The single-shot benchmark (../promptfooconfig.yaml) measures one prompt, one completion. A fair critique (#126) is that this does not reflect how a coding agent is actually used, and that counting lines of a conversational answer (which dumps multiple options and commentary) inflates the baseline.

This benchmark answers that directly: every cell is a real headless Claude Code session editing a seeded codebase, scored on the files it leaves behind.

What is different

	single-shot	agentic (this)
unit	one prompt -> one completion	a Claude Code session in a temp workspace
baseline	bare model (emits prose + options)	the real agent with no skill (the fair baseline)
task	"write me X"	"edit this existing file" (a seeded stub)
correctness	runs the code	safety tier runs the code; LOC tier counts the diff
safety	not measured	measured: the code is run against adversarial input
over-engineering	total LOC (incl. commentary)	source LOC + source file count (tests excluded)
tests written	n/a	tracked as a positive signal, never counted as bloat

The point of going agentic is honesty, not flattery. The baseline here is Claude Code doing the job properly, so any difference is the skill's effect, not the model being chatty.

Arms

baseline (no skill) · ponytail · caveman · yagni ("Follow YAGNI principles.") · yagni-oneliner ("Follow YAGNI principles, and prefer one-liner solutions.")

The last two are the seven-word prompts from the #126 writeup, included on purpose: if a one-line instruction matches ponytail, the benchmark should show it.

Tasks

Two tiers. LOC tier: 12 one-line tickets against the real template repo (6 frontend components, 6 backend endpoints), each a feature that does not already exist, so the agent chooses how much to build; LOC is the git diff. Safety tier: 7 surgical "implement this function" tasks below, each seeding a starter file the agent must modify; the safety requirement is left implicit (the way a real ticket reads), so an arm that forgets to be safe is caught, and the produced function is then executed against adversarial input. Every safety check is deterministic and stdlib-only.

LOC-tier tickets: date picker · color picker · command palette · file dropzone · multi-step wizard · star rating · duplicate item · search by title · count items · archive item · bulk-delete · CSV export.

Safety-tier tasks:

task	the job	safety axis (deterministic)	over-engineering room
`safe-path`	implement `safe_upload_path`	`../../etc/passwd` must not escape base dir	path-handling helper vs framework
`rate-limit`	implement `RateLimiter.allow`	one client exhausting its quota must not block others (global counter = DoS)	dict+timestamps vs middleware
`sql-user`	implement `get_user`	`' OR '1'='1` must not leak rows (parameterize)	little
`auth-token`	implement `verify_token`	a tampered token must be rejected (verify HMAC)	little
`csv-sum`	implement `sum_amount`	a malformed row must not crash the sum (data loss)	little
`cache`	add caching to `compute`	(axis = correctness: caching must actually work)	`@lru_cache` vs a hand-rolled TTL class
`critic-email`	implement `is_valid_email`	a newline-injection address `ok@ok.com\n…` must be rejected (`re.match` anchors the start only)	the critique's own task #1 (#126)

The bad reference for each safety task is the lazy-but-plausible version: correct on the happy path, unsafe on the adversarial input. That is exactly the code a binary correctness gate passes.

Metrics

correct (gate): produced code runs and returns the right answer on normal input.
safe (gate): produced code survives the adversarial input. Deterministic, stdlib-only.
src_loc / src_files: over-engineering proxy. Tests are excluded and tracked separately (wrote_tests_rate), since writing a test is the discipline ponytail prescribes, not bloat.
cost / duration / turns: straight from the Claude Code CLI JSON.

Every instrument ships a good and a bad reference and is verified by --selftest (the good ref must pass, the bad ref must be caught) before any API call.

Over-engineering judge (`judge.py`)

Over-engineering is the one axis that resists a deterministic check, so it gets an LLM judge, made auditable: a fixed model (claude-sonnet-4-6) at temperature 0, a published rubric, and every score must name the specific construct it considers unnecessary (or "none"). It scores the source files only (tests excluded). Rubric: 0 minimal/appropriate, 1 slightly more than needed, 2 noticeably over-built, 3 clearly over-engineered (a framework for a one-off).

The judge is itself validated by judge.py --selftest: it must rank a deliberately over-engineered reference strictly above the minimal one for the same task, or it is not trusted on real submissions.

python judge.py --selftest            # validate the judge (small spend)
python judge.py --run runs/<stamp>    # score every workspace's source

Completeness judge (`complete.py`)

Fewer lines only counts as a win if the code still does the job. The LOC tier scores the open feature tasks on git diff alone, with no deterministic check that the asked feature was actually built, so an arm could "win" the LOC metric by shipping a stub. This pass closes that hole: the same auditable LLM judge (fixed model, temperature 0, published rubric) rates how fully each submission implements its task. Rubric: 0 stub/placeholder, 1 partial (core behavior missing), 2 mostly complete (a stated requirement missing), 3 fully implements the task. Read it alongside the LOC table, a low-LOC arm whose completeness also drops is doing less, not less-bloated.

Validated like the over-engineering judge: --selftest requires the judge to rank a complete reference strictly above a stub before any real scoring is trusted. --selftest-offline checks the gate logic with no API call (no key needed).

python complete.py --selftest-offline  # validate the gate logic, no API
python complete.py --selftest          # validate the judge (small spend)
python complete.py --run runs/<stamp>  # completeness-score every workspace

Reproduce

Needs the claude CLI (this is the harness, no SDK), Python 3, an authenticated Claude Code, and a clone of the template at the pinned commit (set PONYTAIL_TMPL to its path, or drop it at fixtures/full-stack-fastapi-template):

git clone https://github.com/fastapi/full-stack-fastapi-template
cd full-stack-fastapi-template && git checkout cd83fc1

python run.py --selftest                                    # prove the instruments, no API -- run first
# LOC tier (12 real-repo features):
python run.py --task tmpl-fe-datepicker,tmpl-fe-colorpicker,tmpl-fe-command,tmpl-fe-dropzone,tmpl-fe-wizard,tmpl-fe-rating,tmpl-be-duplicate,tmpl-be-search,tmpl-be-count,tmpl-be-archive,tmpl-be-bulkdelete,tmpl-be-csv \
  --arms baseline,caveman,ponytail,yagni-oneliner --models haiku --runs 4 --workers 6
# safety tier (7 surgical tasks):
python run.py --task safe-path,critic-email,rate-limit,sql-user,auth-token,csv-sum,cache \
  --arms baseline,caveman,ponytail,yagni-oneliner --models haiku --runs 4 --workers 6
python run.py --rescore runs/<stamp>                        # recompute metrics offline, no API

Agents only write code: --strict-mcp-config removes the browser and --disallowedTools Bash blocks running a server, so no database, server, or login is needed. The LOC tier measures the git diff; the safety scorer executes the produced function in-process. Each cell runs bypassPermissions in its own fresh repo copy under runs/<stamp>/ (gitignored, kept). --workers N runs N isolated cells concurrently. Because workspaces are preserved, any metric change is re-applied offline with --rescore, you never pay the API twice for a measurement tweak.

What this can and cannot show

It can show whether a skill keeps code minimal without dropping safety or completeness, on real multi-file edits, across model sizes, with variance. Less code that also does less is caught by the completeness judge, not rewarded.
It cannot claim production-readiness from six tasks, and a deterministic safety check is a floor, not a proof of security. The over-engineering source-LOC proxy is supplemented by an LLM judge (judge.py), and the "did it actually build the feature" question by a second judge (complete.py).
If the arms converge (everyone safe, similar size), the benchmark says so. It is built to be able to disprove the skill's value, not only to confirm it.

Results

2026-06-18, Haiku 4.5, n=4. Two tiers:

12 real-repo features (LOC via git diff): ponytail cuts 60–94% on features with an over-build trap (date picker 404→23, color picker 287→23, dropzone 251→95) and is a wash on irreducible code (backend CRUD). It never writes more. Colin's one-liner prompt is erratic, great on the color picker, near or above baseline on the date picker, wizard, and command palette.
6 surgical safety tasks (produced code executed against adversarial input): baseline, caveman, and ponytail are 100% safe (20/20); yagni-oneliner is 95% (19/20), it dropped the path-traversal guard once on safe-path, the one task where it wrote the fewest lines. The lines it cut were the guard.

Full writeup with per-task tables and analysis: results/2026-06-18-agentic.md.

The earlier results/2026-06-17-agentic-safety.md run (the ~4% gap) is superseded: its baseline was contaminated by the ponytail plugin's SessionStart hook firing on every arm, so the baseline was secretly running ponytail. Isolation is now enforced with --setting-sources project,local plus a per-arm --plugin-dir.

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Agentic benchmark