Free model tiers are a practical way to generate documentation drafts without touching a budget, but they come with rate limits that break naive pipelines. The solution is not to chase higher quotas; the solution is to design the workflow around the constraint. This article shows a concrete "draft manifest" that declares which sections a model may generate, and a small Python script that enforces that declaration before any API call is made.
Disclosure: This article was prepared as part of MonkeyCode's product outreach. The workflow below was tested locally with simulated rate limits, and MonkeyCode's free model access and free server option are referenced where they fit the architecture, not as verified performance numbers.
Why Rate Limits Change the Documentation Calculus
When a model endpoint is free, it is usually shared, throttled, or both. A naive script that sends every section of a README to the model at once will hit a 429 mid-run, leave partial output in the working tree, and force a human to untangle which files were actually finished. That failure pattern is worse than the original problem because documentation CI is supposed to reduce manual review, not create a new cleanup chore.
The key insight is to treat rate limits as metadata, not as an error. If each documentation section carries an ownership flag and a token estimate, the pipeline can compute whether a given change fits inside the current quota window before calling the model. If the estimated cost exceeds the budget, the pipeline either defers non-critical sections or fails the draft step while leaving human-owned sections untouched.
Structuring a Draft Manifest
The manifest is a YAML file that lives in the repository, next to the docs directory. Each entry has three fields: a glob path, an owner that is either human or model, and a rough token multiplier that helps the budget calculator plan the call size. Sections owned by human are never sent to the model, regardless of how tempting it is to draft them, and the script refuses to generate them even when explicitly requested.
# docs/manifest.yaml
version: 1
budget:
max_requests_per_hour: 100
max_tokens_per_request: 4000
sections:
- glob: "guides/*.md"
owner: model
token_multiplier: 1.2
- glob: "api-reference/*.md"
owner: model
token_multiplier: 2.0
- glob: "README.md"
owner: human
- glob: "architecture/**"
owner: human
The token multiplier encodes reality: API reference pages with code blocks tend to consume more context, while short guides stay close to the raw text length. These values are heuristics, not exact measurements, and they should be tuned per repository over a week of runs.
A Budget-Checking Script That Fails Cleanly
The artifact below reads the manifest, validates that each changed file maps to a section, and computes the total estimated requests and tokens. It uses a simple sliding-window counter that you can adapt to the actual rate-limit headers from your provider. When a file is human-owned, the script raises an error before any network call is made, which is the whole point.
# docs/check_budget.py
from pathlib import Path
import yaml, sys, time
class BudgetViolation(Exception):
pass
class RateWindow:
def __init__(self, max_requests, max_tokens):
self.max_requests = max_requests
self.max_tokens = max_tokens
self.calls = [] # each: timestamp, tokens
def add_call(self, tokens):
now = time.monotonic()
self.calls = [c for c in self.calls if now - c[0] < 3600]
if len(self.calls) >= self.max_requests:
raise BudgetViolation("request quota exhausted")
if sum(t for _, t in self.calls) + tokens > self.max_tokens:
raise BudgetViolation("token quota exhausted")
self.calls.append((now, tokens))
def load_manifest(path):
with open(path) as f:
return yaml.safe_load(f)
def find_owner(manifest, file_path):
for section in manifest["sections"]:
if Path(file_path).match(section["glob"]):
return section
return None
def main(changed_files):
m = load_manifest("docs/manifest.yaml")
window = RateWindow(m["budget"]["max_requests_per_hour"],
m["budget"]["max_tokens_per_request"])
for f in changed_files:
section = find_owner(m, f)
if section is None:
continue
if section["owner"] == "human":
raise BudgetViolation(f"{f} is human-owned, no draft allowed")
text = Path(f).read_text()
tokens = len(text.split()) * section.get("token_multiplier", 1.0)
window.add_call(int(tokens))
print("Budget check passed")
if __name__ == "__main__":
main(sys.argv[1:])
The script deliberately fails early. If you pass a human-owned file on the command line, you get an error before any external request is attempted, which means a tired developer cannot accidentally use the model to rewrite a compliance-critical section. That is a stronger guarantee than a code review because it is mechanical and runs on every machine.
Where MonkeyCode's Free Tier Fits
MonkeyCode offers free model access for draft generation, and its free server option can run a scheduled job that executes this budget check in the cloud rather than on your laptop. You can set up a cron-like trigger that watches a branch, runs the script, and then calls the model only for sections marked owner: model. The free server is useful here because the budget check is lightweight, but the subsequent generation can sleep and retry without holding a local terminal open.
That said, the script is provider-agnostic; the same workflow works with any model endpoint that gives you request and token limits in headers or settings. MonkeyCode just happens to remove the cost barrier for the low-value drafting work, which makes the human-ownership rule easier to enforce because you are not financially tempted to let the model take over more than it should.
A Practical Merge Workflow
The full cycle has four steps. First, a developer edits a human-owned section; the budget script runs on a pre-commit hook and blocks the commit if that file is not manually authored. Second, when a model-draftable file changes, the script calculates the estimated calls and, if the quota is available, sends the text to the free model endpoint. Third, a maintainer reviews the generated output in a pull request, editing it as needed; the manifest does not change. Fourth, the merge gate requires that every human-owned file has a review approval, while model-owned files only need a linter pass and a smoke test.
The most important part is neither the script nor the manifest; it is the decision to put the ownership boundary in version control. Once the boundary is code, it can be tested, reviewed, and changed deliberately. A free tier should be treated as a resource that encourages experimentation, not as a replacement for the engineering judgment that still belongs in the human lane.
Limitations and Who Should Not Use This
This workflow assumes the model endpoint is occasionally unavailable and that the budget is small enough to need a calculator. If you have a paid tier with generous concurrency, the script adds overhead for no benefit. It also assumes that token estimation from word count is good enough; if your docs contain heavy tables or code fences, the multiplier can drift, and you should log actual tokens and update the manifest monthly.
Do not use this approach for legally binding documentation, security-sensitive runbooks, or contract-language sections that require a named owner. The script only checks who can draft, not what was actually written; a human can still copy model output verbatim into a human-owned section, and no machine will notice. The manifest is a guardrail, not a substitute for a reviewer who reads the diff with care.
If you are already using a CI tool that has native rate-limit awareness, you may not need the Python script; the manifest alone can drive your existing pipeline. But for teams that want a zero-dependency, auditable way to keep free-tier models in their lane, the combination of a declared ownership map and a fail-closed budget check is hard to beat.
The free server option from MonkeyCode is one way to run this pipeline without dedicating a local machine, but the pattern itself is transferable. Start with a small docs folder, measure your actual rate-limit headers, and let the manifest grow with the sections you are confident a model can draft. That is the only durable answer to the question of what a model may write and what you must own.
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