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 opendatahub-io/ai-helpers --skill engineer-snapshotgit clone --depth 1 https://github.com/opendatahub-io/ai-helpersWrote 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/opendatahub-io/ai-helpers/engineer-snapshot)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/engineer-snapshot"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/engineer-snapshot/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/opendatahub-io/ai-helpers/engineer-snapshot"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/engineer-snapshot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00076 | $0.01801 |
| Opus 5 | $0.00038 | $0.00901 |
| Sonnet 5 | $0.00015 | $0.00360 |
| Haiku 4.5 | $0.00008 | $0.00180 |
Grade A, and why
engineer-snapshot 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 9d 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 — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engineer Snapshot
Generate a per-engineer activity snapshot combining JIRA issues, GitHub PR activity, and open action items from 1:1 notes.
Prerequisites
aclimust be installed and authenticated (acli jira auth)ghCLI must be installed and authenticated (gh auth login)jqandyqmust be installed and available in PATH- A team config YAML file (see Config Format below)
Config Format
Create a YAML file with your team's details (same format as
team-weekly-report):
team:
name: "My Team"
jira:
url: "https://mycompany.atlassian.net"
project: "PROJ"
component: "MyComponent" # optional
github:
repositories:
- "org/repo1"
- "org/repo2"
members:
- name: "Engineer One"
jira_username: "712020:account-id-here"
github_username: "eng1"
notes_file: "notes/engineer_one.md" # optional
defaults:
jira_lookback_days: 7
github_lookback_days: 7
stale_threshold_days: 7
Implementation
Step 1: Parse Arguments
Parse $ARGUMENTS for:
- Engineer name (required): first positional argument
--config PATH(required): path to team config YAML--jira-days N(optional): JIRA lookback for stale threshold--github-days N(optional): GitHub lookback for merged PRs
If the engineer name is missing, ask:
"Which engineer should I generate a snapshot for?"
If --config is missing, ask:
"Which team config file should I use? Provide the path to your YAML config file."
Step 2: Load Config
Read the config file to extract the engineer's details:
github_username— needed for GitHub PR queriesnotes_file— path to 1:1 notes (if configured)team.github.repositories— repos to search for PRsdefaults.github_lookback_days— fallback for--github-days
Step 3: Fetch JIRA Data
Run the JIRA fetch script to get active and blocked issues:
"${CLAUDE_SKILL_DIR}/scripts/fetch_engineer_jira.sh" \
--config <CONFIG_PATH> \
--engineer "<ENGINEER_NAME>"
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.
- 9d ago First seen · 242 lines · 76 tokens per session scan A 506ff44c5843
engineer-snapshot is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 5d ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,801 once invoked, about $0.0004 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 skills, from other repositories
parallel-feature-development
Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system…
saga-orchestration
Implement saga patterns for distributed transactions and cross-aggregate workflows. Use this skill when implementing distributed transactions across microservices where 2PC is unavailable, designing compensating actions for failed order workflows that span inventory, payment, and shipping services, building…
task-coordination-strategies
Decompose complex tasks, design dependency graphs, and coordinate multi-agent work with proper task descriptions and workload balancing. Use this skill when breaking down work for agent teams, managing task dependencies, or monitoring team progress.
bazel-build-optimization
Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
nx-workspace-patterns
Configure and optimize Nx monorepo workspaces. Use when setting up Nx, configuring project boundaries, optimizing build caching, or implementing affected commands.
context-driven-development
Creates and maintains project context artifacts (product.md, tech-stack.md, workflow.md, tracks.md) in a conductor/ directory. Scaffolds new projects from scratch, extracts context from existing codebases, validates artifact consistency before implementation, and synchronizes documents as the project evolves. Use when…