Product Launch Legal Review
anthropics/claude-for-legal
Runs a category-by-category legal review of a product launch from a PRD or tracker ticket, calibrated to your team's framework, and writes a review memo in house format.
Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes.
Install Algo Mfg Fmea
$ npx skills add asgard-ai-platform/skills --skill algo-mfg-fmea -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-mfg-fmea --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/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-mfg-fmea .claude/skills/algo-mfg-fmea && 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 "algo-mfg-fmea" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-fmea into .claude/skills/algo-mfg-fmea/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-mfg-fmea", 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/asgard-ai-platform/skills/tree/main/algo-mfg-fmeaType 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 asgard-ai-platform/skills --skill algo-mfg-fmea -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-mfg-fmea --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-mfg-fmea .agents/skills/algo-mfg-fmea && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-mfg-fmea" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-fmea into .agents/skills/algo-mfg-fmea/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-mfg-fmea", 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 asgard-ai-platform/skills --skill algo-mfg-fmea -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-mfg-fmea --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-mfg-fmea .cursor/skills/algo-mfg-fmea && 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 "algo-mfg-fmea" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-fmea into .cursor/skills/algo-mfg-fmea/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-mfg-fmea", 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/asgard-ai-platform/skills.git --path algo-mfg-fmea--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 asgard-ai-platform/skills --skill algo-mfg-fmea -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-mfg-fmea --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-mfg-fmea .gemini/skills/algo-mfg-fmea && 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 "algo-mfg-fmea" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-fmea into .gemini/skills/algo-mfg-fmea/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-mfg-fmea", 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 asgard-ai-platform/skills algo-mfg-fmeaInstalls 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 asgard-ai-platform/skills --skill algo-mfg-fmea -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-mfg-fmea .github/skills/algo-mfg-fmea && 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 "algo-mfg-fmea" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-fmea into .github/skills/algo-mfg-fmea/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-mfg-fmea", 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 asgard-ai-platform/skills --skill algo-mfg-fmea -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-mfg-fmea --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-mfg-fmea .opencode/skills/algo-mfg-fmea && 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 "algo-mfg-fmea" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-fmea into .opencode/skills/algo-mfg-fmea/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-mfg-fmea", 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
algo-mfg-fmea- GitHub stars
- 242
- Token cost
- ~1.2k tokens
- SKILL.md length
- 465 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to assess product
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Mfg Fmea is an agent skill from asgard-ai-platform/skills. Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes. Use this skill when the user needs to assess product or process risks, prioritize corrective actions, or build a risk register — even if they say 'failure mode analysis', 'risk assessment', 'what could go wrong', or 'RPN calculation'.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/aiag-vda-ap.md` and `references/scoring-rubrics.md`).
It sits in Legal & Compliance, covering Legal risk assessment. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to assess product
- Prioritize corrective actions
- Build a risk register — even if they say failure mode analysis
- Risk assessment
Example prompts
- “failure mode analysis”
- “risk assessment”
- “what could go wrong”
- “/algo-mfg-fmea”
Workflow steps
4 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 4e7f4f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
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 json).
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
Algo Mfg Fmea loads about 1.2k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 85 tokens; SKILL.md has 465 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 passed
The automated check found no risky patterns in SKILL.md.
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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 465 words, ~1,204 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-mfg-fmea/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-mfg-fmea
- description
- Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes. Use this skill when the user needs to assess product or process risks, prioritize corrective actions, or build a risk register — even if they say 'failure mode analysis', 'risk assessment', 'what could go wrong', or 'RPN calculation'.
- metadata.category
- WP-48 製造演算法
- metadata.tags
- manufacturing, fmea, risk-analysis, quality
FMEA (Failure Mode and Effects Analysis)
Overview
FMEA systematically identifies potential failure modes, their effects, causes, and current controls. Each failure is scored on Severity (S), Occurrence (O), and Detection (D) on 1-10 scales. RPN = S × O × D prioritizes which risks to address first. AIAG-VDA FMEA (2019) replaces RPN with Action Priority (AP) matrix.
When to Use
Trigger conditions:
- Designing new products/processes and identifying risks proactively
- Systematically evaluating existing failure modes for prioritization
- Meeting automotive (IATF 16949) or medical device (ISO 13485) quality requirements
When NOT to use:
- For root cause analysis of a known problem (use fishbone/5-why)
- For statistical analysis of defect data (use SPC or Pareto)
Algorithm
IRON LAW: Severity Can NEVER Be Reduced by Design Changes
Severity is determined by the EFFECT on the customer. A brake failure
is always severity 10, regardless of how unlikely or detectable it is.
FMEA reduces risk by: lowering Occurrence (better design/process) or
improving Detection (better testing/inspection). NEVER inflate
Detection scores to lower RPN artificially.Phase 1: Input Validation
Define scope: Design FMEA (DFMEA) or Process FMEA (PFMEA). Assemble cross-functional team. Prepare: process flow diagram or system block diagram. Gate: Scope defined, team assembled, reference diagrams available.
Phase 2: Core Algorithm
- List all potential failure modes for each function/process step
- For each failure mode, identify: effect on customer, root cause(s), current prevention controls, current detection controls
- Score: Severity (1-10), Occurrence (1-10), Detection (1-10)
- Classic RPN: RPN = S × O × D. Prioritize high RPNs.
- AIAG-VDA AP: Use the S-O-D combination matrix to assign Action Priority: High, Medium, Low.
- Define recommended actions for High-priority items with responsibility and target dates
Phase 3: Verification
Review: are all functions/steps covered? Do severity scores match actual customer impact? Are detection scores realistic (not overly optimistic)? Gate: Complete coverage, realistic scoring, actions assigned for high-priority items.
Phase 4: Output
Return FMEA register with prioritized actions.
Output Format
{
"fmea_items": [{"failure_mode": "seal leak", "effect": "water damage", "cause": "material degradation", "severity": 8, "occurrence": 4, "detection": 6, "rpn": 192, "ap": "high", "action": "add pressure test at final inspection"}],
"summary": {"total_modes": 45, "high_priority": 8, "medium": 15, "low": 22},
"metadata": {"type": "PFMEA", "scope": "assembly line 3"}
}Examples
Sample I/O
Input: Coffee machine brewing module, function: "heat water to 93°C" Expected: Failure modes: overheating (S=7, O=3, D=4, RPN=84), under-heating (S=5, O=4, D=3, RPN=60), no heating (S=8, O=2, D=2, RPN=32).
Show full SKILL.md (173 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| S=10, any O and D | Always high priority | Safety-critical failures require action regardless of RPN |
| RPN=100 (S=10,O=1,D=10) vs (S=1,O=10,D=10) | Same RPN, very different risk | This is why AIAG-VDA AP replaces pure RPN |
| No current controls | D=10 (no detection) | Honest assessment drives improvement |
Gotchas
- RPN is misleading: RPN=100 from S=10,O=1,D=10 (catastrophic but rare, undetectable) is very different from S=1,O=10,D=10 (trivial but frequent). AIAG-VDA AP matrix addresses this flaw.
- Scoring consistency: Without calibration, different team members score differently. Use scoring rubrics with examples and calibrate as a team.
- Detection ≠ prevention: A low Detection score (good detection) doesn't prevent the failure — it only catches it. Prioritize Occurrence reduction over Detection improvement.
- Living document: FMEA must be updated when design/process changes, new failure data appears, or corrective actions are implemented. A static FMEA provides diminishing value.
- Scope creep: An FMEA that tries to cover everything becomes unmanageable. Focus on the critical functions or highest-risk areas first.
References
- For AIAG-VDA AP matrix and scoring tables, see
references/aiag-vda-ap.md - For S/O/D scoring rubrics, see
references/scoring-rubrics.md
© asgard-ai-platform, 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 3 other files (references) in algo-mfg-fmea of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/aiag-vda-ap.md
- references/scoring-rubrics.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Mfg Fmea 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 |
|---|---|---|---|---|---|---|
| Algo Mfg Fmea this skillasgard-ai-platform/skills | 242 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Product Launch Legal Reviewanthropics/claude-for-legal | 9.6k | 2 repos | ~5k | Automated safety check: Pass | Apache-2.0 | |
| Legal Risk Visualizationzh-xx/legal-assistant-skills | 174 | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Contract Renewal Trackeranthropics/claude-for-legal | 9.6k | 2 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Deep Risk Analysiszubair-trabzada/ai-legal-claude | 1.8k | — | ~1.9k | Automated safety check: Pass | None | |
| Canghe Tianyanchafreestylefly/canghe-skills | 461 | — | ~2.4k | Automated safety check: Pass | None |
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Shows which contracts renew soon and when notice must be sent by, working from a maintained renewal register, and warns about missed cancellation windows.
Deep Risk Analysis
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Clause-by-clause contract risk analysis with severity scoring, financial exposure estimates, and prioritized remediation guidance
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Generates Tianyancha-style company and industry insight dashboards from researched enterprise data.
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Related
Categories
Questions about Algo Mfg Fmea
What does Algo Mfg Fmea do?
Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes. Algo Mfg Fmea is an agent skill from asgard-ai-platform/skills. Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes.
When should I use Algo Mfg Fmea?
Algo Mfg Fmea fits situations like: the user needs to assess product; prioritize corrective actions; build a risk register — even if they say failure mode analysis; risk assessment.
How do I install Algo Mfg Fmea in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-mfg-fmea -a claude-code`. Or copy the skill folder (algo-mfg-fmea in asgard-ai-platform/skills) into .claude/skills/algo-mfg-fmea in your project. Claude Code loads it when a task matches its description.
How do I install Algo Mfg Fmea in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-mfg-fmea -a codex`. Or copy the skill folder (algo-mfg-fmea in asgard-ai-platform/skills) into .agents/skills/algo-mfg-fmea in your project. Codex loads it when a task matches its description.
Can I use Algo Mfg Fmea 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 asgard-ai-platform/skills --skill algo-mfg-fmea -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-mfg-fmea, .gemini/skills/algo-mfg-fmea, .github/skills/algo-mfg-fmea and .opencode/skills/algo-mfg-fmea in your project.
What does Algo Mfg Fmea need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Mfg Fmea is instructions for the agent only.
Does Algo Mfg Fmea 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 Algo Mfg Fmea safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Algo Mfg Fmea use?
Algo Mfg Fmea is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Algo Mfg Fmea use?
About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.1k tokens, read only when the agent opens those files.
What are the alternatives to Algo Mfg Fmea?
Skills that share tags, products or a category with Algo Mfg Fmea: Product Launch Legal Review (anthropics/claude-for-legal, 9.6k stars), Legal Risk Visualization (zh-xx/legal-assistant-skills, 174 stars), Contract Renewal Tracker (anthropics/claude-for-legal, 9.6k stars) and Deep Risk Analysis (zubair-trabzada/ai-legal-claude, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Mfg Fmea?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.