Borrowing it
Nothing to install: this file belongs to Kinneyzhang/source-faithful-summary-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Kinneyzhang/source-faithful-summary-skill/main/AGENTS.mdgit clone --depth 1 https://github.com/Kinneyzhang/source-faithful-summary-skillWrote 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/instructions/kinneyzhang/source-faithful-summary-skill/agents-md)<a href="https://agentmods.dev/instructions/kinneyzhang/source-faithful-summary-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/kinneyzhang/source-faithful-summary-skill/agents-md/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/instructions/kinneyzhang/source-faithful-summary-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/kinneyzhang/source-faithful-summary-skill/agents-md.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 | $0.00389 | $0.00389 |
| Opus 5 | $0.00195 | $0.00195 |
| Sonnet 5 | $0.00078 | $0.00078 |
| Haiku 4.5 | $0.00039 | $0.00039 |
Grade A, and why
source-faithful-summary-skill AGENTS.md 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 8d 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.
What it actually says
AGENTS.md — Source-Faithful Summary Skill
Use this project as an instruction pack for source-faithful summarization.
Core behavior
Before writing a summary, do not jump straight into prose. First build intermediate artifacts:
- Source type classification.
- Source map with timestamps or section references.
- Named concepts and their roles.
- Failure modes, tensions, or default workflow being rejected.
- Action chain or argument chain.
- Decision-model map.
- Main-thread audit.
- Reverse-summary audit.
Then write the final summary.
Optimization target
Prioritize:
source fidelity > elegant argument
attention weight > concept density
evidence > vibes
decision model > isolated tips
Practical/workflow material
For workflows, demos, engineering talks, tutorials, and practical interviews, use this extraction chain:
failure mode → underlying constraint → speaker/author judgment → concrete action → tradeoff/limit
Rules
- Do not turn the source into a general concept essay unless explicitly asked.
- Do not let support concepts steal the spine.
- Preserve concrete examples and named concepts that control later decisions.
- Mark external analysis separately from source summary.
- If transcript/source quality is limited, say so explicitly.
- If the user provides critique from a knowledgeable reader/viewer/listener, audit the method; do not merely patch one paragraph.
Files
- Read
SKILL.mdfor the canonical workflow. - Use
templates/source-map.mdto structure extraction. - Use
templates/summary-outline.mdfor final prose.
Long sources
For sources longer than ~50k characters, longer than ~60 minutes, or multi-speaker/panel transcripts, use templates/long-source-plan.md before summarizing. State chunking/sampling strategy, chapter coverage, recurrence checks, speaker coverage, and evidence budget.
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.
- 8d ago First seen · 57 lines · 389 tokens per session scan A 77bc69687c8b
source-faithful-summary-skill AGENTS.md is an instructions file published in the GitHub repository Kinneyzhang/source-faithful-summary-skill (2 stars, last pushed 3mo ago), licensed MIT. It adds 389 tokens to every session, about $0.0019 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 instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.