holdfast 0.4.0

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

Holdfast

Governed evolution for LLM and agentic systems.

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 to improve the route while guaranteeing the destination doesn't change. Built for teams running LLM pipelines, agentic workflows, and prompt-driven systems that need to improve without regressing.

Built for Claude Code & Python pipelines

Claude Code — no Python required

Install the holdfast skill and it works out of the box. No Python library needed.

Use Claude Code the way you already do — code reviews, test generation, refactoring, planning. Holdfast tracks your interactions, detects when quality drifts, and proposes improvements to Claude's own approach.

• Discover — holdfast notices repeatable tasks and offers to track them
• Track — after each tracked interaction, holdfast silently logs evidence (outcome, quality, what worked)
• Interpret — ask Claude what's drifting and it reads the accumulated evidence, runs detection, explains patterns
• Evolve — Claude proposes a targeted change to its own approach, you approve, the approach improves

Tell Claude to start tracking:

"Use holdfast to track my code reviews."

Or add it to your CLAUDE.md:

Use holdfast to track the quality of code reviews in this project.

Claude sets up the contract, starts logging evidence silently, and surfaces insights when patterns emerge.

Python pipelines

The Python library instruments your pipeline and collects evidence. Wire @track into your functions, and holdfast logs every run, validates against invariants, and detects drift — giving Claude (or your own LLM) the data it needs to propose improvements.

from holdfast import Contract, track

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

@track(contract)
async def classify(item: dict) -> dict:
    prompt = contract.get_evolvable("prompt")
    return await your_llm_call(prompt, item)

# Every call logs evidence. Pass/fail determined by invariant validation.
# Evolution proposals are backed by this accumulated evidence.

For CI or fully automated pipelines, propose_evolution() drives evolution programmatically without Claude Code.

What it looks like

A typical Claude Code session reviewing evidence across contracts:

You: How's my classifier doing?

Claude: Here's what the holdfast evidence shows:

  Classifier — English (847 runs)

  Drift alert (triggered): Mean confidence shifted from baseline 0.82 → 0.71
  (shift 0.11, threshold 0.10). Confidence is trending lower — the model is
  less certain on recent inputs. Could be input distribution shift or the
  prompt change from last week.

  Pass rate: Baseline (first 50) = 88% → Recent (last 50) = 94%. Accuracy
  improved despite lower confidence — the model is better calibrated now.

  1 volatile input category: product reviews with mixed sentiment (stdev 0.31,
  scores [0.9, 0.4, 0.6]). The rest are stable within ±0.1.

  Classifier — Spanish (312 runs)

  No alerts. Consistent output across all detection windows.

  Summarizer — Short Form (1,203 runs)

  Failure rate alert: 23% in last 50 runs (threshold 15%). Failures are all
  schema violations — summaries exceeding the 280-char limit. The model is
  producing longer outputs since the prompt was last evolved.

  Bottom line

  The classifier drift is minor — watch it over the next run cycle. The
  summarizer needs attention: the char limit failures are climbing.

You: Propose an evolution for the summarizer.

Claude: Based on runs 01180, 01187, 01192, 01195, 01201 — all over the char limit:

  Proposed change to evolvable/prompt.md:
  + Your summary MUST be under 280 characters. Count carefully.
  + If the input is complex, prioritize the key finding over completeness.

  This preserves the frozen output schema and targets the specific failure
  pattern. Want me to apply it?

You: Yes.

Claude: Applied evolution evo-00003 (v2 → v3). Previous version snapshotted —
  rollback to v2 anytime.

When to use holdfast

Holdfast fits any repeatable LLM or agent task that needs to improve over time without regressing — whether the output feeds downstream systems or is the end product itself.

Claude Code tasks (no Python needed):

• Code reviews — track whether reviews catch real bugs, flag false positives, miss security issues
• Test generation — track whether tests cover edge cases, catch real bugs, run without modification
• Planning and refactoring — track whether plans lead to successful implementations, whether refactors break things
• Documentation and auditing — track consistency and completeness over time

Python pipelines:

• Classification pipelines — sentiment, intent, severity. Structured output, clear schema, evidence accumulates fast.
• Extraction and summarization — pulling structured data from documents. Quality drifts as input distribution changes.
• Evaluation and scoring — rubric-based assessment, quality scoring. Numeric outputs where variance and drift matter.
• RAG pipelines — retrieval + generation. Output quality shifts as the corpus changes.
• Multi-model routing — switching between providers or models. Holdfast detects when one performs differently.

Not a fit:

• One-shot creative tasks with no repeatable structure
• Tasks where there's no definition of "good" to track against

Install

pip install holdfast

Install the Claude Code skill for interactive evolution:

# Via plugin (recommended)
/plugin add kevintelford/holdfast

# Or manually
mkdir -p ~/.claude/skills/holdfast
cp skills/holdfast/SKILL.md ~/.claude/skills/holdfast/SKILL.md

Quick start — Claude Code

Option A: Let Claude set it up. Just say "use holdfast to track my code reviews" and Claude creates the contract for you.

Option B: Use a template. Copy a starter template from templates/:

cp -r templates/claude-code-review holdfast/contracts/code-reviews

Option C: Set up a Python pipeline contract. See Python API quick start.

Once you have a contract, everything is conversational:

Check for patterns:

"What patterns do you see in my classifier contract?"

Propose an evolution:

"Evolve the classifier prompt based on the last 20 runs."

Check drift:

"Anything drifting in the classifier?"

Review history:

"Show me the evolution history for the classifier."

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

Quick start — Python API

The Python API handles contract setup, evidence logging, and can drive evolution programmatically for CI/automation.

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 programmatically

For auto mode or CI pipelines where you want evolution without interactive review:

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

  summary_prompt:
    path: "src/pipeline/prompts.py"
    symbol: "Prompts.SUMMARY_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

See Security considerations for custom script trust model.

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

Detection rules operate on a sliding window of recent evidence. Runs outside the window are ignored — they're not deleted, just not considered for detection. If you have 500 runs and a window: 100, only the most recent 100 runs are checked. Baseline windows (for drift) work the same way, looking at an earlier slice.

Contract patterns

For projects with multiple pipelines or variants, organize contracts by use case:

holdfast/
  contracts/
    classifier/
      en/             # English language classifier
      es/             # Spanish language classifier
    summarizer/
      short-form/     # Tweet-length summaries
      long-form/      # Executive summaries

Each contract gets its own evidence pool and detection rules. If multiple contracts share the same frozen schema and invariants, duplicate the files — contract inheritance is not yet supported.

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.

Security considerations

Custom invariant scripts

Custom scripts execute as the current user via subprocess.run() with full filesystem access. The only guard is a 30-second timeout. Script paths are validated to stay within the contract root (or project_root if set). This is fine for local development and CI where you control the scripts. Review any custom scripts before trusting third-party contracts.

Evidence data sensitivity

Evidence files (.holdfast/evidence/*.json) store the full output of each run in plaintext. Do not pass sensitive data — API keys, PII, auth tokens, credentials — through log_run() or @track. If your pipeline output contains sensitive fields, strip them before logging.

Evolution in auto mode

In auto mode, evolution proposals are applied without human review if invariants pass. The proposal content comes from an LLM and is written directly to evolvable files. Use monitor or semi-auto mode for contracts where unreviewed changes could have security impact. Reserve auto for low-risk contracts where invariant checks provide sufficient guardrails.

Prompt injection via evidence

Evolution proposals are generated by feeding accumulated evidence into an LLM prompt. If an attacker can control input_summary, notes, or output fields passed to log_run(), they could influence the LLM's proposal. Validate evidence inputs at your application boundary — holdfast logs what you give it.

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