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 skills add bdfinst/agentic-dev-team --skill quality-gate-pipelinegit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWrote 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/bdfinst/agentic-dev-team/quality-gate-pipeline)<a href="https://agentmods.dev/skills/bdfinst/agentic-dev-team/quality-gate-pipeline"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/quality-gate-pipeline/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/bdfinst/agentic-dev-team/quality-gate-pipeline"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/quality-gate-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 126 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00059 | $0.02267 |
| Opus 5.5 | $0.00024 | $0.00907 |
| Sonnet 5.5 | $0.00012 | $0.00453 |
| Haiku 4.5 | $0.00006 | $0.00227 |
Grade A, and why
quality-gate-pipeline 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 1mo 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Gate Pipeline
Overview
Single quality gate that every agent passes through before output is accepted. Replaces three formerly separate skills (accuracy-validation, verification-before-completion, task-review-correction) with a unified pipeline of three phases that run in sequence.
Constraints
- Do not deliver output containing unverified claims; pause and verify first
- Do not claim completion without fresh verification evidence from this session
- Do not reference test results or tool output from earlier in the conversation — re-run and show current output
- Do not substitute reasoning or explanation for actual evidence
- Max 3 review-correction cycles before escalating to the Orchestrator
- Each correction cycle must reduce total defect count; flat or increasing defects trigger escalation
The Three Phases
Phase 1: Self-Validation (before delivery)
Every agent runs this checklist mentally before presenting output.
Factual Accuracy
- All file paths referenced actually exist (verify with tool, don't assume)
- All function/class/variable names match what's in the codebase
- Version numbers, API signatures, and config values are verified, not recalled from training
- No statistics or citations are fabricated
- Claims about an external tool or product's UI (menu paths, button/control labels, screen layouts, settings locations) are backed by a citation the agent actually has — a fetched doc, a user-provided screenshot/description, or tool output — not recalled from training as if current and authoritative. When no citation exists, say so plainly (e.g. "I don't have a verified/current view of this menu — check the tool's current UI") rather than describing specifics; this is the same Low confidence fallback as any other recalled/guessed claim (see Confidence Assessment below).
Instruction Fidelity
- Output addresses what the user actually asked, not a reinterpretation
- All acceptance criteria from the task are met
- No scope creep beyond the request
- Constraints from the agent persona are respected
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.
- 1mo ago First seen · 185 lines · 59 tokens per session scan A d802eb8059c0
quality-gate-pipeline is a skill published in the GitHub repository bdfinst/agentic-dev-team (295 stars, last pushed 4d ago), licensed MIT. It adds 59 tokens to every session and 2,267 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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