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MirrorArc

LLM wiki kit

First committed by cz1993

Published in cz1993/MirrorArc. Publisher page

97 GitHub stars

What it is

Local-first, governed documentation for humans and AI agents—mirror Office, PDF, and GitHub sources into linked Markdown knowledge workspaces.
— the description by cz1993

Summary from the brain’s metadata

Local-first, governed documentation for humans and AI agents—mirror Office, PDF, and GitHub sources into linked Markdown knowledge workspaces. Preserve sources. Minimize derived documents. Generate knowledge views when they are needed.

How it works

Memory model: LLM wiki

AutoGineer overview, from the brain’s own files

Memory model: LLM wiki. Folders: .github (agent config, 3 files); docs (docs, 33 files); examples (examples, 139 files); scripts (scripts, 11 files); src (source code, 126 files); template (templates, 54 files); tests (tests, 57 files). The agent reads README.md, docs/PRODUCT.md. The agent writes CHANGELOG.md.

Folders
FolderRoleFiles
.githubagent-config3
docsdocs33
examplesexamples139
scriptsscripts11
srcsource-code126
templatetemplates54
teststests57
.githooksfolder1
clifolder1

The agent reads

The agent writes

Requirements

Apps: Obsidian

Runtimes: Python >=3.11

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)

Set it up

  1. This brain runs code automatically: 2 scripts. Review .githooks/pre-commit, scripts/init.sh before opening the folder in Claude Code. No step below turns a hook on for you.

    • .githooks/pre-commit: runs on git pre-commit
    • scripts/init.sh: sh scripts/init.sh
  2. Clone the repository at the commit AutoGineer scanned:

    git clone https://github.com/cz1993/MirrorArc.git && cd MirrorArc && git checkout 1df13ebf6efb33018e29a1ea365cde0cf53d41c7
  3. Point Claude Code at AGENTS.md: it does not read that file on its own.

  4. cd MirrorArc && claude

  5. AutoGineer scanned these files at commit 1df13eb.

  6. Not scanned by AutoGineerThe author’s README also describes installing it from a clone of its repository. Read its install section: AutoGineer did not scan that path. https://github.com/cz1993/MirrorArc/blob/1df13ebf6efb33018e29a1ea365cde0cf53d41c7/README.md#quick-start
  7. First committed by cz1993. Published in cz1993/MirrorArc. Licence: GNU Affero General Public License v3.0 only.

Usage

From the brain’s own files

  • Create a vault without cloning MirrorArc itself.
  • The installable command requires Python 3.11+; uvx and pipx run will build an isolated environment when they can find a compatible Python.
  • Before the package is published to PyPI, use the Git URL:
  • Once MirrorArc is published to PyPI, the equivalent short forms are uvx mirrorarc init --profile data-product ~/my-data-product and pipx run mirrorarc init --profile data-product ~/my-data-product.
  • For repeated local use, install the console command once:
  • Then open the vault in Obsidian if you want a human UI, point your agent at it (CLAUDE.md routes all agents to _meta/agent-rules.md), and run the installed command from any folder:
  • Run sandbox from a duplicated pilot vault, not the original document folder.
  • It is read-only and checks copy-boundary, mirror isolation, manifest/recovery readiness, and basic backup posture without printing source paths or document text.

Documentation by cz1993

MirrorArc

Image: Python 3.11+ Image: License: AGPL-3.0 Image: Status: technical alpha

Preserve sources. Minimize derived documents. Generate knowledge views when they are needed.

Open the live Ontario Grid demo portal →

MirrorArc is an open-source, local-first Python toolkit that turns changing Office files, PDFs, GitHub repositories, datasets, and Markdown into a governed knowledge-projection system. It keeps original records authoritative, creates deterministic Markdown projections for opaque sources, maintains source-backed relationships, and renders human or agent context without adding a vector database or an AI-generated wiki.

Image: MirrorArc beginner landing page showing the Ontario Electricity evidence tutorial

Why MirrorArc exists

Generative AI made documentation fast and cheap to create. It did not make a growing collection of summaries, entity pages, hubs, and agent notes cheap to verify or maintain. MirrorArc treats every durable derivative as a liability that must justify its existence; the information and knowledge carried by the sources remain the asset.

The product principle is:

Preserve authoritative records. Treat every durable derivative as a maintenance liability. Generate views instead of accumulating documents.

Common failureMirrorArc's response
Office files, PDFs, spreadsheets, repos, and Markdown stay isolated.L1 specular projections: opaque sources receive stable, source-addressed Markdown read models while originals remain authoritative.
AI documentation creates more files than anyone can govern.Anti-proliferation by architecture: relationships are data and synthesis is a view, not another note by default.
Cross-document meaning is hidden or asserted without evidence.Relationship and provenance ledger: typed connections carry evidence anchors, hashes, generation method, confidence, and review state.
Human readers need interpretation rather than raw extraction.L2 knowledge views: many sources produce a few purpose- and audience-specific reading surfaces that are ephemeral unless pinned or reviewed.
Source changes leave summaries and agent context stale.Dependency-aware refresh: changed sources invalidate only affected projections, relationships, views, reviews, and context.
Sensitive or stale material enters an opaque index.Visible governance: authority, licensing, PII boundaries, lifecycle, freshness, and publication gates remain explicit.

See the connected evidence

The self-contained Catalog Explorer provides a relationship map, a provenance and lifecycle inspector, and a rendered document view. When a governed repository is selected, an optional Code view adds revision-bound source evidence, call paths, impact, and affected-test candidates. The catalog panel is resizable, long filenames wrap instead of disappearing, and INDEX.md is pinned as the beginner guide.

Image: MirrorArc relationship map connecting an original Word document and its generated Markdown mirror

Image: MirrorArc metadata view showing provenance, authority, lifecycle state, and source-to-mirror relationships

Flagship demo: Ontario electricity evidence workspace

examples/ontario-electricity-evidence-vault/ is the flagship clean-room data-product example, built from OGL Ontario data, metadata-only public references, independently authored Word/Excel artifacts, and a synthetic repository fixture. The current snapshot demonstrates the implemented L1, lifecycle, catalog, and portal foundation. Its large hand-authored Markdown layer predates the knowledge-projection architecture and is now a migration fixture rather than the target output.

The local implementation supports source-addressed L1, evidence relationships, reviewed L2 views, dynamic/frozen context, and dependency invalidation. The checked-in example remains deliberately small. Historical stress tests are recorded separately; current batch proof is documented in the Trustworthy Context handoff when available.

The committed snapshot is an independently assembled educational corpus, not live grid data, forecasting, alerts, or an affiliated Ontario energy product.

Launch the hosted demo and start with the pinned INDEX.md five-minute tour. GitHub Pages rebuilds this content-enabled portal only from the provenance-documented public example and blocks deployment unless both the repository-wide provenance/no-data gate and the generated-artifact scan pass alongside vault lint.

The hosted project is progressively discoverable: the project overview identifies MirrorArc's purpose, differentiators, installation path, licence, and canonical GitHub source in semantic HTML. The demo's initial HTML contains the rendered beginner tour, while documents/ provides one semantic, no-JavaScript page per public record. Search and agent discovery surfaces include sitemap.xml, robots.txt, llms.txt, raw public Markdown, and a metadata-only catalog.json.

Try the demo locally

git clone https://github.com/cz1993/MirrorArc.git
cd MirrorArc
python3.11 -m venv .venv
. .venv/bin/activate
python -m pip install -e .

mirrorarc --root examples/ontario-electricity-evidence-vault sync
mirrorarc --root examples/ontario-electricity-evidence-vault code doctor
# If Ready, optionally analyze the synthetic repository fixture:
mirrorarc --root examples/ontario-electricity-evidence-vault code analyze --repo repo_252e1077b0082592ba3d --symbol demand_change
mirrorarc --root examples/ontario-electricity-evidence-vault catalog --html --include-content
python -m http.server 8000 --directory examples/ontario-electricity-evidence-vault

Open http://127.0.0.1:8000/CATALOG.html. Start with the pinned INDEX.md, then follow the five-minute tour. The --include-content output embeds bounded Markdown bodies and must remain local for private or proprietary vaults. The hosted MirrorArc demo is a narrow exception: it is built only from the public example corpus and scanned again before every deployment.

Choose a task and export its evidence

From a synced vault, choose a task, inspect what was selected and omitted, then freeze evidence:

mirrorarc --root <vault> context build --lens orientation --mode metadata --query "inspection interval" --json
mirrorarc --root <vault> context build --lens orientation --query "inspection interval" --name inspection-review
# Use the definition ID printed above:
mirrorarc --root <vault> context resolve <definition-id> --json
mirrorarc --root <vault> context freeze --definition <definition-id> --task "Explain the inspection interval with citations."
mirrorarc --root <vault> catalog --html

Query retrieval is opt-in: lexical ranking can miss relevant evidence. Inspect source hashes, exact spans, selected/excluded reasons, warnings and omissions before use. Metadata contains references only. Frozen packs include bounded evidence and report exact serialized bytes plus an estimated token count, not a model-independent token guarantee. A tiny budget can fail because instructions and citations alone do not fit. offline_complete is false if selected evidence is missing or truncated; it is never a guarantee that the sources answer your question.

The portable Catalog's selection download stays metadata-only. Its governed-context panel shows saved tasks, copyable commands and frozen status. HTML does not execute those commands. Use --include-content only when intentionally embedding local evidence; protect the resulting file according to its sensitivity. Source changes require refresh, context resolution, and Catalog regeneration. Frozen bytes remain unchanged and status reports staleness.

Review PDF page evidence

The optional PageIndex integration adds physical-page evidence and unreviewed answer candidates to the same governed workflow. Install its separate pinned runtime using the PageIndex setup and review guide, then start with model-free indexing:

mirrorarc --root /absolute/path/to/copied-vault document doctor
# Use a registered PDF source ID from mirrorarc status --json:
mirrorarc --root /absolute/path/to/copied-vault document index --source SOURCE_ID --context frozen --page 2
mirrorarc --root /absolute/path/to/copied-vault catalog --html --include-content

In the Catalog, select the PDF, then Document metadata → PDF page evidence. Optional questions require an approved model/endpoint and explicit per-command consent; indexing alone does not create an answer. Answer review shows frozen cited excerpts, current/stale state and uncertainty. A valid page address does not establish that a claim is supported. The local results separate tested behavior from real-model quality, owner acceptance and release readiness.

How it works

  1. Register authoritative sources without changing them.

  2. Project each opaque source into one stable, machine-owned L1 Markdown read model.

  3. Connect evidence through typed relationships with provenance and review state rather than creating more notes.

  4. Interpret many sources through a small number of purpose-specific L2 knowledge views.

  5. Assemble dynamic or frozen context for a human review or agent task.

  6. Refresh only affected projections and dependants when sources change, with reconciliation and full sync retained for recovery and verification.

The default data-product profile covers sources, contracts, pipelines, analysis, models, outputs, governance, and operations. Additional packaged profiles support business operations, research/learning, software-project documentation, and minimal blank starts.

Inspired by Andrej Karpathy's "LLM wiki" pattern. See docs/positioning.md for the product landscape and boundaries.

Quick start

Create a vault without cloning MirrorArc itself. The installable command requires Python 3.11+; uvx and pipx run will build an isolated environment when they can find a compatible Python. Before the package is published to PyPI, use the Git URL:

uvx --from git+https://github.com/cz1993/MirrorArc.git mirrorarc init --profile data-product ~/my-data-product

# or, with pipx:
pipx run --spec git+https://github.com/cz1993/MirrorArc.git mirrorarc init --profile data-product ~/my-data-product

Once MirrorArc is published to PyPI, the equivalent short forms are uvx mirrorarc init --profile data-product ~/my-data-product and pipx run mirrorarc init --profile data-product ~/my-data-product.

For repeated local use, install the console command once:

uv tool install git+https://github.com/cz1993/MirrorArc.git
mirrorarc --version

# or, with pipx:
pipx install git+https://github.com/cz1993/MirrorArc.git
mirrorarc --version

Then open the vault in Obsidian if you want a human UI, point your agent at it (CLAUDE.md routes all agents to _meta/agent-rules.md), and run the installed command from any folder:

mirrorarc --root ~/my-data-product doctor          # check dependencies and vault structure
mirrorarc --root ~/my-data-product plan            # inspect proposed mirror actions first
mirrorarc --root ~/my-data-product sync            # mirror Office files and configured repos
mirrorarc --root ~/my-data-product status          # review manifest-backed lifecycle state
mirrorarc --root ~/my-data-product sync --json     # machine-readable sync evidence for agents/pilots
mirrorarc --root ~/my-data-product status --json   # machine-readable lifecycle status
mirrorarc --root ~/my-data-product doctor --json   # machine-readable preflight report
mirrorarc --root ~/my-data-product conversion --guide   # read-only conversion spot-check + guide
mirrorarc --root ~/my-data-product conversion --init-results # private quality review scaffold
mirrorarc --root ~/my-data-product conversion --results _meta/conversion-quality-results.yml --require-reviewed # after filling scaffold
mirrorarc --root ~/my-data-product migration       # dry-run report for legacy/unknown folders
mirrorarc --root ~/my-data-product migration --runbook  # legacy folder move protocol
mirrorarc --root ~/my-data-product migration --normalize-frontmatter-domains --worksheet # review domain alias cleanup
mirrorarc --root ~/my-data-product recovery --worksheet # review manifest recovery actions
mirrorarc --root ~/my-data-product sandbox --source-root /path/to/original-documents
mirrorarc --root ~/my-data-product catalog         # generate CATALOG.md inventory gateway
mirrorarc --root ~/my-data-product catalog --html  # generate the interactive CATALOG.html explorer
mirrorarc --root ~/my-data-product catalog --html --include-content # local content-review portal
mirrorarc --root ~/my-data-product code doctor     # optional local repository-analysis readiness
mirrorarc --root ~/my-data-product code analyze --repo <repo-id> # bounded revision evidence
mirrorarc --root ~/my-data-product code status --json # current/stale/failed analysis state
mirrorarc --root ~/my-data-product m365            # Microsoft 365/Copilot handoff readiness
mirrorarc --root ~/my-data-product review --json   # summarize metadata-only human review decisions
mirrorarc --root ~/my-data-product overlap         # calibrate overlap thresholds without note bodies
mirrorarc --root ~/my-data-product pilot           # aggregate pilot evidence, no source content
mirrorarc --root ~/my-data-product pilot --worksheet    # redacted Markdown private-pilot summary
mirrorarc --root ~/my-data-product benchmark            # validate agent-readiness task pack, if present
mirrorarc --root ~/my-data-product benchmark --init-tasks    # create private task scaffold
mirrorarc --root ~/my-data-product benchmark --worksheet     # print private benchmark run sheet
mirrorarc --root ~/my-data-product benchmark --init-results  # create private result scaffold
mirrorarc --root ~/my-data-product benchmark --results _meta/agent-readiness-results.yml --require-prompt-safety # after scoring
# edit tools/repos.yml, then:
mirrorarc --root ~/my-data-product sync           # mirror configured GitHub repos too
mirrorarc --root ~/my-data-product lint           # health check

Run sandbox from a duplicated pilot vault, not the original document folder. It is read-only and checks copy-boundary, mirror isolation, manifest/recovery readiness, and basic backup posture without printing source paths or document text.

Use review after spot-checking mirrors, catalogs, or handoff reports. It appends metadata-only decisions to _meta/review-ledger.jsonl with artifact hashes, so later changes are reported as stale reviews instead of silently preserving old approvals.

The generated CATALOG.html is a self-contained Catalog Explorer. It opens on INDEX.md, offers separate relationship-map, document-metadata, rendered-document, and contextual Code views, preserves explicit original-to-mirror lineage, and keeps local context-pack exports metadata-only. Repository analysis runs before generation; the passive HTML never starts a process or fetches data. The safe default does not embed document bodies. Use catalog --html --include-content only for a local review copy; that opt-in mode embeds bounded Markdown and generated-mirror bodies, so the resulting HTML must be protected like the vault itself. The public GitHub Pages demo is built from the explicitly public, provenance-documented example and passes the no-data gate before deployment. Neither mode requires an application server or creates an evidence index.

Source checkout fallback:

git clone https://github.com/cz1993/MirrorArc.git mirrorarc && cd mirrorarc
python3.11 -m pip install -e .
mirrorarc --version
mirrorarc profile list
mirrorarc init --profile data-product ~/my-data-product
mirrorarc init --profile business-operations ~/my-business-vault
mirrorarc init --profile research-learning ~/my-research-vault
mirrorarc init --profile software-project ~/my-software-vault
mirrorarc init --profile blank ~/my-blank-vault
mirrorarc --root ~/my-data-product profile validate
mirrorarc --root ~/my-data-product profile diff 0.2.0
mirrorarc --root ~/my-data-product profile migrate --plan
mirrorarc --root ~/my-data-product profile migrate --write
mirrorarc --root ~/my-data-product profile views --check
mirrorarc --root ~/my-data-product migrate annotations --plan
mirrorarc --root ~/my-data-product plan

The packaged v1 profiles are data-product (the default), business-operations, research-learning, software-project, and blank. Each initializes from the installable package with profile-owned folders, _meta/profile.yml, generated scaffold docs, and only the templates declared by its contract.

Profile contract details: docs/PROFILE_SCHEMA.md.

Step-by-step: docs/quickstart.md.

Documentation

The whitepaper defines architectural principles; the current Trustworthy Context plan governs this local batch. Earlier implementation plans record historical evidence, not current acceptance.

  • Product contract — audience, workflow, outcomes, and non-goals.

  • Quickstart — demo-first setup and daily workflow.

  • Sync specification — authority, mirroring, manifests, and refresh behavior.

  • Security model — trust boundaries, prompt safety, and local-review rules.

  • Profile schema — configurable domains, note types, folders, and policies.

  • Methodology — L0/L1, relationships, L2, context, review, and promotion discipline.

  • Positioning — where MirrorArc fits relative to LLM wikis, RAG, PKM, and document-management tools.

Status

v0 - technical alpha. Source-preserving L1 mirrors, manifests, lifecycle state, audit logs, journaled changed-file materialization, reconciliation, full recovery sync, profiles, lint/safety guards, the portable catalog, stable projection identities, and the versioned evidence-backed relationship ledger work today.

Dependency invalidation, L2 knowledge views, dynamic/frozen context assembly, and optional revision-bound Repository Intelligence are implemented locally. The current Ontario example is retained as migration and regression evidence; do not interpret its curated Markdown count as the target MirrorArc model. This remains a technical alpha, and local proof is not hosted or production proof.

License

MirrorArc is licensed under the GNU Affero General Public License v3.0 or later for the open core, with a separate commercial license planned for enterprise/closed use and consulting. LICENSE contains the full AGPL-3.0 text; commercial terms and contribution policy still require owner/counsel finalization before accepting outside contributions. See LICENSING.md. "MirrorArc" is a trademark — see TRADEMARK.md.

Credits

The "LLM wiki" pattern (Andrej Karpathy), markitdown (Microsoft), and Obsidian. Interoperates with — rather than competes with — tools like basic-memory and Copilot for Obsidian.

docs/AGENT_READINESS_BENCHMARK.md

Agent-Readiness Benchmark

MirrorArc's future claim is not just that people can browse a cleaner knowledge base. The claim to prove is stronger:

AI agents should answer, reconcile, update, and audit operational knowledge more reliably from MirrorArc-generated markdown than from raw source folders or plain document-conversion dumps.

This document defines the benchmark shape for design partners. It is intentionally conservative: until these tasks are measured, "agent-ready markdown substrate" is a thesis, not a proven product claim.

Comparison Modes

Run the same task set against three modes:

  1. Raw source folder - originals only, such as Office files, PDFs, spreadsheets, decks, repos, and loose notes.

  2. Plain markitdown dump - one-off Markdown produced from the same convertible source files, with no manifest identity, lifecycle state, governed relationships, or refresh semantics.

  3. MirrorArc markdown - generated L1 mirrors, manifests, accepted evidence relationships, task selections, and governed context envelopes.

Do not mix evidence between modes during scoring.

For private external pilots, create the plain dump inside the copied pilot vault before scoring:

python3.11 /path/to/mirrorarc/scripts/create_plain_markitdown_dump.py \
  --root "$VW" \
  --force

The helper writes _benchmark/plain_markitdown_dump/ and _benchmark/plain_markitdown_dump-summary.json in the copied vault. Those artifacts may contain source-derived text and private relative paths, so keep them with private pilot evidence and never commit them to this public repository.

Task Families

Use fixed tasks that reflect real operator and agent work:

FamilyExample taskWhat good looks like
Answer"What steps are required before GST/HST registration?"Correct answer with source-backed citations and caveats
Reconcile"Which documents disagree about eligibility or dates?"Finds conflicts and points to specific files/sections
Update"A source changed; which projections or contexts need refresh?"Identifies stale mirrors/notes without rewriting originals
Audit"Show evidence for this recommendation."Produces traceable source path, mirror path, and manifest context
Consolidate"Where should this new fact live?"Preserves originals and consolidates unnecessary derived views

Metrics

Track both outcome quality and operating cost:

  • answer correctness;

  • citation/source-path accuracy;

  • missed caveats or unsupported claims;

  • stale-source detection;

  • duplicate-note avoidance;

  • manual reviewer correction count;

  • time to acceptable answer;

  • token/tool-call count if available;

  • operator confidence score;

  • privacy/provenance violations;

  • prompt-safety review completion and prompt-safety violations.

Scoring

Use a simple 0-2 score for each task:

  • 0 - wrong, uncited, unsafe, or not actionable;

  • 1 - partially correct but missing caveats, citations, or update/audit evidence;

  • 2 - correct, source-backed, and operationally useful.

MirrorArc should not claim agent-readiness superiority unless the markdown mode improves total score, reduces correction effort, or improves auditability across multiple corpora.

Required Evidence

For each benchmark run, keep:

  • corpus description and file counts;

  • supported/unsupported source counts;

  • task prompts;

  • final agent answers;

  • cited source and mirror paths;

  • reviewer corrections;

  • timing and token/tool-call notes where available;

  • lint and sync status output;

  • no-data confirmation that no private corpus evidence is committed to this repository.

Result Pack

Task packs define what to run. Result packs summarize what happened after an agent or operator runs those tasks in each comparison mode. Keep result packs in the private pilot vault or an anonymized review packet; do not commit confidential answers, protected names, source text, mirror text, or reviewer notes to this public repository.

The public MirrorArc repository rejects committed _meta/agent-readiness-tasks.yml and _meta/agent-readiness-results.yml files by default. Store task and result packs in private pilot workspaces, then copy only aggregate numbers into a review packet after no-data review.

Default local path:

_meta/agent-readiness-results.yml

Minimal schema:

schema_version: 1
corpus: private-example-vault
results:
  - task_id: answer-source-question
    mode: mirrorarc_markdown
    score: 2
    reviewer_corrections: 0
    elapsed_seconds: 95
    cited_source_paths:
      - 20_sources/example-source.docx
    cited_generated_mirror_paths:
      - _mirrors/20_sources/example-source.md
    privacy_or_provenance_violation: false
    prompt_safety_reviewed: true
    prompt_safety_violation: false

Rules enforced by the validator:

  • task_id must exist in the task pack;

  • mode must be one of raw_source_folder, plain_markitdown_dump, or mirrorarc_markdown;

  • score must be 0, 1, or 2;

  • reviewer corrections must be a non-negative integer;

  • elapsed seconds, when present, must be finite and non-negative;

  • citation paths must be relative vault paths;

  • cited paths must exist in the current vault copy;

  • cited paths must be declared on the referenced task;

  • source citations must not point into _mirrors/;

  • generated mirror citations must point into _mirrors/;

  • scored results without at least one valid declared source or mirror citation are warnings by default and fail when --require-citations is used;

  • prompt_safety_reviewed and prompt_safety_violation, when present, must be booleans;

  • missing prompt-safety review fields are warnings by default and fail when --require-prompt-safety is used;

  • recorded prompt-safety violations fail when --require-prompt-safety is used;

  • unsupported top-level or per-result fields are rejected so answer text and reviewer notes are not stored in result packs;

  • --require-results fails unless every task has a score for every comparison mode.

Validate and summarize results with:

python3.11 tools/mirrorarc.py benchmark --init-tasks
python3.11 tools/mirrorarc.py benchmark --worksheet
python3.11 tools/mirrorarc.py benchmark --init-results
python3.11 tools/mirrorarc.py benchmark --results _meta/agent-readiness-results.yml
python3.11 tools/mirrorarc.py benchmark --results _meta/agent-readiness-results.yml --require-results
python3.11 tools/mirrorarc.py benchmark --results _meta/agent-readiness-results.yml --require-citations
python3.11 tools/mirrorarc.py benchmark --results _meta/agent-readiness-results.yml --require-prompt-safety
python3.11 tools/mirrorarc.py benchmark --results _meta/agent-readiness-results.yml --json

--init-tasks creates a private _meta/agent-readiness-tasks.yml scaffold from synced source manifest metadata. It references relative source and generated-mirror paths only; it does not read or copy source text, mirror text, answers, or reviewer notes. Treat the scaffold as a starting point: edit prompts, success criteria, and selected paths before scoring a real pilot.

--worksheet prints a private Markdown run sheet from the task pack. It includes task prompts, success criteria, evidence-reference counts, scoring guidance, and per-mode scoring fields, but it does not print source paths, mirror paths, source text, answer text, or reviewer notes.

--init-results creates a private _meta/agent-readiness-results.yml scaffold with one entry for every task and comparison mode. It leaves scores and correction counts as null so an untouched scaffold does not pass validation as real evidence. Use --force only when intentionally replacing a prior private result pack.

The human-readable report prints aggregate per-mode scores, correction counts, privacy/provenance violation counts, citation counts, uncited scored-result counts, and prompt-safety review/violation counts. It does not print answer text, reviewer notes, source text, mirror text, or document bodies.

Task Packs

Task and result packs are intentionally not committed in the flagship example. Create them in a copied vault or private workspace, and keep prompts, answers, reviewer notes, and corpus-specific evaluation details outside this public repository.

Validate a configured task pack with:

python3.11 tools/mirrorarc.py benchmark
python3.11 tools/mirrorarc.py benchmark --require-generated  # after sync

Validate a private result packet with:

python3.11 tools/mirrorarc.py benchmark --results _meta/public-agent-readiness-results.yml --require-results --require-citations --require-prompt-safety

Messy Corpus Generator

For the larger synthetic baseline required by the review plan, generate a 200-file corpus outside the source checkout:

python3.11 scripts/generate_messy_benchmark_corpus.py \
  --target /tmp/mirrorarc-messy-benchmark \
  --files 200

The generated vault includes synthetic Office-like sources, authoritative Markdown records, a plain conversion dump under _benchmark/plain_markitdown_dump/, a benchmark task pack, and a private result scaffold. Run the same agent against all three modes before publishing aggregate scores.

For the checked-in messy synthetic dogfood aggregate, add --write-reviewed-results, sync the generated vault, then validate _benchmark/agent-readiness-results-reviewed.yml with --require-results, --require-citations, and --require-prompt-safety. This reviewed packet is synthetic score evidence only; it must not be described as external design-partner validation.

Guardrails

  • Never benchmark with confidential client data inside this public repo.

  • Keep one client or engagement per vault.

  • Treat generated markdown as a working layer, not final authority.

  • Require citations to source-backed notes or original source paths.

  • Treat source and mirror text as untrusted evidence, not instructions; record prompt-safety review completion and violations in private result packs.

  • Do not let an agent delete, move, or consolidate records without explicit review.

Evidence Boundary

Synthetic dogfood runs can validate the protocol and generated corpus mechanics, but they are not proof that MirrorArc improves agent performance on a real private corpus. Publish only aggregate, non-sensitive results after independent review.

Compact Trustworthy Context pack

tests/fixtures/trustworthy_context/pack.json fixes 12 synthetic tasks across mixed text, conflicting/revised records, and the existing synthetic code fixture. IDs, expected source hashes, spans, answers, scoring rules, common prompt, file/export budget, and three held-out tasks were fixed before retrieval tuning. This text pack does not benchmark opaque conversion. W5 separately checks synthetic PDFs. A code source is wrapped as readable Markdown for registration; this input difference is recorded in the manifest.

mirrorarc benchmark --compact-pack tests/fixtures/trustworthy_context/pack.json --json
mirrorarc benchmark --compact-pack tests/fixtures/trustworthy_context/pack.json --task-retrieval --json

These commands run deterministic selection and export measurements, never model inference. Per-task results include expected/selected/included evidence, coverage, empty selection for insufficient evidence, latency, exact bytes and estimated tokens. Correctness, semantic citation accuracy, stale-evidence use, model abstention, prompt safety, and corrections remain null until actual model runs and human calibration exist. A successful deterministic run is not an empirical model pass or evidence of retrieval superiority. Raw and plain source-order baselines use the same file cap and fixed source revisions; no model has been run on any mode in this batch.

For empirical evaluation after endpoint/data/spend approval, use the manifest's common prompt and three repeats per task/mode with the same approved model/version and tool-free permissions. Retain actual responses, input hashes, latency and tokenizer/version counts outside the repository; human-calibrate the existing 0–2 rubric and record corrections and safety outcomes. Use the existing benchmark --tasks <private-task-pack> --results <private-result-pack> --require-results --require-citations --require-prompt-safety --json validator for the calibrated result pack. Do not turn the historical --write-reviewed-results generator's invented fixture scores into measured results. The compact run intentionally does not call that generator.

439 files · 404 not served here

Advantages

Limitations

  • No ZIP download: install it from its source (a rule-derived fact from the brain’s files)

Security

Risk level: Low risk

This brain runs

Git hooks (1)

  • .githooks/pre-commit (pre-commit): runs on git pre-commit

Agent instructions that run or fetch something (2)

  • AGENTS.md: Operator direction (2026-09-18): project development and validation run locally.
  • AGENTS.md: Every behavior change: run the package-owned vault lint through the example's tool shim, run the test suite, and update `CHANGELOG.md` and relevant docs.

Install scripts (1)

  • scripts/init.sh: sh scripts/init.sh
  • Low risk: scripts-present (.githooks/pre-commit)
  • Low risk: scripts-present (scripts/init.sh)

Attribution and licence

First committed by
GitHub avatar of cz1993cz1993
Declared authors
  • cz1993 (declared in pyproject.toml)
Publisher
Ci Zhu (Community)
Source
cz1993/MirrorArc · commit 1df13eb
Licence
GNU Affero General Public License v3.0 only (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
cz1993

AutoGineer is not affiliated with or endorsed by cz1993.

Install from source

404 of its files are not served here, so there is no ZIP. cz1993/MirrorArc at 1df13eb