Caliper: Know if your agent skill actually works

Caliper is a lightweight evaluation harness for agent skills. Write a short spec of what "good" looks like, run it, and get a success rate you can track. Works with the agent you already use: Claude Code, Codex, Pi, or Hermes. Caliper installs the skill where the agent looks for skills and lets the agent choose.

Teach your agent to evaluate:

npx skills@latest add edonadei/caliper

Or run it yourself:

# Run the evaluation.
caliper run commit-commands.eval.yaml --k 3

# The control subject: your skill is not there.
caliper run commit-commands.eval.yaml --k 3 --ablate commit-commands

# Compare the runs. Did your skill improve it?
caliper compare .caliper/results/commit-commands/<evaluation-run>.json .caliper/results/commit-commands/<ablated-run>.json

You write a spec, a YAML file describing what "working" means. Either hand-write it or have /grill-skill generate it for you. --ablate runs the same tasks with that skill removed, and caliper compare diffs the two runs task by task:

---

Agent skills are hard to test. A skill that works on your machine, on this prompt, today, might fail tomorrow after a model update or a one-line prompt edit. Caliper makes reliability measurable: define what success looks like, run the skill repeatedly, and get a success rate you can track over time.

Use Caliper to answer questions like:

• Is my agent still working the same with this new model?
• Did my prompt edit improved the skill?
• Does my skill fire when it should, and stay quiet when it needs to not trigger?
• Is the skill worth the context? Or would the base agent pass without it?
• Does it still pass the workflows it passed last week?
• Which agent (Claude Code, Codex, Pi, or Hermes) runs this skill more reliably?

---

Quick start

Path A: Agentic (let your agent drive)

1. Install the skills

npx skills@latest add edonadei/caliper

2. Generate a spec interactively

In your agent (Claude Code or Codex):

/grill-skill ./my-skill/SKILL.md

grill-skill reads your SKILL.md, interviews you, and writes a 3-task .eval.yaml (happy path, edge case, adversarial).

3. Run and measure

/evaluate-skill run my-skill.eval.yaml --k 3

Browse past runs:

/evaluate-skill list
/evaluate-skill report my-skill

Path B: CLI (run it yourself)

1. Install the CLI

pipx install caliper-eval   # requires Python 3.10+

2. Write a spec

# commit-writer.eval.yaml
skills:
  - ./SKILL.md                     # the skill under test
  - ../changelog-writer/SKILL.md   # a neighbour it might steal work from

tasks:
  # Autorater: the LLM judge reads the transcript and decides
  - name: Writes a conventional commit message
    prompt: "Summarize the staged git diff as a commit message."
    expect: >
      The response is a conventional-commit message: a concise subject
      line under 72 characters, followed by a body explaining why the
      change was made, not just what changed.
    activates: [commit-writer]

  # Script execution: a deterministic Python assertion
  - name: Keeps the subject line under 72 characters
    prompt: "Commit the staged changes."
    assert: |
      import subprocess
      subject = subprocess.run(
          ["git", "log", "-1", "--pretty=%s"], capture_output=True, text=True
      ).stdout.strip()
      assert len(subject) <= 72, f"subject line is {len(subject)} chars"
    activates: [commit-writer]

  # Activation: this prompt belongs to the neighbour, not to you
  - name: A release summary belongs to changelog-writer
    prompt: "What changed since v2.1? I need it for the release notes."
    activates: [changelog-writer]

Three kinds of check, and a task needs at least one. expect: is graded by the judge LLM; assert: runs locally as Python; activates: asserts which skills the agent chose to load. Use any combination.

The third task is the one you cannot write any other way. Both skills read git history, so a release-notes request is exactly where commit-writer might grab work that belongs to changelog-writer. Declaring the neighbour and asserting activates: [changelog-writer] is how you find out. A task like that needs no expect: at all: it skips the judge, so it costs a fraction of a graded task.

Caliper never pastes your skill into the prompt. It installs it where the agent looks for skills and lets the agent decide, so a run measures the description (does it fire?) and the body (does it work?) together, and activates: is what tells the two apart.

The spec never names an engine. The skill and judge default to claude-code, and you pick a different agent/model at run time with --model / --judge-model (see Choosing an engine).

3. Run it

caliper run my-skill.eval.yaml --k 3          # --ablate <skill> for a run to diff against

4. Read the output

The report ends with the per-task failure panels: for each attempt that didn't pass, the output plus the assertion or autorater reason why. Full results are also saved as JSON under .caliper/results/<spec>/ for you to inspect or caliper compare later. --verbose adds pass@k and pass^k columns (both derived from the raw rate) and a panel for every task.

Not sure what to put in a spec?

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

---

How it works

.eval.yaml spec
      │
      ▼
  Harness  ──── runs your skill against 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. Results are saved as JSON you can inspect and diff later.

---

Agent skills

The repo ships two agent skills. Install both with:

npx skills@latest add edonadei/caliper

evaluate-skill: run and manage evals

Create, validate, run, and summarize evals from inside your normal workflow, with no separate terminal needed. The skill installs Caliper automatically if it's missing.

Then use it in Claude Code:

/evaluate-skill run my-skill.eval.yaml --k 3
/evaluate-skill validate my-skill.eval.yaml

Or in Codex:

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

grill-skill: create evals interactively

Don't have evals yet? grill-skill guides you through creating them. It reads your SKILL.md, interviews you about what good behavior looks like, and generates a 3-task spec (happy path, edge case, adversarial). Then it runs the eval and loops: k=1 to validate, k=3 to measure, an ablated run to diff against before you commit.

/grill-skill ./my-skill/SKILL.md

No path needed if you're already in the skill's directory:

/grill-skill

If an .eval.yaml already exists next to your skill, grill-skill reads the existing tasks and interviews you about gaps instead of starting from scratch.

---

Core concepts

Term	What it is
Spec	A .eval.yaml file that describes the skills, judge, and tasks to run
Backend	The CLI agent that executes the skill (claude-code, codex, pi, hermes)
Judge	What decides pass/fail: an LLM reading the transcript (expect:), Python assertions (assert:), or both
success rate	The primary score: run k times, measure how often a single run works (pass@k/pass^k are secondary views, under --verbose)
Neighbourhood	The set of skills a spec declares (skills:). All installed, none preloaded, and all assertable. This is the competition your description has to win
Activation	The agent choosing to load a skill. Asserted with activates: and scored on its own scoreboard, separate from the success rate
Ablation	Re-run the same tasks with a declared skill removed (--ablate), to prove the skill is doing the work. Name every skill for the bare agent. It's a property of the tasks, so run it once and keep re-diffing against it
Attempt	One isolated run of a single task (fresh temporary home, no session history)

---

Choosing an engine

The engine (backend + model) is a runtime axis, not a spec field. The spec describes what is tested and how success is judged, and you pick the agent that runs and grades it at invocation. Both default to claude-code; select a different one with --model / --judge-model:

caliper run my-skill.eval.yaml                          # claude-code (default)
caliper run my-skill.eval.yaml --model codex            # codex, its default model
caliper run my-skill.eval.yaml --model codex:gpt-5.6-sol
caliper run my-skill.eval.yaml --model pi --judge-model claude-code

Backend	Requires	Best for
claude-code	Claude Code CLI installed and authenticated	Testing Claude Code slash-command skills
codex	Codex CLI installed (npm install -g @openai/codex)	Testing Codex skills
pi	pi CLI installed (npm install -g @earendil-works/pi-coding-agent) and authenticated	Testing pi skills (agentskills.io)
hermes	Hermes Agent CLI installed and authenticated (Nous Research)	Testing skills on Hermes; hermes:<provider>/<model> selects the model

Caliper runs skills only through CLI agents, so every backend can actually load and run a skill. There is no direct-API backend: to run against API-priced billing, configure one of these CLIs with an API key (e.g. ANTHROPIC_API_KEY / OPENAI_API_KEY) rather than selecting a separate backend.

The skill engine and judge engine are independent: you can test a Codex skill with a Claude judge, or any other combination, by pairing --model with --judge-model.

Claude Code setup

Install and authenticate the claude CLI. --model claude-code uses your existing Claude Code auth, with no extra configuration needed.

Codex setup

npm install -g @openai/codex
codex login

--model codex calls codex exec. If the Codex desktop app is installed, Caliper prefers the app-bundled binary over codex on PATH. Set CODEX_CLI_PATH to force a specific binary.

pi setup

npm install -g @earendil-works/pi-coding-agent
pi   # then authenticate (e.g. /login for a subscription provider, or set the provider API key)

--model pi runs pi --print --mode json and installs the declared skills under its agent dir, where pi discovers them (its --skill flag preloads, which caliper never does; pi's own --no-skills exists because discovery is the default). It reuses your ~/.pi/agent auth and settings; the :model half of --model pi:<model> overrides pi's configured default when set. Set PI_CLI_PATH to force a specific binary. Note: pi's built-in default provider is google, so running --model pi with no model relies on your pi config to resolve a provider you are authenticated for.

Hermes setup

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes login   # authenticate
hermes model   # pick a default model/provider you have credits for

Hermes is a stateful, always-on agent (persistent memory, a persona, auto-generated skills), so Caliper normalizes it to a neutral agent to keep its score apples-to-apples with the other backends: every attempt runs in an isolated HERMES_HOME seeded with your ~/.hermes auth/config only (never SOUL.md/MEMORY.md), with --ignore-rules and --yolo (so an approval prompt can't hang the non-interactive oneshot), and only the spec's declared skills are installed (its --skills flag is documented as preload, so caliper does not pass it). --model hermes runs hermes -z (oneshot) then hermes sessions export to recover the full tool-call trajectory; --model hermes:<provider>/<model> (e.g. hermes:anthropic/claude-opus-4-8) selects the model, otherwise your ~/.hermes/config.yaml default is used. Point it at a provider you have credits for. If a run fails because no model is selected or a provider login lapsed, Caliper tells you to run hermes model. Set HERMES_CLI_PATH to force a specific binary. Hermes updates itself (hermes update), so it is not part of caliper update-cli.

Check installed CLI versions:

caliper update-cli --check

---

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. Move to --k 3 or higher once the task is stable.
5. Run once with --ablate <skill> and caliper compare the two runs, to prove the skill is making a difference. That arm is a property of the tasks, so keep it and re-diff against it as the skill changes.
6. Commit the spec alongside the skill so contributors can run the same eval.

/evaluate-skill run my-skill.eval.yaml --k 3 --verbose

---

Spec format

To scaffold a spec, use the evaluate-skill or grill-skill skill, or hand-write the YAML below.

skills:                         # installed where the agent looks for skills,
  - ./SKILL.md                  #   never pasted into the prompt
  - ../evaluate-skill/SKILL.md  # a path source: whatever that file says today
  - repo: vercel-labs/agent-skills   # a git source: caliper clones it
    ref: a1b2c3d                     #   optional — omit to track the default branch
    path: skills/tdd/SKILL.md        #   optional — defaults to SKILL.md at the root
                                # omit `skills:` entirely for a bare agent

# Note: there is no `backend`/`model` or `judge:` block. The engine is a runtime
# axis: pass `--model` / `--judge-model` at run time (default: claude-code).

sandbox:
  extra_path:
    - ./bin                     # prepended to PATH inside each attempt
  forbidden_files:
    - ".*\\.eval\\.yaml$"       # prevents agent from reading the spec
    - "./.caliper/.*"           # prevents agent from reading saved results

mcp:                            # optional: MCP servers the agent may use
  weather:                      # server name → a mcp__weather__<tool> call in the transcript
    command: python3            # a local stdio server the harness spawns
    args: [./servers/weather.py]
    env:
      API_TOKEN: ${MCP_API_TOKEN}   # ${VAR} resolves from your shell at run time
  gdrive:                       # a remote (hosted) server reached over HTTP
    type: http                  # http or sse
    url: https://mcp.example.com/gdrive
    headers:
      Authorization: Bearer ${GDRIVE_TOKEN}   # ${VAR} resolves at run time

tasks:
  - name: Short task name
    setup: <shell command>      # optional, runs before each attempt
    cleanup: <shell command>    # optional, always runs after each attempt
    prompt: <prompt sent to the agent>
    expect: <natural-language success condition>
    assert: |
      # optional inline Python assertion
      assert True

  - name: Task with external assertion script
    prompt: "Generate a report"
    assert: ./assertions/check_report.py

  - name: A neighbour's prompt: yours must not hijack it
    prompt: "How reliable is my commit-message skill? Run it 10 times."
    activates: [evaluate-skill]   # exactly these skills, and no others

  - name: Unrelated work, silence expected
    prompt: "Rename `resolved_model` to `engine_model` across the repo."
    activates: []                 # nothing should fire

Each task needs at least one of expect, assert or activates. Task IDs are assigned automatically as task-001, task-002, and so on.

Upgrading an existing spec? skill: became skills: in v0.10. See docs/MIGRATING-to-skills.md for a short checklist, including the two traps a find-and-replace misses (stale skill.path inside prompt:/expect:/assert: strings, and prompts that name the skill they're testing).

skills:, the neighbourhood

Every entry is installed at the agent's own skills root under its frontmatter name:, and nothing is preloaded. Entries are peers: no entry is "the skill under test", so activates: always names skills explicitly.

The set is closed. The agent sees these skills and nothing else, which is what makes activation a measurement rather than a guess. It also means a skill you don't declare can never activate: if yours delegates to another skill, declare that one too and enumerate the whole chain (activates: [mine, helper]), which makes "did it actually delegate?" assertable.

A skill must be a SKILL.md in a directory, carrying frontmatter name: and description:. A lone slash-command .md is rejected: with no name and no description there is nothing for an agent to discover.

Path sources and git sources

An entry is written one of two ways, and the shape is the difference:

Entry	Means
- ./SKILL.md	a path source — a file on your disk, whatever it says at run time
- {repo: …, ref: …, path: …}	a git source — caliper clones it and resolves ref: to a commit

Git sources are how you give your description real competition to win against without vendoring somebody's repo into yours. One entry is one skill; entries sharing a repo and commit share one clone, so naming five skills from a pack costs five entries and one fetch.

repo: takes anything git can clone. A bare owner/name is expanded to https://github.com/owner/name; a URL, an scp-style git@host:owner/name, or a filesystem path is passed through untouched. To point at a local repo by relative path, write ./owner/name — the leading ./ is what tells it apart from the shorthand.

ref: is optional and an omitted one tracks the default branch, so it will move. That's allowed rather than forbidden because caliper records the commit it resolved and compare tells you when it moved — see below. Pinning a commit is still worth it: a pinned entry is fully offline once fetched, an unpinned one costs one git ls-remote per run.

caliper run fetches before the first attempt, so a bad repo: costs you nothing. caliper validate never touches the network: it resolves git sources from the cache when it can and reports the rest as not cached (and says so when that means it couldn't check your activates: names).

Checkouts land in ~/.cache/caliper/skills/ (or $XDG_CACHE_HOME/caliper/…), keyed by resolved commit — so they're immutable, shared across every spec that names them, and safe to delete. Set CALIPER_CACHE_DIR to put them elsewhere.

If a git source can't be fetched and isn't cached, the run refuses — a member silently missing would measure your skill against competition that wasn't there. If it's cached but the remote is unreachable, the run uses the cache and says so.

Skill drift

caliper compare reports any member whose text changed between the two runs. A git source that moved gets a warning: the spec said where its bytes came from, and the delta you're reading is confounded. A path source that moved is shown without alarm — that's usually the edit the run exists to measure.

 ⚠ tdd changed between runs — git source, a1b2c3d → e4f5g6h; pin `ref:` to hold it fixed
   my-skill changed between runs — path, 4fc7951 → bcbcbde

This is a change in text at constant membership. A change in membership — different skills installed — is the separate neighbourhood warning.

activates:: did the agent reach for it?

activates: asserts the exact set of skills that loaded on each attempt.

Form	Means
(omitted)	not asserted; the column still shows what loaded, dimmed
activates: [a]	exactly a fired, and nothing else
activates: [a, b]	both fired, which is how a delegating skill asserts its chain
activates: []	nothing fired; silence held

A task with activates: and no expect:/assert: is a trigger probe: it asks only what the agent reached for, skips the judge entirely (so it is much cheaper than an execution task), and reports as trigger only rather than a zero. Use it for neighbour and silence probes, where there is no work worth grading.

Activation is scored on its own scoreboard, never blended into the success rate. A failing description and a failing body are fixed in different places, so one number mixing them would point at neither.

MCP servers (mcp:)

The optional mcp: block declares the MCP servers the agent-under-test may use. It is a capability granted to the agent for the eval, part of the run environment like sandbox:, so it lives in the spec rather than behind a flag. It is a top-level mapping keyed by server name (a sibling of sandbox: and skills:, and it applies whether or not the eval declares any skill). Each server's tools appear in the transcript as a namespaced call an expect: judge can verify (mcp__<server>__<tool> on claude-code and codex, mcp_<server>_<tool> on hermes), so word an expect: around the tool's behavior, not one backend's exact spelling, if the spec is meant to run under more than one engine.

A server is either local (stdio), a command the harness spawns, or remote (type: http or sse), a hosted endpoint at url, the shape most connectors (Google Drive, Notion, and so on) use:

mcp:
  weather:                      # local stdio server (the default transport)
    command: python3            # required: the local stdio command to spawn
    args: [./servers/weather.py]  # optional
    env:                        # optional
      API_TOKEN: ${MCP_API_TOKEN}
  gdrive:                       # remote server
    type: http                  # required for remote: http or sse
    url: https://mcp.example.com/gdrive   # required for remote
    headers:                    # optional: usually auth
      Authorization: Bearer ${GDRIVE_TOKEN}

• claude-code, hermes, and codex. All three wire mcp: through: claude-code honors stdio and remote (HTTP/SSE); hermes honors stdio and remote header-auth (it translates the block into its native mcp_servers config inside the isolated HERMES_HOME, resolving ${VAR} at the harness boundary and overwriting any of your personal servers so an attempt sees only the declared set); codex honors stdio and remote header-auth the same way, translating the block into [mcp_servers.*] tables in the isolated ~/.codex/config.toml (stdio as command/args/env, remote as url + a static http_headers map of boundary-resolved literals; codex infers its one streamable-HTTP transport from url, so http/sse collapse onto it), resolving ${VAR} at the boundary and replacing any personal servers from your real config so an attempt sees only the declared set. Remote OAuth is not supported on hermes or codex, since it needs an interactive browser flow the harness can't drive. Running a spec that declares mcp: on a backend that can't honor it is a hard error rather than a silent no-op. pi does not and will not honor mcp: natively: its agent has no MCP by design. Instead of MCP, expose the capability as a CLI tool your skill drives (a skill with a README) or a pi extension, or run the eval on claude-code/hermes/codex. Running an mcp: spec on pi fails with that guidance.
• Transport is set by type:. Omitted (or stdio) means a local command; http/sse means a remote url. The two field sets are mutually exclusive: a stdio server can't set url/headers, and a remote server can't set command/args/env.
• Secrets stay out of the spec. A value in a stdio env:, a remote headers:, or a remote url: may reference a host environment variable as ${VAR}; it is resolved from your shell at run time (never written into the committed spec), and an unset variable fails the run with a clear message.
• Server names must match [A-Za-z0-9_-]+ so the backend's namespaced tool handle (mcp__<server>__<tool> / mcp_<server>_<tool>) is well-formed.

caliper validate checks the mcp: block and reports a malformed entry (bad name, unknown key, unknown type, a stdio server missing/blank command, or a remote server missing url).

---

Judging

LLM autorater (expect:)

The judge engine reads the full attempt transcript and decides whether the expect condition was met. When the backend captures tool-call traces (Claude Code, Codex, pi, Hermes), those traces are included, so the judge can verify things like "the agent used tool X" without relying on the final text alone.

The judge engine is chosen at run time and defaults to claude-code; point it at a different agent with --judge-model (e.g. --judge-model codex), independently of the skill's --model.

Deterministic assertions (assert:)

Python assertions run locally. Use these for facts the LLM judge might guess:

• file exists / exact file contents
• JSON / schema validity
• command output
• images or screenshots
• repository state

tasks:
  - name: Writes an output file
    cleanup: rm -f /tmp/out.txt
    prompt: "Write hello world to /tmp/out.txt"
    assert: |
      from pathlib import Path
      path = Path("/tmp/out.txt")
      assert path.exists(), "Output file was not created"
      assert path.read_text().strip() == "hello world"

When both expect and assert are present, both must pass.

---

CLI reference

Command	Description
caliper run <spec>	Run an evaluation spec
caliper validate <spec>	Validate a spec file
caliper list [spec]	List specs and saved runs. Per-spec, each row carries its Run id and which skills that run ablated — how you find the control arm to diff against
caliper report <spec-or-result>	Re-render saved results
caliper compare <A> <B>	Diff two saved runs of the same eval, task by task. Each side is a spec name (that spec's latest run) or a results-JSON path; they must be two distinct runs
caliper update-cli [backend]	Check or update installed agent CLI versions

caliper run flags

Flag	Default	Description
--k INT	3	Attempts per task
--ablate NAME	none	Run without this declared skill installed (repeatable; name them all for the bare agent)
--workers INT	4	Parallel task workers
--timeout INT	120	Seconds per attempt
--fail-fast INT	0	Stop a task after N consecutive infra_error/timeout attempts (0 disables)
--model TARGET	claude-code	Skill engine: backend and/or model (see below)
--judge-model TARGET	claude-code	Judge engine: backend and/or model (see below)
--verbose	off	Show per-attempt judge reasoning
--output PATH	—	Also save results JSON to a specific path

--model and --judge-model syntax

The engine is not stored in the spec; these flags select it, defaulting to claude-code when omitted. Both accept a backend:model compound value, a bare backend name, or a bare model name:

# Backend and model together
caliper run my-skill.eval.yaml --model codex:gpt-5.6-sol

# Backend only (that backend's default model)
caliper run my-skill.eval.yaml --model codex

# Model only (backend stays claude-code)
caliper run my-skill.eval.yaml --model claude-fable-5

# Select the judge engine independently
caliper run my-skill.eval.yaml --model codex --judge-model claude-code:claude-haiku-4-5-20251001

Accepted backends: claude-code, codex, pi, hermes (alias: claude → claude-code). The actual engine used is recorded in each saved run's RunMeta (the skill backend/model, and the judge_backend/judge_model that graded it), so results stay traceable even though the spec doesn't pin it. When you don't name a model and the CLI uses its own default, RunMeta records the concrete model the agent resolved rather than a bare "default", wherever the backend reports it: the skill model from hermes' session export, and the judge_model from the claude-code judge's JSON output. judge_model stays empty for an assert:-only run, where no LLM judge fired. When --judge-model is omitted, the claude-code judge still pins claude-sonnet-5 at execution time so it does not inherit a stale model from the installed Claude CLI; that pin is not written into RunMeta unless you pass it explicitly or the autorater reports what it used.

---

Comparing two runs (caliper compare)

An ablation compares two runs of the same eval: a full skill against a shortened variant, or the same skill at two points in time. caliper compare <A> <B> diffs two already-saved runs task by task, so you don't have to hand-write a JSON script to answer "did this change regress?".

# Latest run of each spec (a bare spec name resolves to its latest run)
caliper compare commit-simple-full commit-simple-short

# Pin specific runs by pointing at their results JSON
caliper compare .caliper/results/demo/2026-07-01T10-00-00Z.json \
                .caliper/results/demo/2026-07-02T09-00-00Z.json

# Machine-readable diff for a ship / no-ship decision
caliper compare A B --format json

Each positional (A, B) is addressed exactly like report's argument: a spec name (which resolves to its latest run) or a path to a results JSON. There are no --run-a/-b flags. To pin a historical run, name its JSON path.

How the diff reads:

• Each row reads before → after. The runs are named once in the header (an ablation pair is titled without <skill> → full neighbourhood), so there's no A/B legend.
• Tasks are matched by name, so reordering doesn't matter. A task in only one run is listed as unmatched and left out of the delta.
• Δ is after − before, and the headline Δ (matched) averages only the tasks measured on both sides, so it stays strictly like-for-like. A negative Δ renders red and flags a regression.
• Unusable attempts can't fake a loss. A side with no usable attempts (rate-limit / timeout / judge error) shows — and never counts as a regression.
• Token and wall-clock deltas are secondary and never a regression: a drop is green (cheaper), a rise red (a trade-off to weigh). Only the score feeds has_regression.

--format json serializes the full comparison (per-task scores, deltas, regression flags, unmatched lists, warnings, skill_drift, and per-side usage) for scripting. Each skill_drift entry carries the member's name, source_kind, and the two sides' a_ref/b_ref — so a script sees drift for every member, including the path-sourced ones that don't raise a warning.

---

Scoring

Every attempt carries a typed outcome, so infrastructure and judge noise are not scored as task failure:

Outcome	Meaning	Counts toward the score?
pass	satisfied the task's judge(s)	✅ success
task_fail	the skill genuinely failed the task	✅ attempt
cheat	a forbidden-file read was detected	✅ attempt
infra_error	harness failure: nonzero exit, or a detected rate-limit / spending-cap	❌ unusable
timeout	exceeded the time budget with no result	❌ unusable
judge_error	the judge produced no verdict (unparseable / errored autorater)	❌ unusable
not_checked	the task authored no expect:/assert:, so it is a trigger probe	⊘ not asked

not_checked is the one outcome that is neither: it leaves the denominator like an unusable attempt, but nothing went wrong, so it is never reported as an error and its tokens are not counted as wasted spend.

The primary metric is the raw success rate: how often a single run works, computed over the usable attempts (the ones that got a fair shot). Unusable attempts leave the denominator and are reported as a separate "N unusable" count:

usable  = pass + task_fail + cheat
score   = successes / usable                # raw rate; None if usable == 0

Two secondary views are kept for anyone who wants them (shown under --verbose, and on every task in the JSON as pass_at_k / pass_hat_k):

pass@k  = 1 - (1 - score) ^ usable   # P(≥1 of k passes)
pass^k  = score ^ usable             # P(all k pass)

Which one to look at depends on how the skill is actually used:

The question you're asking	Metric	For a 1/3 skill (k=3)
How reliable is a single run? (default)	success rate	33%
If I retry up to k times and keep any win, do I get one?	pass@k	70%
Will it work on every run, no exceptions?	pass^k	4%

Use pass@k when retrying is cheap and you keep the winning run; it's the optimistic view, always ≥ the raw rate. Use pass^k when the skill runs unattended and one failure breaks the chain; it's the strict view, always ≤ the raw rate. Caliper leads with the raw rate because pass@k flatters flaky skills (1/3 → 70.4%).

The aggregate is the average task success rate, skipping tasks with no usable attempts. To get a delta against the bare agent, run the same tasks with --ablate and caliper compare the two saved runs.

--fail-fast N stops scheduling new attempts for a task after N consecutive infra_error or timeout outcomes (default 0 runs all k). An early-stopped task shows as ABORTED; if every completed attempt was unusable, its score stays null and it's skipped in the aggregate.

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Token and time usage

Pass@k tells you whether a skill works; usage tells you what it costs to get there. Two runs can have identical scores while one burns twice the tokens. Caliper records token volume and wall-clock time per attempt and rolls them up per run:

 With skill    100.0%  ████████████████████

 Tokens   1.2M in / 340K out
 Wall     6m 18s  12.6s per attempt
 ⊘ unusable spend: 180K tokens, 42s  (2 attempts, not counted in the average)

• The results table carries per-task Tokens and Wall columns, so you can spot the expensive task at a glance; the summary line below aggregates the whole run.

• Each AttemptRecord carries an optional usage object that splits tokens four ways:

  • input_tokens: prompt, excluding cache
  • output_tokens: generated output
  • cache_read_tokens: cache hits
  • cache_creation_tokens: cache writes

  Those four are disjoint, so the computed total_tokens never double-counts. Wall-clock time comes from duration_seconds, which was already recorded.

• Each AttemptRecord also carries an optional transcript array of ordered turns (role, content, and tool tool_name/tool_input/tool_output when present). This preserves the full tool-call trace in saved results for later inspection; older JSON without the field still loads (transcript is null).

• Each SkillSnapshot records source_kind ("path" or "git") alongside git_repo/git_sha, so a saved run says how each member of the neighbourhood was obtained and — for a git source — the exact commit it was fetched at. Older JSON without the field still loads and reads as "path".

• In the summary, in = input + cache_read + cache_creation and out = output. The unusable slice (timeout / infra / judge error) is broken out separately, so wasted spend stays visible without distorting the per-attempt average.

• Support: claude-code, codex, pi, and hermes all report usage; a backend that can't leaves the fields null and renders —. codex includes cache in its input_tokens, so it's normalized to the non-cached contract above.

• Dollar cost is deliberately not tracked: it's inconsistent across backends. Tokens are the volume signal, so derive a dollar figure downstream if you need one.

• An ablated run is an ordinary saved run, so the skill-vs-bare-agent view is caliper compare like any other diff — same table, attempt strips, and token/wall deltas.

• report --format json adds a derived usage_totals block; the saved JSON keeps the raw per-attempt usage (totals are always derived, never persisted).

Activation fields in saved results

• Each AttemptRecord carries activated, the skills the agent chose to load, recorded on every attempt whether or not the task asserted on it. It is null when nothing was observable: an attempt with the whole neighbourhood ablated (no skills installed), or a timeout / infra failure whose transcript may be truncated. A bare [] in those cases would be a fabricated "the description never fired", so caliper never writes one.
• activation_passed is the verdict: null = not asserted (a different null from activated's, matching the existing assert_passed idiom).
• TaskResult carries activation_expected (the task's activates: set) plus derived activation_usable / activation_successes / activation_score.
• AggregateScore carries avg_activation_score, activation_tasks, and activation_per_skill (per-skill expected/fired/hits with derived recall/precision), alongside scored_tasks for the execution half.
• RunMeta.era records the loading discipline a run was produced under. Pre-#18 runs have no era, and caliper compare refuses to diff across that boundary, because those runs measured something else (see ADR 0013). A neighbourhood change between two same-era runs only warns.
• RunResults.skill_snapshots is a list, one snapshot per declared skill, since a neighbour's description is part of what produced the score. Runs saved before #18 carry a singular skill_snapshot; they still load, and their missing era is what makes compare refuse them.
• TaskComparison carries a_activation/b_activation/activation_delta/ activation_regression, and RunComparison carries has_activation_regression, kept strictly separate from has_regression.

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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.

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Troubleshooting

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

Judge model ... is unavailable / Judge authentication failed / Judge rate limited The judge CLI reached the provider and the call was refused. Caliper classifies these at the harness boundary (from the CLI's structured output) and suggests passing --judge-model <backend[:model]> to pick an available judge engine or model. Example: caliper run my-skill.eval.yaml --judge-model claude-code:claude-haiku-4-5-20251001.

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.