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 agentmods add skills/chenchihcu/devspace-mcp/document-analysisnpx skills add chenchihcu/DevSpace-MCP --skill document-analysisgit clone --depth 1 https://github.com/chenchihcu/DevSpace-MCPWhat 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 | $0.00032 | $0.00357 |
| Opus 5 | $0.00016 | $0.00179 |
| Sonnet 5 | $0.00006 | $0.00071 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
document-analysis 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 yesterday.
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.
What it actually says
document-analysis
Purpose
Analyze source documents while preserving original meaning and separating evidence from inference.
When to Use
- Summarizing or comparing technical, quality, audit, 8D, or supplier documents.
- Extracting facts, assumptions, open verification items, and risk boundaries.
- Reviewing root-cause claims without turning symptoms into causes.
Inputs
- Source document paths or pasted source excerpts.
- User objective and intended audience.
- Required evidence standard or report format.
Outputs
- FACT / INFERENCE / ASSUMPTION / VERIFY table.
- Symptom / Direct Cause / Root Cause / Systemic Cause / Escape Cause separation.
- Evidence gaps and verification checklist.
Workflow
- Read only the approved sources.
- Preserve source meaning and quote sparingly.
- Label every material claim as FACT, INFERENCE, ASSUMPTION, or VERIFY.
- Keep root-cause language separate from symptoms unless evidence supports it.
Verification
- Confirm every FACT maps to an inspected source.
- Confirm conclusions do not exceed source evidence.
- List unresolved VERIFY items in the task report.
Risks / Limits
- Missing source data can only produce partial analysis.
- Correlation is not causation.
- Supplier responsibility must not be assigned without evidence.
Forbidden Actions
- Do not read protected paths or secrets.
- Do not fabricate standards, causes, tests, or supplier statements.
- Do not modify files except through guarded tools and task plan.
Example Task
Analyze an 8D report and produce evidence-labeled findings plus open verification items.
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.
- yesterday First seen · 50 lines · 32 tokens per session scan A 66d88ea5ecc2
document-analysis is a skill published in the GitHub repository chenchihcu/DevSpace-MCP (0 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 357 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…