holdfast 0.2.0

Governed evolution for prompts and skills — learn from runs, propose bounded improvements, stay within contracts.

Holdfast

Stable outcomes, smarter prompts.

The destination is fixed. The route gets better.

Holdfast separates what your downstream systems depend on (frozen) from how you deliver it (evolvable), then uses evidence from real usage to improve the route while guaranteeing the destination doesn't change.

How it works

1. Setup — define a contract: what's frozen (output schemas, formats), what's evolvable (prompts, examples), and what invariants must hold.
2. Instrument — log evidence from your pipeline with log_run() or the @track decorator (supports sync and async).
3. Monitor — detect drift, variance, and failure patterns across runs.
4. Evolve — propose bounded improvements to evolvable surfaces, backed by evidence. Claude Code (recommended) or programmatic via propose_evolution().

Human approval required in monitor and semi-auto modes. Nothing changes without evidence.

Install

pip install holdfast

Optionally install the Claude Code skill for interactive evolution:

# Personal — available across all projects
mkdir -p ~/.claude/skills/holdfast
cp skills/holdfast/SKILL.md ~/.claude/skills/holdfast/SKILL.md

# Or via plugin
/plugin add kevintelford/holdfast

Quick start

1. Create a contract

By convention, contracts live under holdfast/contracts/ in your project root:

holdfast/
  contracts/
    my-pipeline/
    ├── contract.yaml
    ├── frozen/
    │   └── output_schema.json
    ├── evolvable/
    │   └── prompt.md
    ├── invariants.yaml
    └── detection.yaml          # optional — pattern detection rules

contract.yaml:

name: my-pipeline
version: 1
evolution_mode: monitor     # monitor | semi-auto | auto

frozen:
  output_schema: "frozen/output_schema.json"

evolvable:
  prompt: "evolvable/prompt.md"

2. Log evidence

from holdfast import Contract, log_run

contract = Contract.load("holdfast/contracts/my-pipeline/")
prompt = contract.get_evolvable("prompt")

result = your_llm_call(prompt, data)

log_run(contract=contract, output=result, passed=validate(result))

Or use the decorator:

from holdfast import Contract, track

contract = Contract.load("holdfast/contracts/my-pipeline/")

@track(contract)
def classify(item: dict) -> dict:
    prompt = contract.get_evolvable("prompt")
    # ... your LLM call ...
    return result

# Each call logs evidence. Pass/fail determined by invariant validation.

The @track decorator also works with async functions:

@track(contract)
async def classify(item: dict) -> dict:
    ...

3. Monitor for patterns

python -m holdfast status holdfast/contracts/my-pipeline/
# Contract: my-pipeline (v1, mode: monitor)
# Evidence: 47 runs (42 passed, 5 failed)
# Alerts: 1 — score variance on 'score' (stddev=0.89)

Or in Python:

from holdfast import Contract, check_contract

contract = Contract.load("holdfast/contracts/my-pipeline/")
alerts = check_contract(contract)

4. Evolve

Interactively with Claude Code (recommended for monitor and semi-auto modes):

"Look at the evidence in holdfast/contracts/my-pipeline/ and propose an evolution."

The skill reads evidence, analyzes patterns, and proposes bounded edits to evolvable surfaces. Frozen surfaces are never touched. You approve before anything changes.

Programmatically (for auto mode or CI pipelines):

from holdfast import Contract, propose_evolution, apply_evolution

contract = Contract.load("holdfast/contracts/my-pipeline/")
proposal = propose_evolution(contract=contract, llm=my_llm_callable, min_runs=10)

if proposal:
    print(proposal.diff)
    print(proposal.rationale)
    apply_evolution(contract=contract, proposal=proposal)

5. Rollback if needed

from holdfast import Contract, rollback, list_versions

contract = Contract.load("holdfast/contracts/my-pipeline/")
versions = list_versions(contract)  # [1, 2, 3]
rollback(contract, to_version=2)

Evolvable references

Evolvable surfaces can reference standalone files or Python symbols in existing source files.

File references (default)

evolvable:
  prompt: "evolvable/prompt.md"

Reads and writes the entire file.

Source references (Python symbols)

Point directly at string constants in your source code — no need to extract prompts into separate files:

evolvable:
  system_prompt:
    path: "src/pipeline/prompts.py"
    symbol: "SYSTEM_PROMPT"              # module-level assignment

  maturity_prompt:
    path: "src/pipeline/prompts.py"
    symbol: "CyberPrompts.MATURITY_PROMPT"  # class attribute

Supports:

• Module-level assignments: PROMPT = "..." — symbol is "PROMPT"
• Class attributes: class Foo: PROMPT = "..." — symbol is "Foo.PROMPT"

Holdfast uses ast.parse() for extraction — no code execution. Write-back preserves all surrounding code and original quoting style.

Both formats can be mixed in the same contract. get_evolvable() returns the string value regardless of format.

Contracts

A contract separates outcome (frozen) from method (evolvable):

• Frozen surface: output schemas, response formats, scoring scales, coding standards. Protected.
• Evolvable surface: prompts, examples, reasoning instructions. Improves from evidence.
• Invariants (invariants.yaml): automated checks that must pass before and after changes.
• Detection rules (detection.yaml): pattern detection across runs (variance, drift, failure rate).

Evolution modes

Mode	Behavior
monitor	Detect and alert only. Default.
semi-auto	Detect, propose, human approves.
auto	Detect, propose, apply if invariants pass.

Set in contract.yaml as evolution_mode. Graduate when you trust the contract.

Invariant types

Type	What it checks
schema	JSON Schema validation against a frozen schema file
contains	Field value is one of the allowed values (scalar) or contains all required values (list)
custom	External Python script — passes output as JSON on stdin, checks exit code

Security note on custom scripts: Custom invariant scripts execute as the current user via subprocess.run() with full filesystem access. The only guard is a 30-second timeout. This is fine for local development and CI where you control the scripts. Review any custom scripts before trusting third-party contracts.

Detection rule types

Type	What it detects
variance	Field values vary too much within a window. Optional group_by to check per-group (e.g. per question).
drift	Field average shifted between baseline and recent windows
failure_rate	Too many failed runs in a window

Storage

Everything is flat files in .holdfast/ inside each contract directory:

.holdfast/
├── evidence/     # JSON files, one per run
└── versions/     # snapshots + evolution records

Human-readable, greppable, no database.

Gitignore

Add this to your project's .gitignore:

**/.holdfast/

Evidence and version snapshots are managed state, not source. They can get large with many runs. If you want to track them (e.g., for team review of evidence), remove this line — the files are plain JSON and git handles them fine.

Inspired by

Memento-Skills (Zhou et al., 2026) demonstrated that agents can improve by evolving external artifacts rather than retraining models. Holdfast applies that insight with governance — frozen contracts, invariant validation, audit trails, and graduated trust levels for enterprise pipelines.

License

MIT