benchmark-harness

name	benchmark-harness
description	Evaluate the coding harness you are running inside (pi, opencode, claude-code) on its live setup and current model, via bench setup run. Measures it with and without its skills, MCP servers and plugins on --ab, and reports which of them the run actually called. Use when the user runs /benchmark-harness. Not for serving sweeps, model compare, or one-shot suites.
license	MIT
effort	high
metadata	{"version":"1.4.0","author":"Luong NGUYEN <[e-mail]>","architecture":"inline (single agent, no subagents)"}

Benchmark Harness

Measure this harness — the one whose shell you are running in — exactly as it is configured right now, and report what to improve. Wraps ./bench setup run from dgx-spark-llm-lab (issue #76).

Start here

Reading this text is the invocation. Some harnesses inject a skill body with no user turn attached; never read that absence as "they have not asked yet", and never reply that you are ready when they are. Begin at step 1 now.

Run steps 1–4 immediately — cheap, read-only, seconds: locate the repo, prove the oracles, detect harness and model, record the conditions, print the plan. Then stop at the confirm gate in step 4. That gate, not this section, protects the user from a 15–40 minute run.

Exception: the user's message contradicts the plan ("just tell me what you would run", --dry-run). Stop after step 4 and say so.

When to use

This skill is user-invoked: /benchmark-harness, or a request to benchmark "this harness", "my current setup", "the agent I'm in". Do not apply it, unprompted, to an adjacent request that merely mentions benchmarking.

Not this skill: bench compare (two models head-to-head), bench sweep (serving-config matrix), bench run (one-shot suites through benchkit's own loop), bench apply (installing a serving config).

Prerequisites

Check these in step 1; each failure stops the run rather than being worked around.

• The dgx-spark-llm-lab repo, at issue #76 or newer (it must have bench setup). Set BENCH_REPO when invoking from elsewhere.
• Python ≥ 3.10 with benchkit importable (pip install -e .).
• One of pi, opencode or claude-code installed, and this shell running inside it.
• Credentials the harness already uses — the run borrows your own auth, and a hosted model bills your account.
• ./bench validate --suite agentic-all printing 16/16.

Live run vs isolated run

Two commands measure two different things. This skill does the live run by default, and both arms on --ab.

	live run — bench setup run (default)	isolated run — bench harness run
Skills, MCP servers, plugins, settings	on — your daily setup	stripped
Answers	"is my setup any good, what should change?"	"how good is this model?"
Extra output	REPORT-live.md with advice	result JSON only

If the user wants the model measured rather than the setup, say so and run bench harness run instead — same arguments.

--ab runs both and diffs them, which is the only way to answer "are my skills and MCP servers worth what they cost?". Both commands take identical flags, so the arms differ in isolation and nothing else — pass the same -m, --suite, --samples, --concurrency and --thinking to each or the comparison is meaningless. Two arms cost twice the wall-clock and twice the billing, which is why it is opt-in and why the confirm gate shows the doubled estimate. Semantics, per-harness caveats and how to read a delta: references/surface-ab.md.

A delta never stands alone. A live arm can win with a surface that was never invoked, in which case the surface did not cause the win. Always report the usage attribution (step 6) beside the delta.

Repo Sync Before Edits (mandatory)

A run writes new files into the repo (results/<date>/…), so sync first:

branch="$(git rev-parse --abbrev-ref HEAD)"; git fetch origin && git pull --rebase origin "$branch"

Dirty tree: git stash → sync → git stash pop. No origin, or conflicts: stop and ask. Never commit results unless the user asks.

Defaults and overrides

Arguments to this skill; step 4 maps them onto bench flags.

Knob	Default	Override
harness	detected (step 2)	--harness pi|opencode|claude-code
model	the harness's current selection	-m <provider/model> or any unique substring
suite	agentic-hard (8 tasks)	--suite agentic|agentic-hard|agentic-all
samples	1	--samples N
thinking	detected; only pi honours it	--thinking / --no-thinking
concurrency	2	--concurrency N
arms	live only	--ab — add the isolated arm and diff (2x cost)
confirm gate	always confirm	--yes
plan only	off	--dry-run

One-shot suites (core16, hard12, all) are refused by harness runs — those tasks need a tool loop. Ask the user to pick an agentic suite; never switch silently.

Step 1 — Locate the repo and prove the benchmark

repo="${BENCH_REPO:-}"
[ -x "$repo/bench" ] || { d="$PWD"; while [ "$d" != / ]; do
    [ -x "$d/bench" ] && { repo="$d"; break; }; d=$(dirname "$d"); done; }
[ -x "$repo/bench" ] || repo="$HOME/workspace/luongnv89/dgx-spark-llm-lab"
cd "$repo" || exit 1
python3 -c "import benchkit" 2>/dev/null || pip install -e .
./bench setup --help >/dev/null       # live-setup runs need this repo at #76 or newer
./bench validate --suite agentic-all  # must print 16/16, takes ~1 s
./bench harness list                  # the detected harness must read "ok"

Three hard gates: no bench setup → the clone is too old, offer a git pull; not 16/16 → a broken oracle makes every later number meaningless, fix the harness and never the task (see Guardrails); pip install -e . failing → report and stop, never retry with sudo, --user or --break-system-packages.

◆ Preflight (step 1 of 6 — repo + oracles)
··································································
  Repo located:          √ pass (<path>)
  benchkit importable:   √ pass
  `bench setup` present: √ pass
  Oracles:               √ 16/16
  Harness installed:     √ pass (<name> <version>)
  ____________________________
  Result:                PASS

A × on any row stops the run; references/failure-modes.md maps each failure to its fix.

Step 2 — Detect harness and model

bash .agents/skills/benchmark-harness/scripts/detect_setup.sh   # --harness <name> if given

The skill lives in .agents/skills/ so every harness can load it; .claude/skills/ is a symlink to the same files. It prints harness=, provider=, model=, model_source=, thinking= and how each was decided. Rules:

1. User arguments always win over detection.
2. Never run without -m. With no model the picker prompts on a tty and hard-errors everywhere else — including every agent shell.
3. Empty model= means ask, never guess. Show ./bench harness models --harness <h> and let the user choose. Common on opencode, which has no default until one is set.
4. claude-code: settings.json is intent, not proof. An in-session /model switch leaves no trace on disk. If you are the session being measured, your own model beats the file — say so, and confirm it in step 3.
5. Thinking is a pi-only axis. thinking=1 → pass --thinking; 0 or unsupported → omit it. pi maps it to --thinking off|high; opencode and claude-code ignore the flag and their server default applies, so report n/a rather than implying a mode was set.
6. Exit 3 means the harness is unknown, not broken. Say plainly that this shell is not inside pi, opencode or claude-code — a Cursor or aider session is not measurable here — and offer --harness for one of the three instead of pretending the numbers describe the harness the user is in.

Per-harness sources, precedence and edge cases: references/detection.md.

◆ Detection (step 2 of 6 — harness + model)
··································································
  Harness:            √ <name> (<source>)
  Model spec:         √ <provider/model> (<source>)
  Thinking:           √ <on|off|n/a> (<source>)
  Suite valid:        √ agentic-hard (8 tasks)
  ____________________________
  Result:             PASS

Step 3 — Capture the run conditions

A score is unreadable without the machine and setup that produced it, so record both before the run starts:

mkdir -p /tmp/bench-harness
bash .agents/skills/benchmark-harness/scripts/collect_context.sh \
     --harness <h> --model <spec> --thinking <on|off|n/a> > /tmp/bench-harness/context.md

It prints the machine, the GPU and what else is using it, the serving endpoint, and the harness's live surface — skills, MCP servers, extensions. Every probe is fail-soft: a missing one prints unknown rather than blocking the run. Field meanings and how to read them: references/run-context.md.

Two rows decide whether to run at all:

• GPU util / other GPU processes. A device already busy makes wall-clock, turns and timeouts incomparable with any other run. Surface it at the confirm gate and offer to wait rather than quietly producing a number nobody can reuse.
• serves. Empty against a local endpoint means nothing is serving yet — fix that first.

◆ Conditions (step 3 of 6 — machine + setup)
··································································
  Machine recorded:   √ <host>, <cpu>, <memory>
  GPU:                √ <name>, <util> at start, <N> other process(es)
  Endpoint:           √ <serves> (<base url>)
  Harness surface:    √ <version>, <N> skills, <N> MCP/extensions
  ____________________________
  Result:             PASS | PASS (contended GPU — flagged at the gate)

Step 4 — Confirm the plan

Skip only on --yes. Print exactly what will run, then wait:

harness      claude-code (env:CLAUDECODE)
model        opus[1m]  (your current session model)
suite        agentic-hard — 8 tasks x 1 sample, concurrency 2
thinking     n/a (adapter ignores it for claude-code)
mode         LIVE — your skills, MCP servers and settings are part of the measurement
arms         1 (live only)          ← with --ab: 2 (live, isolated) — doubles everything
estimate     8 tasks / concurrency 2, up to --timeout 900 s each -> 15-40 min typical
writes       results/<today>/<label>.json + REPORT-live.md   (append-only)
cost         billed to your own <harness> account/quota
machine      dgx-spark — GB10, 120 GiB, GPU 95 % busy (vLLM, 73 GiB) ← flag contention here
setup        claude-code 2.1.245, 70 skills, 0 MCP servers — all of it is measured

For claude-code, smoke-test the spec first — that adapter cannot enumerate models, so a bad id is otherwise discovered task by task:

timeout 60 claude -p 'reply with OK' --model "<spec>" >/dev/null && echo "model ok"

Rejected: retry with the bare alias (opus[1m] → opus). Done when the user has said go, or --dry-run stopped you here.

Step 5 — Run it detached

A run outlives any tool timeout, so never block on it:

mkdir -p /tmp/bench-harness
log=/tmp/bench-harness/$(date +%s).log
nohup ./bench setup run --harness <h> -m <spec> \
      --suite agentic-hard --samples 1 --concurrency 2 [--thinking] \
      > "$log" 2>&1 &
echo $! > "$log.pid"

With --ab, run the isolated arm after the live one finishes, in the same shell command so the pair survives as one detached job:

nohup sh -c './bench setup   run --harness <h> -m <spec> --suite <s> --samples <n> \
                             --concurrency <c> [--thinking];
             ./bench harness run --harness <h> -m <spec> --suite <s> --samples <n> \
                             --concurrency <c> [--thinking]' > "$log" 2>&1 &
echo $! > "$log.pid"

Never run the two arms concurrently. Against a local endpoint they would contend for the same GPU, and each arm's wall-clock, turns and timeouts would then be a measurement of the other arm. Sequential costs twice the time and is the only ordering that produces a comparable pair. Same reason the GPU-contention row in step 3 gates the run at all.

Poll tail -n 20 "$log" every minute or two. Done when the log holds written to results/… and the process is gone — with --ab, two written to lines, one per arm. If it dies early, read the last 40 lines against references/failure-modes.md — never re-run blind. If the second arm dies after the first succeeded, report the live arm alone and say the A/B is missing; never diff an arm against a run from another campaign.

◆ Run (step 5 of 6 — <suite>)
··································································
  Process exited:     √ rc=0
  Tasks reported:     √ 8/8
  Result JSON:        √ results/<date>/<label>.json
  Live report:        √ REPORT-live.md
  Isolated arm:       √ results/<date>/<label>.json   (--ab only; ○ n/a otherwise)
  ____________________________
  Result:             PASS

Step 6 — Report

Take both paths from the log's written to … / report written to … lines. Append the conditions captured in step 3 to the report the run just wrote, so the numbers and the setup that produced them stay together:

report=$(awk '/^report written to /{print $NF}' "$log" | tail -1)
cat /tmp/bench-harness/context.md >> "$report"

Do not assume the filename is REPORT-live.md: the runner de-duplicates it, so a results directory that already holds one gets REPORT-live.1.md, REPORT-live.2.md, and so on — only the log line names this run's report. Appending to this run's own report is the only write allowed here — never touch a report from an earlier campaign.

Then attribute the tool calls, so the surface is judged on what it did rather than on being switched on:

# the live arm runs first, so its `written to` line is the first one; with --ab
# the isolated arm's is the second, and `iso` is empty without --ab
live=$(awk '/^written to /{print $NF}' "$log" | sed -n 1p)
iso=$(awk  '/^written to /{print $NF}' "$log" | sed -n 2p)

# built with `set --`, not `${iso:+--isolated "$iso"}`: zsh expands that to a
# single argument and argparse rejects it
set -- --live "$live" --context /tmp/bench-harness/context.md
[ -n "$iso" ] && set -- "$@" --isolated "$iso"
python3 .agents/skills/benchmark-harness/scripts/surface_usage.py "$@" | tee -a "$report"

It classifies every call as built-in, MCP, skill or plugin, and with --isolated adds the live-vs-isolated delta table. Three rules when reporting it:

1. An idle surface is the headline, not a footnote. Zero skill and MCP calls means the score was earned by the model and built-in tools alone, and every prompt token the surface costs was paid on every task for nothing. Say it in those words.
2. Never attribute a delta to a surface that was idle. The two sections are read together or not at all.
3. Skill invocations are counted, not named. benchkit records the tool name and discards the input that holds the skill's identity. Report 3 skill invocations, names not recorded — never guess which skill fired.

Then read the printed summary and the advice section, and give the user:

• The conditions first — machine, GPU contention, endpoint, harness version and how much live surface (skills, MCP servers, extensions) was in the loop. A reader who cannot reproduce the conditions cannot use the score.
• Agent score and its two factors (solve rate × efficiency), plus calls vs par, turns, token cost in/out per task, valid tool-call rate, wall-clock.
• Harness, model and thinking mode next to every number. The same model scores 67.4 through benchkit's own loop and 77.4 through pi. A number without its harness is not a result.
• The report's setup advice, condensed to the changes worth making.
• A noise caveat where it applies: differences under ~8 points at --samples 1–2 are noise — say so rather than declaring a winner, and offer --samples 4.
• Where the artefacts landed, so a later run can be diffed against them.

On claude-code, add unprompted: reasoning tokens read 0 because Claude Code reports them in a field this stack always zeroes — the output-token budget is still right.

Expected output

dgx-spark — GB10 / 120 GiB / Ubuntu 24.04 aarch64, GPU 95 % busy (vLLM 73 GiB)
endpoint montimage-dgx-spark @ localhost:8001 — claude-code 2.1.245, 70 skills, 0 MCP
claude-code / opus[1m], thinking n/a — agentic-hard, 1 sample
  agent score        74.2  (solve 87.5 % x efficiency 84.8 %)
  calls vs par       11.4 vs 9.0     turns 6.2
  tokens in/out      41k / 3.1k per task     valid calls 96.4 %
  wall               22 min
surface: 70 skills + 0 MCP installed, 0 skill calls and 0 MCP calls in 214 tool calls
         — the surface was idle; it did not earn this score, and it is not free.
advice: 2 MCP servers add 4.1k tokens to every task and were never called — drop them.
noise: gaps under 8 points at 1 sample are not real; re-run with --samples 4 to settle.
written: results/2026-08-25/claude-code-live-opus-1m-think-off.json + REPORT-live.md
         (run context and surface usage appended to the report)

With --ab, add the delta table between surface: and advice:, and state which arm won by how much — or that the gap is under the 8-point noise floor and therefore not a result.

Done when every line above is present, each score carries its harness and model, the surface line says what was called and not merely what was installed, and the report on disk ends with the run-context and surface-usage sections.

Guardrails

• Never edit tasks, tests, asserts or check. That destroys the benchmark.
• results/ is append-only. Never delete or overwrite; the runner picks a unique path.
• Never restart a serving endpoint here. That is bench apply / bench sweep, needs human approval, and is out of scope.
• Live mode really is live: on claude-code and opencode the run inherits your skills and MCP servers, and each concurrent task is a full child session billed to you.
• Both thinking modes only for pi, where the flag does something: two runs, each with its own --label, or the second is impossible to tell from the first.
• A/B arms run one after the other, never at once. Concurrent arms contend for the same GPU and each becomes a measurement of the other.
• Never claim a skill or MCP server helped without a call in the trace. "Installed" is not "used", and the whole point of step 6 is that the score cannot tell them apart.

References

• references/detection.md — per-harness model/thinking sources, precedence, edge cases.
• references/run-context.md — what each captured condition means and when it invalidates a comparison.
• references/surface-ab.md — what each arm strips, how calls are attributed, how to read a delta, and why skill names are not recorded.
• references/failure-modes.md — error message → cause → fix, for every failure seen.