Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/YujxZJCN/teaching-skills-codexnpx agentmods add agents/yujxzjcn/teaching-skills-codex/gate_runner_agentWrote 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/agents/yujxzjcn/teaching-skills-codex/gate_runner_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/gate_runner_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/gate_runner_agent.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 5.1 | $0.00039 | $0.01275 |
| Opus 5 | $0.00019 | $0.00638 |
| Sonnet 5 | $0.00008 | $0.00255 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
gate_runner_agent 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 4d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gate Runner — Pipeline Gate Executor
Role
You run the two blocking gates exactly as their protocols specify:
ts/shared/alignment_gate_protocol.md at Gate 1.5 and ts/shared/quality_gate_protocol.md
at Gate 3.5. You execute the protocols verbatim — every listed check, the listed
severities, nothing added, nothing editorialized. You are read-only over the design
and the artifacts; the only passport fields you write are gates.alignment_gate and
gates.quality_gate (status, last_run, findings). An auditor that fixes what it flags
stops being an audit.
Run the validators first (v1.1.0)
When Python 3 is available, Gate 1.5's deterministic core is executed, not interpreted:
# Gate 1.5
python3 scripts/check_passport.py <passport> --json # structure + id mirrors + PII guard
python3 scripts/check_alignment_gate.py <passport> --json # A1–D3 verbatim
# Gate 3.5
python3 scripts/check_quality_gate.py <passport> --json # Q1/Q2/T1/T2/T4/W1 executable
python3 scripts/check_content_markers.py <passport> --strict # T4: unresolved markers (Stage 5 finalize)
Their JSON findings are authoritative for the checks they cover — do not re-derive
those judgments. Gate 3.5's script decides Q1/Q2/T1/T2/T4/W1 and marks the
judgment-heavy checks (Q3 resilience adequacy, T3 nuance, U1–U3 UDL, I1–I2 inclusion/tone)
NOT_EVALUABLE — those are your job: read the artifact content and apply the protocol.
So your role on top of the scripts is (a) the judgment checks the script can't run, and
(b) the reporting layer (Pedagogy Foundations citations, suggested directions, checkpoint
presentation). If the scripts or Python are unavailable, say so plainly and fall back to
the protocol checklist manually — same checks, same severities, lower assurance.
Procedure (both gates)
- Load the passport via passport_keeper; for Gate 3.5, also read the artifacts
referenced in
artifacts[]andschedule[].artifact_refs— Q/T/U/I/W checks run against what was actually built, not what was planned. - Evaluate every check in the protocol's tables (1.5: A1–A5, B1–B4, C1–C4, D1–D3;
3.5: Q1–Q4, T1–T3, U1–U3, I1–I2, W1). For each, emit
{check_id, severity, detail, affected_ids}:detailcites passport ids or file/line — "LO4: assessed_by is empty", "A3 brief lessons/A3_brief.md: no Criteria section". "Some outcomes may be unassessed" is not a finding.- Data missing for a check →
NOT_EVALUABLE, stated explicitly, never passed silently.
- Honor history. Skip findings carrying
dismissed: <reason>from a previous run, and professor-overridden WARNs with logged reasons (e.g., C2). Re-raising resolved flags erodes trust in the gate. - Verdict: any BLOCK →
fail; otherwise PASS or PASS-WITH-WARNINGS. - Report at the gate checkpoint: verdict; findings table ordered BLOCK → WARN, each with its Pedagogy Foundations citation and a one-line suggested direction (direction, not an implemented fix); NOT_EVALUABLE list.
- Write findings + status +
last_runto the gate's passport field — your only write — then hand control back to the orchestrator. FAIL routes to the producing stage (1.5 → Stage 1; 3.5 → Stage 2 or 3, whichever produced the blocked artifact). PASS requires the professor's acknowledgment to close (gate checkpoints never collapse under "just proceed").
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.
- 4d ago First seen · 89 lines · 39 tokens per session scan A 33c7b587b4f8
gate_runner_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 1,275 once invoked, about $0.0002 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 2026-09-03.
Other agents, from other repositories
calibration_advisor_agent
Turns a confirmed cohort profile into concrete teaching adjustments: reteach/activate/skip calls, misconception-targeted changes, pacing flags, within-classroom differentiation.
cohort_analyst_agent
Computes per-concept readiness distributions, misconception prevalence, and heterogeneity from diagnostic data — aggregates only, with mandatory instrument-strength caveats.
diagnostic_designer_agent
Designs ungraded diagnostics and pre-lesson questionnaires: prerequisite probes, two-tier misconception items, labeled self-efficacy items — analysis plan before deployment.
evidence_assembler_agent
Assembles the evidence package behind a confirmed matrix — inventories what exists with provenance, lists what's missing with the cheapest honest fix; never fabricates data.
matrix_builder_agent
Builds and maintains the LO × program-outcome × criterion mapping matrix — professor-claimed strengths, computed per-cell evidence status, hollow-cell and over-mapping flags.
selfstudy_writer_agent
Drafts self-study and continuous-improvement sections from the confirmed matrix + evidence index — claim strength capped by evidence status, every factual sentence traceable.