Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tokenbender/agent-guides/audit-sft-data-qualitynpx skills add tokenbender/agent-guides --skill audit-sft-data-qualitygit clone --depth 1 https://github.com/tokenbender/agent-guidesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tokenbender/agent-guides/audit-sft-data-quality)<a href="https://agentmods.dev/skills/tokenbender/agent-guides/audit-sft-data-quality"><img src="https://agentmods.dev/badge/skills/tokenbender/agent-guides/audit-sft-data-quality.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00124 | $0.02135 |
| Opus 5 | $0.00062 | $0.01068 |
| Sonnet 5 | $0.00025 | $0.00427 |
| Haiku 4.5 | $0.00012 | $0.00214 |
Grade A, and why
audit-sft-data-quality scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit SFT Data Quality
Core rule
Judge every row against the target task. A polished answer is not high-quality supervision if it teaches the wrong behavior, violates the task contract, leaks the evaluator, or cannot be verified.
Apply hard correctness and integrity gates before diversity scores, confidence scores, or aesthetic judgments.
For base-eval diagnosis, capability planning, synthesis, augmentation, and iterative dataset design, read iterative-sft-data-design.md.
1. Define the behavior contract
Write the contract before reading candidate answers:
| Field | Required description |
|---|---|
| Task | What the model must accomplish |
| Inputs | Allowed data, context, tools, and state |
| Output | Required schema, format, files, actions, or response style |
| Invariants | Facts that must remain true |
| Failure behavior | Rejection, abstention, rollback, or recovery rules |
| Resource limits | Context, tokens, latency, memory, calls, or complexity |
| Evaluation | Oracle, tests, rubric, benchmark, and sampling policy |
| Generalization target | Novel domains, templates, difficulty, or workflows |
Do not infer train readiness while any contract-critical field is unknown. Record assumptions explicitly when the source does not define them.
2. Freeze the source inventory
Preserve immutable evidence before transforming data:
- source path, repository, revision, archive member, or URL;
- file and member SHA-256;
- row counts and unique task counts;
- schema version and split;
- synthetic, human, model-generated, repaired, or imported provenance;
- license, privacy, consent, and secret-handling constraints;
- parent row or revision lineage.
Never overwrite raw inputs. Put normalized, selected, repaired, and rejected rows in separately identified artifacts.
3. Validate structure and conversation semantics
Check every row, not a sample:
- parseability and required keys;
- stable task identity and label consistency;
- legal role order and nonempty assistant target;
- output-format compliance;
- tool-call and tool-result pairing;
- referenced files, attachments, schemas, and environments;
- absence of accidental test, reference-answer, private-state, or system-prompt content;
- tokenizer-measured length under the actual model revision;
- loss masking and target boundaries when the training loader uses them.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 254 lines · 124 tokens per session scan A df842d656ca5
audit-sft-data-quality is a skill published in the GitHub repository tokenbender/agent-guides (368 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 124 tokens to every session and 2,135 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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