Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/daffy0208/ai-dev-standardsnpx agentmods add skills/daffy0208/ai-dev-standards/manifest-generatorWrote 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/daffy0208/ai-dev-standards/manifest-generator)<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/manifest-generator"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/manifest-generator/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/daffy0208/ai-dev-standards/manifest-generator"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/manifest-generator.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 | $0.00023 | $0.02240 |
| Opus 5 | $0.00012 | $0.01120 |
| Sonnet 5 | $0.00005 | $0.00448 |
| Haiku 4.5 | $0.00002 | $0.00224 |
Grade A, and why
Manifest Generator 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 10d 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Manifest Generator
Auto-generate capability manifests from skill/MCP descriptions using Codex
Purpose
Uses OpenAI Codex to analyze existing skills, MCPs, tools, and components to automatically generate their capability manifests. This bootstraps the entire orchestration system by inferring preconditions, effects, domains, and relationships from descriptions and implementations.
When to Use
- Bootstrapping: Generate manifests for all 59 skills + 50 MCPs at once
- New resources: Auto-generate manifest when creating new skills/MCPs
- Updates: Regenerate manifest when skill description changes
- Validation: Compare generated manifest with existing to detect drift
Key Capabilities
- Precondition Inference: Analyzes skill description to determine what must exist before use
- Effect Extraction: Identifies state changes the skill produces
- Domain Detection: Categorizes skill into technical domains
- Relationship Discovery: Infers which skills this enables/conflicts with/composes with
- Risk Assessment: Evaluates cost, latency, and risk level from description
Inputs
inputs:
skill_path: string # Path to skill directory (e.g., SKILLS/rag-implementer)
resource_type: string # "skill" | "mcp" | "tool" | "component" | "integration"
description_file: string # Usually SKILL.md or README.md
implementation_file: string # Optional: actual code file for validation
output_path: string # Where to write manifest.yaml (default: same directory)
Process
Step 1: Read Source Materials
# Read skill description
DESCRIPTION=$(cat $skill_path/SKILL.md)
# Read implementation if available
if [ -f "$skill_path/index.js" ]; then
IMPLEMENTATION=$(head -100 $skill_path/index.js)
fi
# Read existing registry entry
REGISTRY_ENTRY=$(jq ".[] | select(.name==\"$skill_name\")" META/skill-registry.json)
Step 2: Generate Manifest with Codex
codex exec "
Analyze this ${resource_type} and generate a capability manifest.
DESCRIPTION:
${DESCRIPTION}
IMPLEMENTATION (if available):
${IMPLEMENTATION}
REGISTRY ENTRY:
${REGISTRY_ENTRY}
Generate a YAML manifest matching this schema:
$(cat SCHEMAS/capability-manifest.schema.json)
Infer the following:
1. PRECONDITIONS: What files, dependencies, or state must exist?
Examples:
- file_exists('package.json')
- has_dependency('react')
- env_var_set('OPENAI_API_KEY')
- not file_exists('.vector-index')
2. EFFECTS: What does this create/modify/delete?
Examples:
- creates_vector_index
- adds_auth_middleware
- configures_database
- updates_tests
3. DOMAINS: What technical areas does it touch?
Examples: rag, auth, api, database, testing, nextjs, react
4. COMPATIBILITY:
- requires: What must exist first?
- conflicts_with: What can't coexist?
- composes_with: What works well together?
- enables: What does this unlock?
5. RISK ASSESSMENT:
- cost: free/low/medium/high (API calls, compute)
- latency: instant/fast/slow (execution time)
- risk_level: safe/low/medium/high (side effects)
6. SUCCESS SIGNAL: How do we know it worked?
Examples:
- 'tests pass'
- 'file exists: .vector-index'
- 'HTTP 200 from /api/search'
- 'can query vector database'
Output ONLY valid YAML. No explanatory text.
"
What ships with it
2 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.
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.
- 10d ago First seen · 324 lines · 23 tokens per session scan A 6089c0b3887b
Manifest Generator is a skill published in the GitHub repository daffy0208/ai-dev-standards (36 stars, last pushed 8mo ago), licensed MIT. It adds 23 tokens to every session and 2,240 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
subagent-dev
Execute implementation plans using fresh subagents per task with two-stage review. Auto-triggered for plans with multiple independent tasks. Also available as /subagent-dev.
clarify
Turn vague requests into structured, testable specs through collaborative design. Auto-triggered when requirements are ambiguous. Explores intent, constraints, and alternatives before implementation. Also available as /clarify.
plan
Create a detailed implementation plan with bite-sized tasks. Auto-triggered for non-trivial work. Includes design exploration, devil's advocate challenge, and subagent review. Also available as /plan.
review
Two-stage code review: spec compliance first, then code quality. Auto-triggered after implementation. Also available as /review.
verify
Verification before completion — no success claims without fresh evidence. Auto-triggered before any completion claim. Also available as /verify.
devils-advocate
Challenge a plan, design, or PR by finding every flaw, risk, and wrong assumption. Use before committing to an architecture or shipping a major change.