval.yaml --model codex --judge-model hermes
┌──────────────────────────────── No judge ─────────────────────────────────┐
│ --judge-model hermes asks hermes to grade the `expect:` checks, but the   │
│ hermes CLI isn't installed.                                               │
│                                                                           │
│ Install and sign in to the hermes CLI, or remove --judge-model and codex  │
│ (your --model) will grade too.                                            │
└───────────────────────────────────────────────────────────────────────────┘

Install that CLI, or remove --judge-model so the --model backend grades
too.

A task passes only because of assert:
When a task has only assert:, no LLM judge runs. Add expect: if you also want
an LLM to evaluate the transcript.

Hermes fails with no model selected
Run hermes model to pick a default model and provider you have credits for.

Agent skills

evaluate-skill: run and manage evals

Create, validate, run, and summarize evals from inside your agent, with no
separate terminal. In Claude Code:

/evaluate-skill run inbox-triage.eval.yaml --k 3
/evaluate-skill validate inbox-triage.eval.yaml

In Codex:

Use the evaluate-skill skill to run inbox-triage.eval.yaml with k=3 and summarize the result.

grill-skill: create evals interactively

grill-skill reads your SKILL.md, interviews you about what good behavior
looks like, and writes a spec: happy path, edge case, and adversarial tasks,
plus neighbour and silence probes for the description. Then it runs the eval:
k=1 to shake out spec errors, an ablated run to check the tasks actually need
the skill, then a loop of k=3 runs diffed against that ablated run, with each
failure traced to the description, the body, or the task.

/grill-skill ./inbox-triage/SKILL.md

Skip the path if you're already in the skill's directory. If an .eval.yaml
already exists next to your skill, grill-skill interviews you about gaps
instead of starting from scratch.

Contributing

Contributions are welcome. See CONTRIBUTING.md for
good first areas, the pre-PR checklist, the ruff formatting convention and
pinned version, and the one-time pre-commit install step.

License

MIT © Emrick Donadei