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/kintecus/build/process-documentnpx skills add kintecus/build --skill process-documentgit clone --depth 1 https://github.com/kintecus/buildWhat 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 | $0.00038 | $0.00852 |
| Opus 5 | $0.00019 | $0.00426 |
| Sonnet 5 | $0.00008 | $0.00170 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
process-document 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process Document
Convert a raw document into a structured, searchable markdown summary.
Usage
/process-document projects/{project}/raw/{filename}
Transcript detection
Detect call transcripts via two signals:
- Path: folder name contains
call-transcriptsOR filename containsrecording,transcript,sync,meeting,call - Content: 3+ unique speaker labels in first 50 lines, in either format:
**Speaker Name**(newer .md transcripts)Speaker Name 0:00(older .txt with timestamps)
Either signal is sufficient. Both together = high confidence.
Workflow
- Read the source document
- If transcript detected: run the transcript cleaning sub-step (see below), then use the cleaned file as input for step 3
- Generate a structured markdown summary (see format below)
- Save to
projects/{project}/docs/with a matching name (e.g.,raw/spec.pdfbecomesdocs/spec.md) - Update
projects/{project}/docs/INDEX.mdwith a one-line description - If transcript was cleaned: include both raw and cleaned source paths in the summary header
Transcript cleaning
When a transcript is detected, before summarizing:
- Read transcript-cleaning.md (relative to this skill) for the full cleaning instructions
- Use the
Agenttool to spawn a cleaning sub-agent:model: "sonnet"subagent_type: "general-purpose"- Prompt must include:
- The cleaning instructions from the reference file
- The full raw transcript content
- Paths to project context files for the sub-agent to read:
projects/{project}/docs/transcript-context.md,projects/{project}/docs/glossary.md,projects/{project}/BRIEF.md - The original filename (for the
sourcefrontmatter field)
- The sub-agent reads context files, applies ASR corrections, and returns the cleaned transcript
- For non-English transcripts, the sub-agent also translates to English (per the cleaning instructions)
- Save the sub-agent's output as
{original_path}/{original_name}_cleaned.md(same folder as the raw file) - Proceed with the cleaned transcript as input to the summary step
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.
- yesterday First seen · 103 lines · 38 tokens per session scan A 9bad4c4d0af0
process-document is a skill published in the GitHub repository kintecus/build (2 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 852 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…