project-context-v2

Project Context V2

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Evidence-first, long-term experiment memory for AI coding agents. Every claim stays traceable from question to evidence, every session resumable without archaeology.

Research projects rarely die because results are lost — they die because their context is: which config produced that number, why this baseline, what was invalidated, what was merely hypothesized. This skill turns an AI agent (Claude Code, ZCode, any skills-compatible agent) into a disciplined lab-notebook keeper: 10 operations, a controlled validity vocabulary, provenance gates, and read-only audits.

How it works

flowchart TB
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    init(["✦ init — adopt once"]):::seed --> startS

    subgraph loop["🔬 Per-session loop"]
        startS(["▶ start — load task-conditioned context"]):::seed
        frame["🎯 frame<br/>question · hypotheses · falsifiers"]:::op
        plan["📋 plan<br/>stable experiment ID · frozen protocol"]:::op
        record["🧾 record<br/>provenance gate · evidence before interpretation"]:::op
        endS(["🏁 End — journal handoff"]):::op
        startS --> frame --> plan --> record --> endS
        endS -. next session .-> startS
    end

    endS --> syn["📦 synthesize<br/>promote traceable observations to facts"]:::read

    subgraph guards["⚠️ Available anytime"]
        correct["🧊 correct<br/>freeze · invalidate · supersede"]:::gate
        doctor["🩺 doctor<br/>read-only structural + semantic audit"]:::gate
    end

    endS -. bug found .-> correct
    endS == close-out ==> doctor

    classDef seed fill:#EEF2FF,stroke:#6366F1,stroke-width:2px,color:#312E81;
    classDef op fill:#FFFFFF,stroke:#6366F1,stroke-width:1.5px,color:#1E1B4B;
    classDef read fill:#ECFDF5,stroke:#10B981,stroke-width:1.5px,color:#064E3B;
    classDef gate fill:#FFF7ED,stroke:#F59E0B,stroke-width:1.5px,color:#7C2D12;

How Jev audits your memory

flowchart TB
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    src["📄 Memory under audit — records · CURRENT.md · claims"]:::c1
    r1["1️⃣ doctor.py ｜ local regex · free<br/>structural: missing sections · stale · empty provenance"]:::c2
    r2["2️⃣ jev_doctor.py ｜ one batched Jev call<br/>provenance recoverable? · headline matches table? · observations clean? · claim support"]:::c3
    gate{"confidence gate"}
    ok(["✅ silent pass (p ≥ 0.5)"]):::okc
    warn(["⚠️ warning — high-confidence finding, confirm first"]):::warnc
    rev(["🔍 manual review — confidence < 0.5"]):::revc

    src --> r1 --> r2 --> gate
    gate --> ok & warn & rev

    classDef c1 fill:#EEF2FF,stroke:#6366F1,stroke-width:1.5px,color:#312E81;
    classDef c2 fill:#FFFFFF,stroke:#6366F1,stroke-width:1.5px,color:#1E1B4B;
    classDef c3 fill:#F5F3FF,stroke:#8B5CF6,stroke-width:1.5px,color:#4C1D95;
    classDef okc fill:#ECFDF5,stroke:#10B981,stroke-width:2px,color:#064E3B;
    classDef warnc fill:#FFF7ED,stroke:#F59E0B,stroke-width:2px,color:#7C2D12;
    classDef revc fill:#FFF1F2,stroke:#F43F5E,stroke-width:2px,color:#881337;

Why a decision model for audits? Every audit question is a small, closed-vocabulary judgment — exactly the shape a non-generative "System One" model is built for:

• One batched call per audit — all questions across all records go out in a single request (the quickstart triage measured 425 input tokens), instead of the main model re-reading every record in full.
• Calibrated confidence, free routing — every answer carries a probability distribution and a confidence score, so findings split automatically into silent pass / warning / manual review, with no prompt-engineering to squeeze uncertainty out of an LLM.
• Closed vocabulary, no invented findings — answers are constrained to the schema (supported / partially-supported / unsupported / invalidated), so the pre-screen can only flag, never fabricate.
• Consistent, loggable, tunable — the same schema runs every time: log Jev's verdicts next to your own reviews and tune thresholds per project.
• The main model remains the judge — Jev only triages; every warning and every low-confidence item routes to your review. Without a key, the same checks run on the main model (fallback above).

Operations

Install

Requires Python ≥ 3.10 for the scripts (zero third-party dependencies).

# via the skills CLI (project-level; add -g for global)
# interactive: you will be asked which of your agents to install to
npx skills add poiuyjie/jev_project_context
# non-interactive (CI, scripts): name the target agent explicitly
npx skills add poiuyjie/jev_project_context --agent claude-code -y

# or copy the folder into your agent's skills directory
git clone https://github.com/poiuyjie/jev_project_context ~/.agents/skills/project-context-v2

Then, inside your research project, tell your agent:

initialize research memory for this project

Daily use: "continue this project" (start), "record the results of E2026-0922-01" (record), "close the session" (end).

Optional Jev layers

Two scripts become active once TYPESAFE_API_KEY is configured. The simplest way is a .env file:

cp .env.example .env   # then paste your key from https://console.typesafe.ai/keys

jev_client.py loads .env from the current directory, the scripts/ directory, or the skill root — no dependencies, and real environment variables always take precedence. .env is git-ignored: never commit a real key.

With the key in place (see TypeSafe/Jev docs):

• jev_context.py — task-conditioned context triage for start: ranks experiment records, knowledge entries, protocols, and journals against the current task in one batched decision call, and prints a LOAD / SKIP manifest under a character budget. Relevance never hides staleness: SURFACE validity warnings (invalidated/superseded evidence) are printed even for skipped items.
• jev_doctor.py — semantic pre-screening for doctor / claim-audit / synthesize: provenance recoverability, headline-vs-table consistency, interpretation leaking into observations, and claim-support classification over the controlled vocabulary.

Both are read-only and advisory: findings are triage, never verdicts. Without a key (or on API failure) the scripts print a fallback note and exit 0 — nothing breaks, and every judgment falls back to your agent's main model: the skill's workflows instruct the agent to run the same semantic checks itself and to load context via the fixed reading order. This is a fallback, not a degradation — quality is backed by the main model (arguably stronger), only cost and latency return to the plain-LLM baseli