simplicio-cli 0.2.0

Portable task-to-code pipeline that works with any LLM. Turn a one-line task into a verified code change — diff + test + verify loop. +54 pts on a 156-check benchmark vs raw prompting.

simplicio-cli

Turn a one-line task into a verified code change — with any LLM.

"hide the Delete button for non-admins" → diff + test + applied + verified. Works with OpenRouter, OpenAI, Anthropic, GLM, DeepSeek, Ollama — one env var.

pip install simplicio-cli

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Why it works — the numbers

Same model. Same task. Only the prompt changes. Measured, reproducible, deterministic.

Model	Without simplicio	With simplicio	Gain
Llama 3.1 8B Instruct	34%	98%	+64 pts
Gemma 3 12B IT	38%	94%	+56 pts
Qwen 2.5 7B Instruct	38%	80%	+42 pts
Average across 3 models · 10 cases · 156 checks	37%	91%	+54 pts (+145%)

Output-quality signals (rate across all 30 runs)

Signal	Raw prompt	With simplicio
DIFF block present	0%	100%
Target file mentioned	3%	96%
TEST block present	86%	93%

A 7B-parameter open model wrapped in simplicio's 6-layer contract outperforms the same model with raw prompting by 42 to 64 points. Without changing the model. Without fine-tuning. Without extra tokens at runtime worth mentioning.

Full report: bench/results.md · bench/results.pdf · raw outputs under .simplicio/bench_runs/.

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How it works

mapper        WHERE   project structure + latest state
precedent     HOW-1   the real snippet in THIS repo that already does it
skill-router  HOW-2   the ONE mapper skill that matches (ranked, not all)
simplicio     BUILD   stacks the 6 layers into one prompt (cache-friendly)
test          JUDGE   contract written as testable states
verify        PROOF   ran it — did it actually pass? loop-fix up to 3x

The idea in one line: don't ask the model to guess — hand it the path. Each layer terminates one decision the model would otherwise hallucinate. Relevant > complete — inject the right context, never all of it.

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Install

pip install simplicio-cli           # from PyPI
# or
pip install -e .                    # from this repo

Configure — any LLM, nothing hardcoded

Provider	SIMPLICIO_MODEL	SIMPLICIO_BASE_URL
OpenRouter	anthropic/claude-opus-4	https://openrouter.ai/api/v1
GLM (z.ai)	glm-4.6	https://api.z.ai/api/paas/v4
DeepSeek	deepseek-chat	https://api.deepseek.com
OpenAI	gpt-4.1	https://api.openai.com/v1
Local (Ollama)	llama3	http://localhost:11434/v1
Anthropic native	claude-opus-4-7	(leave unset)

If SIMPLICIO_BASE_URL is unset and the key is ANTHROPIC_API_KEY, it uses the native Anthropic SDK. Otherwise it uses an OpenAI-compatible client pointed at your base_url — so any OpenAI-like provider works without code changes.

simplicio smoke      # prints provider config + one test call

Use

# index once (caches embeddings; re-run after big changes)
simplicio index --stack angular

# run a task
simplicio task "hide Delete button for non-admins" \
  --stack angular \
  --alvo src/app/screen/screen.component.html \
  --criterios "- no admin perm: button absent from DOM
- with admin perm: button present" \
  --restricoes "- don't touch save flow
- build passes"

Each task: precedent (from cache) → skill match → 6 layers → LLM generates (diff + test + Playwright) → apply → run SIMPLICIO_TEST_CMD → pass? done : send the error back → fix → retry (up to 3x).

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Cache — why it doesn't re-map every time

Embeddings are keyed by content hash, stored in .simplicio/. Unchanged code block → vector reused. Change one file → only that block re-embeds.

Run	Blocks embedded	Time
1st (cold cache)	3	~baseline
2nd (no change)	0	~instant
after editing 1 file	1	partial

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Benchmark — reproduce in 30 seconds

OPENROUTER_API_KEY=… \
  BENCH_MODELS="qwen/qwen-2.5-7b-instruct,meta-llama/llama-3.1-8b-instruct,google/gemma-3-12b-it" \
  python3 bench/run_offline.py

No project required, stdlib only, deterministic regex scoring — no LLM judges the LLM. Each case runs twice on the same model: raw one-line objective vs simplicio's 6-layer contract. Outputs scored on target-file mention, DIFF block, TEST block, contract-state words. Full numbers in bench/results.md.

Full harness (your real project, your real tests)

simplicio bench --cases bench/cases.json --stack angular

Runs each case two ways and runs your real test command (e.g. ng test --watch=false) on each output. Writes the true pass-rate to bench/results.md.

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Plug points (stubs marked in code)

File	Replace with
prompt.py::_mapper	your real llm-project-mapper
pipeline.py::_aplicar_e_testar	extract diff → git apply → parse test result
skill_router.py	point SIMPLICIO_SKILLS_DIR at your mapper's skills

Layout

simplicio/
  cli.py          # index | task | bench | smoke
  cache.py        # content-hash embedding cache
  precedent.py    # grep + semantic rank (uses cache)
  skill_router.py # picks the ONE matching skill
  prompt.py       # stacks the 6 layers
  providers.py    # any OpenAI-compatible endpoint + Anthropic native
  pipeline.py     # generate → test → fix loop
  bench.py        # with-vs-without harness
  templates/simplicio_prompt.md
bench/
  run_offline.py  # stdlib-only multi-model benchmark
  cases.json      # your benchmark tasks
  cases_offline.json
  results.md      # filled by `simplicio bench` / `run_offline.py`
  charts/         # SVG: overall, delta, by_case, by_stack

License

MIT