Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add vladm3105/aidoc-flow-framework/plugin install aidoc-flowWrote 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/vladm3105/aidoc-flow-framework/doc-ears-audit)<a href="https://agentmods.dev/skills/vladm3105/aidoc-flow-framework/doc-ears-audit"><img src="https://agentmods.dev/badge/skills/vladm3105/aidoc-flow-framework/doc-ears-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vladm3105/aidoc-flow-framework/doc-ears-audit"><img src="https://agentmods.dev/badge/skills/vladm3105/aidoc-flow-framework/doc-ears-audit.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00042 | $0.07095 |
| Opus 5 | $0.00021 | $0.03547 |
| Sonnet 5 | $0.00008 | $0.01419 |
| Haiku 4.5 | $0.00004 | $0.00709 |
Grade A, and why
doc-ears-audit 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 3d 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.
This is a copy
86% identical to doc-bdd-audit — 159 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 591 lines — stays where its author put it; the contents beside it link to each section on GitHub.
doc-ears-audit
Purpose
Run a unified EARS audit — declarative structural checks plus content-quality
review — in one pass, producing a single combined report that
../doc-ears-fixer/SKILL.md consumes. The framework ships no runtime code, so
this skill is the validator: Claude performs each check directly against the
EARS using the spec as the contract.
Layer: 3 (EARS quality gate). Upstream: an EARS file. Downstream:
.aidoc/audit/03_EARS-audit.md and an optional fix-cycle trigger.
When to Use
Use after an EARS exists and before generating the BDD, or inside the autopilot's
audit↔fix cycle. Do not use to create an EARS (use ../doc-ears/SKILL.md or
../doc-ears-autopilot/SKILL.md).
Fresh-audit policy: always audit from scratch — never reuse prior scores or cached results; compute the BDD-Ready score independently each run.
Report cleanup: the audit report is a single file
(.aidoc/audit/03_EARS-audit.md) overwritten in place each run — no version cleanup
needed. Keep EARS-NN.F_fix_report_v*.md and .drift_cache.json.
Execution Contract
Input: EARS path (docs/03_EARS/EARS-NN_*/...); optional score threshold
(default 90).
Sequence: 1) run structural checks → 2) record findings → 3) run content
review → 4) merge/normalize findings → 5) write .aidoc/audit/03_EARS-audit.md
→ 6) if auto-fixable findings exist, hand off to doc-ears-fixer.
Review Mode
Resolve review_mode from .aidoc/profile.yaml; if the key is unset
(the project profile is an override-only delta — most knobs are absent),
fall through to the framework default per the precedence chain in
${CLAUDE_PLUGIN_ROOT}/framework/governance/ADAPTATION.md (framework defaults < user-global seed < project profile). The framework default
is team at gates (pre_promotion / pre_merge) and single_pass at
write-time (on_author). The same fallback rule applies to every other
adaptation knob (audit_threshold, section_toggles, active_layers,
glossary). The structural checks below are run deterministically by
this skill in every mode — they are the gate floor per
${CLAUDE_PLUGIN_ROOT}/framework/governance/REVIEW_TEAM.md §"Scoring,
conflicts & the gate".
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.
- 3d ago Changed e78af3f6ea85
- 4d ago Changed afcabfa20859
- 8d ago First seen · 591 lines · 42 tokens per session scan A 7b3a867dba99
doc-ears-audit is a skill published in the GitHub repository vladm3105/aidoc-flow-framework (17 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 7,095 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to doc-bdd-audit, differing in 159 lines, and is treated as a copy.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.