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 skills add wsbm393/Fable-Skill --skill fable-skillgit clone --depth 1 https://github.com/wsbm393/Fable-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/skills/wsbm393/fable-skill/fable-skill)<a href="https://agentmods.dev/skills/wsbm393/fable-skill/fable-skill"><img src="https://agentmods.dev/badge/skills/wsbm393/fable-skill/fable-skill.svg" alt="Measured on agentmods" 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 | $0.00068 | $0.01494 |
| Opus 5 | $0.00034 | $0.00747 |
| Sonnet 5 | $0.00014 | $0.00299 |
| Haiku 4.5 | $0.00007 | $0.00149 |
Grade A, and why
executing-hard-tasks 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 3d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executing Hard Tasks
Overview
Core principle: a plan is a hypothesis; a checkpoint is its test. Prove hypotheses cheaply, in risk order; re-derive the plan whenever an observation contradicts it. The basics — recon first, honest results, scope discipline — remain binding; this skill adds the bar on top of them, it does not replace them.
The skill shapes behavior, not prose: users get plain engineering language — no skill vocabulary ("unspiked", "buckets"), no announcements of compliance.
Decomposing
Contract for every plan:
- Done, as an observation the user would accept. "Done means: [command/action] shows [result]" — someone holding only the original request must agree it entails that observation. Can't state it → the task is underspecified: ask, or record the assumption as a DECISION line, before writing dependent code.
- State the budget, then thread it. Put the budget (wall-clock or steps) in the plan header. First build target: the thinnest end-to-end slice through the riskiest integration path (auth, proxies, uploads, unfamiliar glue) — one row, one event, one request — proven inside the first quarter of that budget, through the surface the user will actually use. Layer the rest onto the proven thread. Validation and error handling on the thread are part of the thread; only performance and cosmetics may be deferred, each with its own checkpoint or a "Not checked" line in the final report.
- Checkpoint every subtask, fixed at plan time. A checkpoint is a command whose output you will paste, or an externally observable result (HTTP response, rendered output, screenshot). Self-review of your own edits never qualifies. More than two subtasks without an executed checkpoint = an end-loaded plan, whatever its phases are called. Weakening or replacing a checkpoint after it fails is itself a red checkpoint — report it as a plan change.
- Spike before you build on a recommendation. Prove an architecture's critical integration point with the cheapest end-to-end experiment before implementing it as the chosen path — whether or not anyone is asked to approve it. An unproven option may be described, but the description must name exactly what is unproven and the rework cost if it fails.
- DECISION lines for load-bearing choices. A choice that changes what data exists or what users observe (skip vs update duplicates; suppress-at-write vs suppress-at-read) appears in the user-facing plan as "DECISION: X over Y because Z". If it destroys or transforms persisted data, it gates on the user like any irreversible action.
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.
- 3d ago First seen · 70 lines · 68 tokens per session scan A b6a15efcbe56
executing-hard-tasks is a skill published in the GitHub repository wsbm393/Fable-Skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 1,494 once invoked, about $0.0003 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 2026-09-04.
Other skills, from other repositories
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
pinchtab-mcp
Use this skill when a task requires browser automation through PinchTab's MCP server connected to a remote browser instance. Covers navigation, element interaction, data extraction, form filling, multi-step flows, and session management via MCP tools.
peekaboo
Capture and automate macOS UI with the Peekaboo CLI.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.