Borrowing it
Nothing to install: this file belongs to fwornle/coding. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fwornle/coding/main/.claude/commands/experiment.mdgit clone --depth 1 https://github.com/fwornle/codingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/fwornle/coding/experiment)<a href="https://agentmods.dev/commands/fwornle/coding/experiment"><img src="https://agentmods.dev/badge/commands/fwornle/coding/experiment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/fwornle/coding/experiment"><img src="https://agentmods.dev/badge/commands/fwornle/coding/experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.05964 |
| Opus 5.5 | $0.00000 | $0.02386 |
| Sonnet 5.5 | $0.00000 | $0.01193 |
| Haiku 4.5 | $0.00000 | $0.00596 |
Grade A, and why
experiment scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
if ! curl -sf -m 3 http://localhost:3032 >/dev/null 2>&1; then How it starts
The opening of the file, as written. The whole thing — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment (/experiment) — describe → run → compare in one command
Wrap the full cross-agent experiment flow as a single operator command: synthesize a spec (from a plain-English description or from headline flags), run the whole matrix, then auto-compare the variants and print the ranked table (and write the report JSON the dashboard Comparison tab reads live).
Two ways to invoke — same pipeline (Step 0 chooses):
- Natural language (default): just describe the experiment, e.g.
/experiment compare Claude Sonnet against OpenCode Haiku on writing a fizzbuzz function, run each twice. The skill synthesizes the spec, shows you a preview + drafted test, and asks you to confirm before spending tokens. - Structured flags (power users):
/experiment run --goal "…" --variants A,B --agents claude,opencode --repeats N.
This skill is a THIN wrapper (D-09). It shells to three existing CLIs and reimplements NO runner, serialization, or comparison logic:
scripts/experiment-write-spec.mjs— builds, validates, and persists the spec YAML toconfig/experiments/gen-<id>.yaml(so it lists in the dashboard Launch listbox + is re-launchable).scripts/experiment-run.mjs— runs the matrix (spec-file driven; RUN-02/03/04, Phase 78).scripts/experiments-compare.mjs— aggregates + ranks the Runs and writes the report JSON.
The ONLY skill-side computation is re-deriving the task_hash (the sha256 the runner itself
computes at close) so the run→compare handoff is mechanically closed — no manual lookup, no
scraping of runner stdout.
DO NOT pre-solve the task. The analysis IS the experiment. The whole point is to compare how each agent independently investigates and solves the task, in its own isolated sandbox. The orchestrator's job is to turn the user's description into a
GOAL_SENTENCEand run the matrix — never to read the codebase, locate the bug, design the fix, or otherwise do the work before running. Doing so contaminates the comparison (you'd be feeding one agent's answer to all cells) and defeats the purpose. Write the goal from symptoms + the observable deliverable the user described (e.g. "the REC indicator wrongly toggles between black and green — make it stable"), optionally with a one-line orienting pointer the user themselves gave, and let each cell do its own analysis. If you catch yourself grepping the target code to "craft a better goal," stop — that is the cells' job.
Parallel is the default execution mode. Run cells concurrently (Step 4) unless the user explicitly asks for serial/sequential execution, or the matrix includes an agent whose tokens must be measured and that agent is ambient-slot-bound (opencode — see Step 4). Only then fall back to serial.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed 120632d255df
- 1mo ago First seen · 372 lines · 0 tokens per session scan A 5c3dc10bd259
experiment is a command published in the GitHub repository fwornle/coding (2 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,964 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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