LangChain Skills is a collection of agent skills for building software agents with LangChain, LangGraph, and Deep Agents. Coding agents that support the Agent Skills specification, including Claude Code, Cursor, and Windsurf, can install and use these skills. The catalogue lists the project's skills and its Claude Code plugin.
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 langchain-ai/langchain-skills --skill eval-engineeringgit clone --depth 1 https://github.com/langchain-ai/langchain-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/langchain-ai/langchain-skills/eval-engineering)<a href="https://agentmods.dev/skills/langchain-ai/langchain-skills/eval-engineering"><img src="https://agentmods.dev/badge/skills/langchain-ai/langchain-skills/eval-engineering/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/langchain-ai/langchain-skills/eval-engineering"><img src="https://agentmods.dev/badge/skills/langchain-ai/langchain-skills/eval-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00067 | $0.03342 |
| Opus 5.5 | $0.00027 | $0.01337 |
| Sonnet 5.5 | $0.00013 | $0.00668 |
| Haiku 4.5 | $0.00007 | $0.00334 |
Grade A, and why
eval-engineering 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Engineering
Flow
- Inspect all inputs first: the repository, Harness, optional traces, existing Tasks and runs, existing World knowledge, and the human goal. Identify the source files and skill references that apply before proposing work.
- Create or update the small project World Knowledge Skill from reusable facts in those inputs. Use it to propose one grounded Task.
- Draft the Task Spec and World Skill together. Show both to the user, keep exact
Task truth only in
Task.md, and refine both until the user approves them. - Implement the approved Task, validate its Environment and Verifier, run the real Harness, inspect the full evidence, and fix only non-agent failures.
- Reconcile the World Skill with what the run proved, then repeat this flow for the next Task.
Terms
- Task Spec:
Task.md, which describes the input, relevant agent conditions, Environment, scoring, fairness, and open decisions for one Task. - Task: the runnable instruction, Environment, and Verifier.
- Harness: the complete agent Harbor runs, including prompts, model loop, tools, hooks, memory, sessions, and adapter.
- Environment: the files, data, services, identity, permissions, network, clock, and mutable state around the Harness.
- Verifier: independent checks that score the result or mark a run invalid.
- World Knowledge Skill: a repository-local skill with reusable project-specific knowledge, references, scripts, assets, and tests that help generate Task Specs and build future Tasks.
- Spec2Task: the full loop that turns a reviewed Task Spec into an audited runnable Task. Follow Task implementation for its concrete build order and reference routing.
Reference routing
Read each reference when its decision appears:
| Need | Read |
|---|---|
| Inspect source, traces, the Harness, dependencies, access, and existing evals | Discovery |
| Bootstrap or update reusable project knowledge | World knowledge |
| Propose Tasks and write the single Task Spec | Task design |
| Build data, services, access, state, and reset | Environment building |
| Create structured or natural-language data | Synthetic data |
| Define independent evidence and scoring | Verifier design |
| Apply Spec2Task to turn a reviewed Spec into an audited Task | Task implementation |
| Compare model runs and classify failures | Calibration |
| Package and run Harbor tasks | Harbor |
| Adapt a known benchmark design | Benchmark patterns |
| Build multi-turn conversations | Multi-turn simulation |
| See World knowledge learned across two Tasks | Service-desk example |
What ships with it
20 files 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.
- agents/openai.yaml 378 B
- assets/task/Task.md.template 1.8 KB
- assets/world-skill/SKILL.md.template 2.9 KB
- references/calibration.md 3.1 KB
- references/discovery.md 12 KB
- references/environment-building.md 4.0 KB
- references/examples/service-desk.md 2.9 KB
- references/harbor.md 6.8 KB
- references/multi-turn-simulation/guide.md 5.1 KB
- references/multi-turn-simulation/harbor_example.py 10 KB runs code
- references/multi-turn-simulation/model_user.py 5.0 KB runs code
- references/multi-turn-simulation/runner.py 7.0 KB runs code
- references/patterns.md 37 KB
- references/synthetic-data.md 2.9 KB
- references/task-design.md 5.5 KB
- references/task-implementation.md 8.2 KB
- references/verifier-design.md 3.0 KB
- references/world-knowledge.md 11 KB
- scripts/compare_tool_schemas.py 14 KB runs code
- scripts/snapshot_sqlite_state.py 5.2 KB runs code
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 · 315 lines · 67 tokens per session scan A 47c93e705a3a
eval-engineering is a skill published in the GitHub repository langchain-ai/langchain-skills (1,313 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 3,342 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.