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ML reconnaissance — inventory all models, pipelines, data sources, and monitoring.
Install Cortex Recon
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-recon --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-recon .claude/skills/cortex-recon && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cortex-recon" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-recon into .claude/skills/cortex-recon/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-recon", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-reconType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-recon --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-recon .agents/skills/cortex-recon && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cortex-recon" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-recon into .agents/skills/cortex-recon/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-recon", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-recon --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-recon .cursor/skills/cortex-recon && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cortex-recon" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-recon into .cursor/skills/cortex-recon/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-recon", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jeremylongshore/tons-of-skills-marketplace.git --path plugins/ai-agency/tonone/skills/cortex-recon--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-recon --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-recon .gemini/skills/cortex-recon && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cortex-recon" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-recon into .gemini/skills/cortex-recon/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-recon", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-reconInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-recon .github/skills/cortex-recon && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cortex-recon" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-recon into .github/skills/cortex-recon/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-recon", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-recon --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-recon .opencode/skills/cortex-recon && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cortex-recon" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-recon into .opencode/skills/cortex-recon/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-recon", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
cortex-recon- GitHub stars
- 2.8k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 423 words
- Files
- 2
- Skills in repo
- 3,342
- Repo updated
- First seen
- Licence
- MIT
At a glance
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring.
- Works in 7 steps: Detect Environment → Models in Production → Training Pipelines → …
- Asked what ML do we have
- SKILL.md covers Steps and Delivery
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Cortex Recon is an agent skill from jeremylongshore/tons-of-skills-marketplace. ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `.claude-plugin/plugin.json`).
It sits in DevOps & Cloud. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.
When your agent uses it
- Asked what ML do we have
- Model inventory
Example prompts
- “what ML do we have”
- “model inventory”
- “ML assessment”
- “/cortex-recon”
Requirements
- Pre-approved tools (allowed-tools): Read, Bash, Glob, Grep, WebFetch, WebSearch, AskUserQuestion
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
ReadBashGlobGrepWebFetchWebSearchAskUserQuestion
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Cortex Recon loads about 1.5k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 423 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Read, Bash, Glob, Grep, WebFetch, WebSearch, AskUserQuestion
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 423 words, ~1,457 tokens.
Download SKILL.mdSave it as
.claude/skills/cortex-recon/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- cortex-recon
- description
- ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
- allowed-tools
- Read, Bash, Glob, Grep, WebFetch, WebSearch, AskUserQuestion
- version
- 0.6.4
- author
- tonone-ai <hello@tonone.ai>
- license
- MIT
ML Reconnaissance
You are Cortex — the ML/AI engineer on the Engineering Team.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect Environment
Scan the project broadly to find all ML-related artifacts:
# Model artifacts
find . -type f \( -name "*.pkl" -o -name "*.joblib" -o -name "*.onnx" -o -name "*.pt" -o -name "*.pth" -o -name "*.h5" -o -name "*.savedmodel" -o -name "*.mlmodel" \) 2>/dev/null | head -30
# Training scripts and configs
find . -type f -name "*.py" | xargs grep -l "model\.fit\|model\.train\|trainer\.train\|\.compile(" 2>/dev/null | head -20
# ML dependencies
cat requirements.txt 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|mlflow|wandb|sagemaker|vertex|huggingface|transformers|langchain|anthropic|openai"
cat pyproject.toml 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|mlflow|wandb|sagemaker|vertex|huggingface|transformers|langchain|anthropic|openai"
# Experiment tracking
ls -la mlruns/ wandb/ .neptune/ 2>/dev/null
# ML configs
find . -type f \( -name "*.yaml" -o -name "*.yml" -o -name "*.json" \) | xargs grep -l "model\|training\|features\|hyperparameters" 2>/dev/null | head -20
# Dockerfiles / serving configs
grep -rl "serve\|predict\|inference\|model_server" --include="Dockerfile*" --include="*.yaml" --include="*.yml" . 2>/dev/null | head -10
# Notebooks
find . -type f -name "*.ipynb" 2>/dev/null | head -20Step 1: Models in Production
Inventory every model that's serving predictions:
- What does it predict? (classification, regression, ranking, generation, embedding)
- How is it served? (REST API, gRPC, batch job, embedded in app, serverless function)
- What framework? (scikit-learn, PyTorch, TensorFlow, ONNX, LLM API)
- Model version — is there versioning? What version is deployed?
- Traffic volume — how many predictions per day/hour?
- Latency — p50/p95 response time
Step 2: Training Pipelines
Inventory every training pipeline:
- How often does it run? (daily, weekly, monthly, manually, never retrained)
- Where does it run? (local, CI/CD, cloud ML platform, notebook)
- Is it automated? (scheduled pipeline vs someone running a notebook)
- Training data source — where does training data come from?
- Training duration — how long does a training run take?
- Cost per training run — compute cost estimate
Step 3: Data Sources and Feature Pipelines
Inventory data and feature infrastructure:
- Data sources — databases, APIs, files, streams feeding the models
- Feature pipelines — how are features computed? Is there a feature store?
- Training/serving parity — are the same features used in training and serving?
- Data freshness — how stale is the data the model sees?
- Data quality checks — any validation, schema enforcement, or monitoring?
Step 4: Experiment Tracking
Assess experiment tracking maturity:
- Is there any? (MLflow, W&B, Neptune, TensorBoard, spreadsheet, nothing)
- What's tracked? (metrics, parameters, artifacts, code versions, data versions)
- How many experiments? (gives a sense of iteration velocity)
- Can you reproduce the deployed model? (the acid test)
Show full SKILL.md (148 more words)Show less
Step 5: Model Monitoring
Assess production monitoring:
- Is anyone watching accuracy? (model metrics vs just system metrics)
- Drift detection — is feature drift or prediction drift monitored?
- Alerting — do alerts fire when model performance degrades?
- Feedback loop — is there a way to get ground truth for predictions?
- A/B testing — is there infrastructure to compare model versions?
Step 6: ML Infrastructure Cost
Estimate the cost of ML infrastructure:
- GPU/TPU instances — are they running 24/7 or on-demand?
- Training compute — cost per training run, frequency
- Serving compute — cost to run inference endpoints
- Data storage — model artifacts, training data, feature stores
- Third-party APIs — LLM API costs, ML platform fees
Present the full inventory:
## ML Reconnaissance Report
### Model Inventory
| Model | Predicts | Framework | Serving | Frequency | Health |
|-------|----------|-----------|---------|-----------|--------|
| [name] | [what] | [framework] | [how] | [volume] | [status] |
### Training Pipelines
| Pipeline | Schedule | Platform | Duration | Automated |
|----------|----------|----------|----------|-----------|
| [name] | [freq] | [where] | [time] | [yes/no] |
### Data & Features
- Data sources: [list]
- Feature store: [yes/no — which]
- Training/serving parity: [verified/unverified/skewed]
### Experiment Tracking
- Tool: [name or "none"]
- Reproducibility: [can/cannot reproduce deployed model]
### Monitoring
- Model metrics monitoring: [yes/no]
- Drift detection: [yes/no]
- Alerting: [yes/no]
- Feedback loop: [yes/no]
### Cost Estimate
- Training: $[X]/month
- Serving: $[X]/month
- Data/storage: $[X]/month
- Total ML infra: $[X]/month
### Health Summary
- [model]: [status emoji + one-line assessment]
### Top Risks
1. [risk] — [impact]
2. [risk] — [impact]
3. [risk] — [impact]Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in plugins/ai-agency/tonone/skills/cortex-recon of jeremylongshore/tons-of-skills-marketplace.
- SKILL.md
- .claude-plugin/plugin.json
Open the folder on GitHubat commit cfae287
Compare with similar skills
Cortex Recon next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cortex Recon this skilljeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.5k | Automated safety check: Notes | MIT | |
| Fastllm Deploymentazrtydxb/Fastllm-proxy | 108 | — | ~727 | Automated safety check: Pass | Apache-2.0 | |
| Telemetry Integrationrapidaai/voice-ai | 745 | — | ~690 | Automated safety check: Pass | Custom licence | |
| Molmim NimNVIDIA/skills | 3.6k | 1 repos | ~1.9k | Automated safety check: Notes | Apache-2.0 | |
| Azure AI Projects Pyaiskillstore/marketplace | 433 | 4 repos | ~2.1k | Automated safety check: Pass | None | |
| China Mirror ResolverLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.6k | Automated safety check: Notes | MIT |
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Related
Questions about Cortex Recon
What does Cortex Recon do?
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Cortex Recon is an agent skill from jeremylongshore/tons-of-skills-marketplace. ML reconnaissance — inventory all models, pipelines, data sources, and monitoring.
When should I use Cortex Recon?
Cortex Recon fits situations like: asked what ML do we have; model inventory.
How do I install Cortex Recon in Claude Code?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a claude-code`. Or copy the skill folder (plugins/ai-agency/tonone/skills/cortex-recon in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/cortex-recon in your project. Claude Code loads it when a task matches its description.
How do I install Cortex Recon in Codex?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a codex`. Or copy the skill folder (plugins/ai-agency/tonone/skills/cortex-recon in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/cortex-recon in your project. Codex loads it when a task matches its description.
Can I use Cortex Recon in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cortex-recon, .gemini/skills/cortex-recon, .github/skills/cortex-recon and .opencode/skills/cortex-recon in your project.
What does Cortex Recon need to run?
SKILL.md names no scripts, command-line tools or credentials: Cortex Recon is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Bash, Glob, Grep, WebFetch, WebSearch, AskUserQuestion.
Does Cortex Recon access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Cortex Recon safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Cortex Recon use?
Cortex Recon is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Cortex Recon use?
About 1.5k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Cortex Recon?
Skills that share tags, products or a category with Cortex Recon: Fastllm Deployment (azrtydxb/Fastllm-proxy, 108 stars), Telemetry Integration (rapidaai/voice-ai, 745 stars), Molmim Nim (NVIDIA/skills, 3.6k stars) and Azure AI Projects Py (aiskillstore/marketplace, 433 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cortex Recon?
jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.
Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.