data-flywheel: Data & Analytics for AI Agents | OpenAgentSkill

Overview

Data Flywheel - approved runs into reusable intelligence

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Data Flywheel - approved runs into reusable intelligence

Use this skill when a user wants to turn repeated human-approved agent work across Claude Code, Hermes, OpenClaw, Codex, Cursor, or custom .agent/ loops into local artifacts for retrieval, evals, prompt shrinking, and optional future open-weight model/adapters.

The flywheel is:

approved run
-> redacted trace
-> context card
-> eval case
-> training-ready JSONL
-> optional downstream SLM/adapter experiment later

This skill creates the harness. It does not train a model.

Hard Rules

Use only human-approved runs. Rejected or unknown-review runs can become failure-mode notes, not trainable examples.
Redaction must pass before anything is marked trainable.
Do not store raw prompts, raw code, client names, addresses, phone numbers, emails, secrets, credentials, or unredacted CRM records.
Keep .agent/flywheel/ private and gitignored unless the user explicitly commits sanitized examples.
Stay model-agnostic. Mention model families only as downstream examples.

Inputs

Default local input:

.agent/flywheel/approved-runs.jsonl

Each line should be a sanitized run record with:

domain
workflow
harness
instruction
input_redacted
output_approved
human_review.status as accepted or edited
redaction_status: passed
pii_level
optional stable_rules, tool_contracts, eval_tags, failure_modes

Export

Run:

python3 .agent/tools/data_flywheel_export.py

Outputs go to:

.agent/flywheel/exports/<YYYY-MM-DD>/

Key outputs:

trace-records.jsonl
training-examples.jsonl
eval-cases.jsonl
context-cards/<domain>/<workflow>.md
context-cards/<domain>/<workflow>.json
flywheel-metrics.json

Readiness Checks

Use these as heuristics, not hard rules:

10-25 approved runs: useful first context card
25-100 approved runs: first eval set and repeated failure modes
100-300 approved runs: context compression and routing measurement
500-1,500 high-quality examples: narrow adapter experiment candidate
2,000-10,000+ examples: broader workflow-family corpus

What To Report

When finishing, report:

traces exported
trainable examples exported
eval cases exported
context cards created
redaction pass rate
acceptance rate by workflow
workflows that should stay frontier-model/manual-review
workflows that may become SLM/adapter candidates later

Self-rewrite hook

If users repeatedly ask for the same domain-specific fields, add them to a local context card or schema example instead of hard-coding them into this general skill.

View source ↗

File metadata

name: data-flywheel
version: 2026-04-25
triggers: ["data flywheel", "trace to train", "training traces", "context cards", "eval cases", "approved runs", "vertical intelligence"]
tools: [bash, git]
preconditions: [".agent exists"]
constraints: ["local-only by default", "human-approved runs only", "redaction required before trainable", "do not train models"]

View original text

---
name: data-flywheel
version: 2026-04-25
triggers: ["data flywheel", "trace to train", "training traces", "context cards", "eval cases", "approved runs", "vertical intelligence"]
tools: [bash, git]
preconditions: [".agent exists"]
constraints: ["local-only by default", "human-approved runs only", "redaction required before trainable", "do not train models"]
---

# Data Flywheel - approved runs into reusable intelligence

Use this skill when a user wants to turn repeated human-approved agent work
across Claude Code, Hermes, OpenClaw, Codex, Cursor, or custom `.agent/` loops
into local artifacts for retrieval, evals, prompt shrinking, and optional
future open-weight model/adapters.

The flywheel is:

```text
approved run
-> redacted trace
-> context card
-> eval case
-> training-ready JSONL
-> optional downstream SLM/adapter experiment later
```

This skill creates the harness. It does not train a model.

## Hard Rules

- Use only human-approved runs. Rejected or unknown-review runs can become
  failure-mode notes, not trainable examples.
- Redaction must pass before anything is marked trainable.
- Do not store raw prompts, raw code, client names, addresses, phone numbers,
  emails, secrets, credentials, or unredacted CRM records.
- Keep `.agent/flywheel/` private and gitignored unless the user explicitly
  commits sanitized examples.
- Stay model-agnostic. Mention model families only as downstream examples.

## Inputs

Default local input:

```text
.agent/flywheel/approved-runs.jsonl
```

Each line should be a sanitized run record with:

- `domain`
- `workflow`
- `harness`
- `instruction`
- `input_redacted`
- `output_approved`
- `human_review.status` as `accepted` or `edited`
- `redaction_status: passed`
- `pii_level`
- optional `stable_rules`, `tool_contracts`, `eval_tags`, `failure_modes`

## Export

Run:

```bash
python3 .agent/tools/data_flywheel_export.py
```

Outputs go to:

```text
.agent/flywheel/exports/<YYYY-MM-DD>/
```

Key outputs:

- `trace-records.jsonl`
- `training-examples.jsonl`
- `eval-cases.jsonl`
- `context-cards/<domain>/<workflow>.md`
- `context-cards/<domain>/<workflow>.json`
- `flywheel-metrics.json`

## Readiness Checks

Use these as heuristics, not hard rules:

- 10-25 approved runs: useful first context card
- 25-100 approved runs: first eval set and repeated failure modes
- 100-300 approved runs: context compression and routing measurement
- 500-1,500 high-quality examples: narrow adapter experiment candidate
- 2,000-10,000+ examples: broader workflow-family corpus

## What To Report

When finishing, report:

- traces exported
- trainable examples exported
- eval cases exported
- context cards created
- redaction pass rate
- acceptance rate by workflow
- workflows that should stay frontier-model/manual-review
- workflows that may become SLM/adapter candidates later

## Self-rewrite hook

If users repeatedly ask for the same domain-specific fields, add them to a
local context card or schema example instead of hard-coding them into this
general skill.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: codejunkie99/agentic-stack
License: Apache-2.0
Version: 1.0.0
Last GitHub push: Aug 6, 2026
Registry updated: Sep 2, 2026
Instruction path: .agent/skills/data-flywheel/SKILL.md @ 9424c58e1cfc

Version reported in registry metadata; check source releases before relying on it.

Quality

77/100

Strong

Trust

68/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Creator: codejunkie99
Source: codejunkie99/agentic-stack
Indexed by: OpenAgentSkill community index

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Overview

Data Flywheel - approved runs into reusable intelligence

Hard Rules

Inputs

Export

Readiness Checks

What To Report

Self-rewrite hook

Review the source

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal