v1.0.0 to v1.0.1

---

name: synthesis-text-provenance

description: >

Plan, record, and audit text provenance across hosted, local, and open-weight

model workflows. Use for text provenance, watermark capability checks,

local-model generation, reproducible AI-assistance records, mixed-authorship

lineage, text-integrity audits, authorized detector results, and claims about

what a provenance signal can or cannot prove. Do not use to defeat provider

marks, evade detectors, or disguise AI authorship.

license: Apache-2.0

depends_on: []

metadata:

author: Rajiv Pant

source_repo: "github.com/synthesisengineering/synthesis-skills"

source_type: "public"

---

# Synthesis Text Provenance

Build a reproducible account of how text was produced and changed. Keep four

judgments separate:

1. **Quality:** whether the prose is worth using.

2. **Style:** whether it contains slop or model-shaped patterns.

3. **Technical provenance:** whether a documented signal or credential is

present, absent, unverifiable, or unknown.

4. **Authorship:** who wrote or edited the text and which tools participated.

No result on one axis proves another. A detector miss does not prove human

authorship or watermark absence. Strong prose does not prove human authorship.

A provider mark does not identify which person submitted or edited the text.

## Trigger boundary

Use this skill when the request concerns:

- choosing between hosted and local/open-weight generation for provenance;

- preserving model, runtime, prompt, output, parameter, and edit lineage;

- checking current provider or standards documentation about text marking;

- recording an authorized detector result;

- inspecting invisible Unicode or normalization properties;

- making or reviewing a claim that text is marked, unmarked, AI-authored, or

human-authored.

Use [`synthesis-content-quality`](../synthesis-content-quality/SKILL.md) for

editorial quality and model-shaped prose. Use

[`synthesis-clean-text`](../synthesis-clean-text/SKILL.md) for ordinary text

normalization. This skill owns provenance mechanics and claim boundaries.

## Non-negotiable boundary

Do not provide or execute a workflow whose objective is to:

- strip or defeat a provider watermark;

- mutate text until a provenance detector stops firing;

- optimize paraphrasing, token substitutions, translation, sampling, or model

choice against detector feedback;

- represent AI-assisted text as solely human-authored;

- promise output is watermark-free or statistical-fingerprint-free.

When a user needs control over provider-added signals, prefer prevention by

choosing a local/open-weight model before generation, plus transparent lineage.

If the text already exists, preserve it, audit only with authorized tools, and

report the result without turning it into a mutation objective.

## Workflow

### 1. Define the provenance requirement

Record the actual constraint before selecting a model:

- privacy or data-egress requirement;

- reproducibility requirement;

- provider-mark policy;

- disclosure or recordkeeping requirement;

- quality threshold;

- allowed runtimes and licenses;

- whether a provider or standards detector is authorized and available.

Do not collapse “I do not want a hosted-provider mark” into “make generated

text look human.” The first is a model-selection and provenance requirement;

the second is an authorship-evasion objective.

### 2. Re-verify current capability claims

Provider behavior, regulation, model IDs, detector access, and local runtimes

change. Search current primary documentation before making a claim. Record:

- provider, exact model, product surface, region, and date;

- whether the capability is deployed, a roadmap statement, research, or

third-party observation;

- whether verification is public, account-bound, provider-only, or absent;

- what a positive and negative result can and cannot establish.

Use the evidence classes and bounded language in

[`references/capability-claims.md`](references/capability-claims.md).

### 3. Select the generation path

Use this order:

1. If a hosted provider's documented behavior satisfies the requirement, use

it and record the exact surface.

2. If provider-added text marking conflicts with the requirement, select an

authorized local/open-weight model before generating.

3. If no path satisfies both quality and provenance constraints, report the

conflict. Do not claim an unverified workaround removes a mark.

The local runner is provider-neutral and speaks to a loopback

OpenAI-compatible endpoint. See

[`references/open-weight-runner-contract.md`](references/open-weight-runner-contract.md).

### 4. Create the evidence bundle

Preserve:

- prompt or prompt hash and a private pointer;

- source-input hashes;

- model requested and model returned;

- runtime and endpoint class;

- parameters actually set or returned;

- raw output and SHA-256 hash;

- parent record IDs and human-edit description;

- detector and integrity-audit results with tool versions and limitations.

Create and validate the manifest:

```bash

python3 scripts/provenance_manifest.py create \

--generation-mode local_open_weight \

--provider local \

--model example-model \

--runtime ollama \

--runtime-receipt ollama-metadata.json \

--endpoint-class local_loopback \

--prompt-file prompt.txt \

--output-file output.txt \

--manifest provenance.json

python3 scripts/provenance_manifest.py validate provenance.json

python3 scripts/provenance_manifest.py verify provenance.json

```

For an edited or derived output, add each direct parent with

`--parent-manifest parent.json` during creation and pass the same explicit

parent manifest to `verify`. Parent links contain hashes and record IDs, not

paths; verification never follows a path stored by a parent.

The full schema and field semantics are in

[`references/provenance-manifest.md`](references/provenance-manifest.md).

### 5. Run non-mutating integrity inspection when relevant

Invisible Unicode and normalization differences can affect text handling, but

they are not proof of a statistical watermark. Audit without rewriting:

```bash

python3 scripts/text_integrity_audit.py article.txt --format human

python3 scripts/text_integrity_audit.py article.txt --format json --fail-on-findings

```

The script reports code points, positions, normalization differences, hashes,

and line-ending counts. For a file, it performs two complete byte reads and

refuses the audit if their SHA-256 hashes differ. Standard input is necessarily

single-read. The script never writes a cleaned copy.

### 6. Run local generation when it satisfies the policy

For an already running loopback OpenAI-compatible endpoint, capture the native

runtime receipt first. The bundled metadata helper supports Ollama:

```bash

python3 scripts/ollama_metadata.py \

--model example-model \

--output ollama-metadata.json

```

Then bind that receipt into the one-shot generation manifest:

```bash

python3 scripts/local_generate.py \

--endpoint http://127.0.0.1:11434/v1/chat/completions \

--provider local \

--runtime ollama \

--runtime-receipt ollama-metadata.json \

--model example-model \

--prompt-file prompt.txt \

--output-file output.txt \

--manifest provenance.json

```

The runner records one generation. It does not call a detector, regenerate

selectively, or optimize against provenance results. Non-loopback endpoints are

rejected unless the operator passes `--allow-non-loopback` deliberately.

The receipt preserves the runtime version, model digest, size, quantization,

license and template hashes, selected model metadata, and declared unknowns.

It deliberately excludes the full tensor inventory and never treats an Ollama

tag as proof of authorship, license compliance, or watermark absence.

### 7. Report bounded conclusions

Use conclusions shaped like:

- “The provider documents text marking for this exact surface as of DATE.”

- “This authorized detector returned RESULT under TOOL VERSION; the provider

states that this result does not prove AUTHORSHIP CLAIM.”

- “The integrity audit found U+200B at these positions. That finding describes

Unicode content, not a token-distribution watermark.”

- “No compatible public detector was found in the bounded sources reviewed;

verification remains unavailable, not negative.”

Never write “watermark-free,” “undetectable,” “human-written,” or “clean” when

the evidence establishes only a narrower technical fact.

## Completion checklist

- [ ] Exact model, surface, runtime, and collection date are recorded.

- [ ] Hosted versus local selection follows the stated requirement.

- [ ] Raw input/output and hashes are preserved before editing.

- [ ] Native runtime receipt is hash-bound for local generation.

- [ ] Manifest validation, self-hash, file hashes, and direct-parent lineage

verification pass.

- [ ] Detector access and authorization are recorded, if used.

- [ ] No detector result was used as an optimization loop.

- [ ] Positive and negative conclusions are bounded to the evidence.

- [ ] Editorial quality is reviewed separately with the writing-quality stack.