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 PracticalSwan/agent-skills --skill competition-submission-checkergit clone --depth 1 https://github.com/PracticalSwan/agent-skillsWrote 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/practicalswan/agent-skills/competition-submission-checker)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/competition-submission-checker"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/competition-submission-checker/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/practicalswan/agent-skills/competition-submission-checker"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/competition-submission-checker.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 | $0.00024 | $0.00870 |
| Opus 5 | $0.00012 | $0.00435 |
| Sonnet 5 | $0.00005 | $0.00174 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
competition-submission-checker 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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competition Submission Checker
Use this skill for the folder 04. Competition (due Jun 12th 11AM UTC) or any
NFL Draft prediction submission work.
Context Source
Load current competition facts from Serena memory first. If Serena is
unavailable, use docs/memory-bank/progress.md, then verify against the current
competition README or notebook before final advice.
Snapshot from project onboarding:
- training target:
Drafted - metric reported in audit: AUC
train.csv: 2781 rows x 16 columnstest.csv: 696 rows x 15 columnssample_submission.csv: 696 rows x 2 columns- deadline needs verification because folder and README dates may differ
Workflow
- Read
AGENTS.mdand relevant Serena memory ordocs/memory-bank/fallback context. - Verify the current README/notebook instructions for columns, metric, and deadline before final advice.
- Compare output columns to
sample_submission.csv. - Confirm row count, ID alignment, duplicate IDs, missing predictions, numeric prediction range, and file encoding.
- Check that target leakage is not used.
- If evaluating locally, use AUC-compatible probabilities, not hard labels unless the assignment explicitly asks for labels.
Safety
- Do not submit files externally unless the user explicitly asks.
- Do not use git, GitHub, or Google services.
Cross-Client Portability
This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.
- GitHub Copilot: keep the folder in a Copilot-visible skill path or wrap the workflow in project instructions when folder discovery is unavailable.
- Claude Code: keep the folder in a local skills directory or a compatible plugin source.
- Codex: install or sync the folder into
$CODEX_HOME/skills/competition-submission-checkerand restart Codex after major changes.
MCP Availability And Fallback
Preferred MCP Server: None required
- Fallback prompt: "Use the Competition Submission Checker skill without MCP. Rely on its local instructions, bundled resources, standard shell or editor tools, and direct verification. Show the evidence used before concluding."
- Do not claim an MCP operation was used when the active host does not expose it.
- Treat local files, tests, rendered outputs, logs, or screenshots as the fallback evidence path.
What ships with it
1 file 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.
- 3d ago Changed 40e30e0d4e4a
- 4d ago Changed b2d00013702e
- 7d ago Changed 71d386dde778
- 11d ago First seen · 93 lines · 24 tokens per session scan A d9f5a27b8ee4
competition-submission-checker is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 3d ago), licensed MIT. It adds 24 tokens to every session and 870 once invoked, about $0.0001 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.
Other skills, from other repositories
cli-eval
Create and run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from the CLI.
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
darwinian-evolver
Evolve prompts/regex/SQL/code with Imbue's evolution loop.
validate
Validate Semantica pipelines, extraction quality, graph schemas, and ontology consistency. Returns structured error/warning checklists. Uses PipelineValidator, PipelineBuilder.validatepipeline(), GraphValidator, and OntologyValidator. Sub-commands: pipeline, step, dependencies, extraction, graph, ontology, performance.
launching-evals
Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher. Covers running evaluations, checking status and live progress, debugging failed runs, exporting artifacts and logs, and analyzing results. ALWAYS triggers on mentions of running evaluations, checking progress, debugging failed evals…
nemo-automodel-recipe-development
Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.