fscars 0.3.0

Bolt-on correction primitive for AI coding agents.

Stop explaining the same fix every session. Make corrections persist.

Status: alpha (v0.3.0). Install with pip install fscars. Core engine, Claude Code and Codex (instruction-mode) adapters, 5 starter scars, and the validation layers (fscars.validation) — a three-tier loop for turning observations into auditable outcomes — are working. Native Codex hook blocking remains on the roadmap. Read CHANGELOG.md for the current state.

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Why this exists

A junior engineer reads the textbooks and learns the fundamentals — that is the floor. What turns the junior into a senior is the weight that mistakes leave behind: the migration that ran half-applied in production, the timezone bug that shipped to a customer, the build that broke at 2am. Those scars become heavier than any chapter of the book; they bend future decisions in a way pure knowledge cannot.

AI coding agents come into your project with a strong prior — billions of tokens of training, especially on code. But the way your assistant behaves on your codebase is not just that prior; it is shaped by every correction you make along the way. The catch is that those corrections rarely survive: the next session starts from training again, and the model regresses to its statistical default in any area where the correction carries less weight than the prior. A functional scar is the anchor that gives your correction enough weight to bend the next decision.

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What is a Functional Scar?

A scar is what an operator's correction becomes when you make it deterministic. Not text presented to the model — code that runs outside the model, intercepts the moment of risk, and pushes back.

	System prompt	Memory / KB	Hook	Functional Scar
Where does the rule live?	In context	In context	In code outside the model	In code outside the model
Does the model decide whether it applies?	Yes	Yes	No	No
Does it survive /compact?	Partial	Yes	Yes	Yes
Does it learn from its own fires?	No	No	Manual	Yes
Built directly from a real correction?	No	No	Manual	Yes — by design

Functional Scars complement memory and skills, they do not compete with them. The companion paper Lucy Syndrome in LLM Agents explains the underlying framework — five invariants that distinguish corrections that persist from those that decay.

This repository is the first installable implementation of those invariants.

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Quick start

pip install fscars            # v0.3.0 on PyPI
cd your-project
fscar init                    # creates .fscars/ + wires Claude Code
fscar init --adapter codex    # creates .fscars/ + writes Codex AGENTS.md guidance
fscar list                    # 5 starter scars come pre-installed

Three quick wins to try right away:

# 1) Web dev — kill timezone regressions in handler code
fscar list | grep utc-timestamps

# 2) Data science — require explicit UTF-8 in pandas.read_csv
fscar list | grep csv-encoding

# 3) Marketing copy — block "we don't do X" framing
fscar list | grep avoid-negative-framing

Once installed, every Claude Code tool call passes through the engine. When a scar matches, the engine emits an additionalContext reminder (or blocks the call when the scar is severity block) and writes one JSON line to .fscars/logs/fires.jsonl.

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Commands

Command	Description
fscar init	Initialize .fscars/ and register the hook entrypoint
fscar list	Show registered scars + fire counts
fscar log [-n N]	Show the most recent fires (filter by --scar, --session)
fscar stats	Compute fire counts, latency p50/p99, tokens added
fscar disable <scar_id>	Disable without deleting (use --enable to restore)
fscar doctor	Diagnose installation and hook wiring
fscar validate	Run Capa 4 deterministic rules over observed opportunities
fscar dashboard	Render markdown + HTML metrics from fires + opportunities
fscar audit	Validate + cross-link fires↔opportunities + render dashboard
fscar --version	Print the installed version

The hook entrypoint is python -m fscars.run_hook. Single command across every event type — no per-scar hook scripts. For Codex today, fscar init --adapter codex installs instruction-mode support in AGENTS.md plus .codex/fscars.json; native pre-tool blocking is reserved until Codex exposes a stable hook API.

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How it works

┌─────────────────────────────────────────────────────────────┐
│                          fscars.core                        │
│   payload · scar · engine · log · store · fire (Pydantic)   │
└──────────────────────┬──────────────────────────────────────┘
                       │
        ┌──────────────┴──────────────┐
        │  fscars.adapters/           │
        │   claude_code (v0.1)        │
        │   codex (instruction mode)  │
        │   cursor (community)        │
        └──────────────┬──────────────┘
                       │
                       ▼
       .claude/settings.json wired with one entrypoint:
              python -m fscars.run_hook

       Codex projects get AGENTS.md + .codex/fscars.json
       until native hook registration is stable upstream

The engine reads stdin, parses through the right adapter, dispatches to every matching scar, and emits the combined additionalContext plus exit code. A failure inside any scar is swallowed — the host harness must never crash because of fscars.

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Cookbook

cookbook/scars/ ships starter scars you can use directly or copy-paste:

File	What it does
large_write_review.py	Reminds the operator to self-review writes over 200 lines
utc_timestamps.py	Pushes back on time.Now() / new Date() in handler files
csv_encoding.py	Requires explicit encoding="utf-8" in pandas.read_csv
avoid_negative_framing.py	Blocks "we don't do X" patterns in marketing copy
subagent_coverage_report.py	Reminds the operator to ask subagents for a coverage report
import_aware_imports.py	AST-based detection of writes that import a watched package — see cookbook_import_aware.md
_template.py	Copy-paste starting point for new scars

See cookbook/scars/README.md for the contract and the 5-invariant checklist.

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Validation layers

Once you have observation in place, the next problem is precision: out of every hundred fires, how many actually prevented an error? fscars.validation is a three-tier loop developed in production during May 2026 that downgrades the labelling problem from "operator stares at thousands of rows" to "operator confirms an edge slice automation cannot resolve":

1. Capa 4 — deterministic rules per scar. Free, predictable, resolves most clearly-true and clearly-false opportunities.
2. Capa 3 — LLM classifier (subprocess to the local claude CLI) for what Capa 4 leaves ambiguous. Configurable threshold, parallel workers.
3. Capa 5 — cross-link the observed opportunities to actual hook fires so coverage stops being a proxy.

A fscars.dashboard module renders the resulting metrics as markdown + self-contained HTML; fscars.io.safe_jsonl guards concurrent pipeline writes with file-locked atomic merges.

The CLI shortcut for the common case:

fscar audit --classifiers myapp.scars:register --period 30d

Full architecture, examples, and the cross-link / outcome marker details: docs/advanced_validation.md.

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When NOT to use fscars

A scar only works when the correction satisfies the five invariants. If your fix is:

• Subjective ("I prefer tabs over spaces") — use .editorconfig or a linter.
• Proportional ("use async when it makes sense") — leave it to the model's judgment.
• One-off (the case has not repeated) — wait for the second occurrence first.
• Non-binary (cannot be checked deterministically) — keep it in your knowledge base.

These are the four cases the paper explicitly excludes. Adding a scar there creates noise without preventing anything.

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Platforms

Currently supported:

• Claude Code (Anthropic) — full adapter, all event types
• Codex CLI (OpenAI) — instruction-mode installer via AGENTS.md + .codex/fscars.json; see docs/codex_integration_plan.md

On the roadmap:

• Codex native hooks — deterministic pre-tool blocking once Codex hook stability is available upstream
• Cursor, Aider, Continue.dev — community adapters welcome

The core engine is platform-agnostic. Each adapter is a small glue layer (~300 LoC) that translates between platform-specific JSON shapes and the canonical HookPayload.

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The research behind this

Functional Scars is the reference implementation of the framework described in Lucy Syndrome in LLM Agents: A Practitioner Framework for Cross-Session Correction Persistence (Del Puerto, 2026). The paper analyzes 163 findings from 17 production session logs, identifies 5 persistence invariants, and proposes a 3-layer implementation model.

If you want the why, read the paper. If you want the how, you are in the right place.

The first derivative essay From Memory to Scar (May 2026) extends the four-layer progression with Anthropic's Managed Agents Memory beta as a working example of Layer 3 industrialized.

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Contributing

See CONTRIBUTING.md. New adapters and cookbook scars are especially welcome. Contributors and acknowledgments — including the Codex-authored Codex adapter — live in CONTRIBUTORS.md.

git clone https://github.com/Vdp89/fscars
cd fscars
pip install -e ".[dev]"
pytest -q
ruff check fscars cookbook tests

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License

Apache 2.0 — see LICENSE.

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_{Built on research first published as Lucy Syndrome in LLM Agents · companion repo}