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Skillsworld-model-ledger

world-model-ledger

Coding & development

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Updated 10-15 21:09

About this skill

world-model-ledger

The key words MUST, MUST NOT, SHOULD, SHOULD NOT and MAY in this skill are to be interpreted as described in BCP 14 (RFC 2119, RFC 8174) when, and only when, they appear in all capitals.

A persistent world model for a coding agent, backed by SQLite. It records what the agent learns about a codebase — entities (symbols / files / modules / real-world referents), the interactions between them, and the constraints that should hold — and, for every interaction and constraint, how sure we are it exists, how sure we are it is correct, whether it has been validated, and what evidence backs it.

The defining rule, enforced as an invariant: code-observed relationships are not ground truth. A file sighting can drive observed_conf to 1.0 while normative_conf stays 0 and status stays unverified. Only oracle evidence — a passing test, CI, a doc, a human — raises normative confidence. See references/confidence-model.md for the epistemics and references/schema.md for the data model. The design rationale is in docs/superpowers/specs/2026-07-01-world-model-ledger-design.md.

Install & operate

Python interpreter. Most commands in this skill and its references are written with python3. Find the interpreter once, before the first of them. If $SKILL_PYTHON is set, use it. Otherwise, on macOS, if command -v python3 prints /usr/bin/python3 and xcode-select -p fails, that python3 is the install stub: skip it without running it. Use the first of python3, python and py -3 that is not skipped and for which <interpreter> -c 'import sys; sys.exit(sys.version_info < (3, 10))' exits 0. Write the interpreter you found in place of python3 in every command, and in any Bash(...) permission rule you suggest. Commands that start with uv run or {python} keep their own form. In a session resumed under a Bash(...) allowlist, skip this lookup and use the interpreter that the allowlist names. If none passes, follow this skill's own fallback for running without Python where it gives one; otherwise stop and tell the user that this skill needs Python 3.10 or newer.

Detect the install state first — before asking anything. The skill is already installed if ~/.claude/world-model-ledger/wm.py exists (GLOBAL) or ./wm.py + ./world_model.py exist in the project (PROJECT). Then route by how you were invoked; the full flag table is in references/parameters.md.

--seed / --prune — operate on an existing install (NO scope question, NO re-install)

If the skill is already installed and you were asked to seed or prune, do exactly that against the current repo and stop — do not run the full installer and do not ask which scope to use (that decision was already made at install time):

scripts/install.sh --seed     # build THIS repo's model (files + structural edges)
scripts/install.sh --prune    # build + soft-invalidate edges for deleted/renamed files

These short-circuit to python3 "$WM/wm.py" build . [--prune] (where $WM is . for a project install, else ~/.claude/world-model-ledger), create and gitignore .world-model/, and report entities_added / interactions_added. They are idempotent — safe to re-run anytime on an installed model.

First-time install (the ONLY path that chooses scope)

Only when the skill is not yet installed (or the user explicitly asks to (re)install), ask which scope, then run the installer once:

scripts/install.sh /path/to/project      # PROJECT scope — just this repo (default: cwd)
scripts/install.sh --global               # GLOBAL scope — every project, via ~/.claude
scripts/install.sh --with-constraints     # also load the optional starter constraint pack
scripts/install.sh --seed                 # install AND seed the repo in one go

Both modes are idempotent: they copy the core files (world_model.py, wm.py, harvest.py, test_world_model.py) plus hooks/, additively merge the four hooks into the right settings.json (existing hooks preserved), gitignore .world-model/, and run the 116-test suite as an install gate — the guarantees are only real if those pass. Requires python3 (stdlib only — no pip, no network) and, for clean settings merging, jq (falls back to writing settings.hooks.json for manual merge). After install, tell the user to restart Claude Code so the hooks load. You do not re-invoke this skill to install afterward — the hooks run automatically; --seed/--prune remain available anytime to (re)build the model. Installing this skill alongside the sibling context-hygiene-kit? The settings merges coexist, but two project-scoped installs into the same repo clobber each other's files — read references/interop.md first for the safe layouts, install order, and a joint-install verification checklist.

How it works — four hooks, three cadences

Hook Fires Job
PreToolUse → hooks/pretooluse.sh before an edit retrieve & summarize the model's ✓validated / ?unverified / ✗contradicted items for the touched file(s)/symbol(s); inject as context. Read-only.
PostToolUse → hooks/posttooluse-observe.sh after every tool call the universal observer: register the entities/edges the call reveals — files any tool reads/edits → entities; Bash → runtime executes/reads edges + verifier oracle (green→validated, red→contradicted); URLs → referents. Tool input only, not output; no invented facts.
Stop → hooks/stop.sh every turn the summarization home: harvest any optional markers from the trusted channel, consolidate (re-derive confidence + evaluate constraints), refresh .world-model/digest.md.
SessionStart → hooks/session_start.sh session start/resume auto-bootstrap the model on first run (create + seed .world-model/, no manual step), then inject the digest so a resumed session starts aware of contradictions.

Operating procedure — how the agent maintains the model

The model maintains itself: the universal PostToolUse hook captures entities and behavioural edges from every tool call automatically, and SessionStart auto-bootstraps the repo model on first run — no markers required, no env to set. The steps below are an optional precision layer: the agent may emit markers to add high-value facts the hooks can't infer (a semantic mapping, a validated oracle, a constraint), but nothing forces it to, and the model is useful with zero markers. A model never guesses facts inside a hook. Full conventions are in references/capture.md.

  1. (Usually automatic) Seed the model repo-wide. SessionStart auto-seeds a fresh repo, so this is normally already done. To (re)seed explicitly, run python3 wm.py build . to register every source file and its structural edges in one deterministic, language-aware pass — this avoids the cold start where the model is empty until the agent has touched files. Local imports become file→file edges (imports/includes/references) and external dependencies become file→referent depends_on edges, across Python, Ruby, JavaScript/TypeScript, Go, Rust, Java, C/C++, C#, PHP, Kotlin, Swift, Dart, Scala, Elixir, the DevOps stack (Dockerfile, docker-compose/K8s, GitHub Actions, Terraform, Make, shell), and dependency manifests (package.json, requirements.txt/pyproject.toml, go.mod, Cargo.toml, pom.xml/build.gradle, composer.json, Chart.yaml, .gitlab-ci.yml) for precise declared dependencies. Everything is recorded as observation only (observed_conf rises; normative_conf stays 0, unverified) — a declared dependency is a sighting, never a correctness judgement. Safe to re-run: writes are upserts on stable keys, so a re-build only adds new files/edges or refreshes existing ones — it does not duplicate or delete rows, or downgrade a fact you have already validated. Each run reports entities_added / interactions_added (both 0 on an unchanged repo). Add --prune to soft-invalidate build-origin edges for files you have since deleted or renamed (opt-in; it soft-invalidates rather than hard-deletes, and does not touch an edge the agent has observed or validated).
  2. (Optional) Record what you observe. The hooks already capture files, executions, and fetched URLs automatically. To add a relationship the hooks can't infer, emit a marker line — WM-OBSERVE: hash_pw uses bcrypt @ auth/hash.py:14 — or call python3 wm.py observe hash_pw uses bcrypt --evidence auth/hash.py:14. This raises observed confidence only; the fact stays unverified until an oracle backs it.
  3. Validate with evidence. When a test/doc/human confirms a relationship is correct, it is recorded with oracle evidence. Only test|ci|doc|human evidence raises normative confidence and flips status to validated. The WM-VALIDATED and WM-REFUTES markers are harvested only from the user's own message (the sanctioned route; a forged transcript is outside the threat model, see references/capture.md), for example WM-VALIDATED: hash_pw uses bcrypt by test:tests/test_auth.py::test_hash; the Stop hook rejects them from your turn. From your side, record a passing test or CI run with python3 wm.py validate "hash_pw,uses,bcrypt" --by test:tests/test_auth.py::test_hash. The sanctioned route for human evidence is the user's own WM-VALIDATED/WM-REFUTES … by human:<name>; wm validate and wm refute with --by human… exit 2, because the CLI runs with your authority.
  4. Assert constraints and map referents. Declare what should hold (WM-CONSTRAINT: no-weak-hash | forbids | uses | {"patterns":["md5","sha1"]} | …) and tie code to the reality it stands for (WM-MAPS: billing/refund.py -> stripe/refunds-api).

Detecting contradictions and proposing fixes

The Stop hook evaluates constraints and opens contradiction rows with a located, human-readable proposed fix (the least-entrenched member is the one to change). Review them with python3 wm.py contradictions --open; the pre-call hook also surfaces them for touched files. When you fix the code, resolve the row (wm resolve <id> --as fixed_code) and validate the corrected edge — unverified/contradicted mass converts to validated mass, so the model's normative correctness improves over time. The loop is detailed in references/contradiction-loop.md.

Output style. Write reports and explanations for the user in about 80% ASD-STE100 Simplified Technical English. Use short sentences, common words, active voice and one action per step.

The invariants (do not weaken these)

  1. Code observation MUST NOT raise normative confidence. This is the whole point: it lets the model say "observed, but unverified." A hook may set observed_conf high, but normative_conf moves only on oracle evidence.
  2. No invented facts in hooks. Hooks MUST capture only what is deterministically parseable from the trusted channel — files any tool names, executions parsed from a Bash command's argv, verifier exit status, fetched URLs, and explicit markers. Richer semantic interactions MUST come from the agent's own markers/CLI and MUST NOT come from a model summarizing inside a hook, because a summary is a guess and a hook writes what it captures straight into the ledger.
  3. Trusted channel only. tool_result / tool_use content MUST NOT be harvested into facts or evidence, so untrusted output cannot forge a marker. The two tags that raise the ORACLE axis (WM-VALIDATED/WM-REFUTES) MUST NOT be accepted from any channel but the user's — an agent quoting a poisoned file back into its own reply MUST NOT be able to validate a fact. The wm CLI runs with the agent's authority, so wm validate and wm refute MUST refuse a human evidence kind and constraint --assert-valid MUST record agent_assert. Regression tests guard the direct path, the echo path and the CLI path.
  4. Append-only evidence: evidence MUST be soft-invalidated, not hard-deleted. Superseded facts get invalidated_at; confidence is always derived from live evidence, so the audit trail and the score cannot drift apart.
  5. Every triple MUST be ontology-checked before it enters the ledger. Predicates are a closed, deliberately-extensible vocabulary with RDFS-style domain/range per verb — a hallucinated verb or a semantically impossible pairing (a referent that imports a file) MUST be rejected with the allowed set named, not silently stored. Markers that violate it are skipped (hooks never break); CLI writes get a structured, self-correctable error. Extensions MUST go through wm ontology --add, not happen as a side effect of a marker. See references/ontology.md.

When a situation genuinely needs an exception to an invariant, you MUST surface it to the user rather than silently working around it.

Verifying after install

You MUST confirm the gate passed: python3 test_world_model.py (116 tests — the two-axis invariant, noisy-OR derivation, soft-invalidation, contradiction detect + propose, trust boundary, idempotent ingest, referent mapping, cycle-safe recursive-CTE traversal, repo-wide build seeding, measurable improvement). If any fail, the guarantees above do not hold — fix before relying on the model. Inspect live state anytime with python3 wm.py stats and cat .world-model/digest.md.

References

  • references/confidence-model.md — the two-axis epistemics and the derivation formulas.
  • references/schema.md — the SQLite data model and query patterns.
  • references/capture.md — markers, the wm CLI, and the trust boundary.
  • references/ontology.md — the predicate vocabulary (domain/range guardrails), rejection behavior, and how to extend it deliberately.
  • references/contradiction-loop.md — detect → propose → improve, and the constraint kinds.
  • references/parameters.md — every install/operate flag and wm command, and when to use each.
  • references/interop.md — running this skill alongside context-hygiene-kit (merge behavior, safe layouts, joint verification).

Assets

  • assets/world_model.py — the store: schema, confidence derivation, contradiction detection, graph/FTS retrieval, and the wm CLI.
  • assets/wm.py — friendly CLI entrypoint (thin wrapper).
  • assets/harvest.py — the deterministic marker harvester (trusted channel only).
  • assets/hooks/ — the four lifecycle hook scripts.
  • assets/starter_constraints.json — the optional starter constraint pack (off by default).
  • assets/test_world_model.py — the 116-test install gate.
  • scripts/install.sh — project / global installer with additive settings merge.