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 assafkip/kipi-system --skill fable-disciplinegit clone --depth 1 https://github.com/assafkip/kipi-systemWrote 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/assafkip/kipi-system/fable-discipline)<a href="https://agentmods.dev/skills/assafkip/kipi-system/fable-discipline"><img src="https://agentmods.dev/badge/skills/assafkip/kipi-system/fable-discipline/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/assafkip/kipi-system/fable-discipline"><img src="https://agentmods.dev/badge/skills/assafkip/kipi-system/fable-discipline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 17 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Excessive Agency · line 53 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00137 | $0.02742 |
| Opus 5.5 | $0.00055 | $0.01097 |
| Sonnet 5.5 | $0.00027 | $0.00548 |
| Haiku 4.5 | $0.00014 | $0.00274 |
Grade A, and why
fable-discipline 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 18d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- fable-discipline — 97% identical, 71 lines differ
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable-Discipline
Coding and task discipline, distilled from reading thousands of edits by one capable model and grading them against an independent review. It is additive: it does not replace what your model already does well, it adds the habits that strong engineers have and most code generation skips.
Most of it is judgment the skill teaches. One slice is enforced by a hook, because a rule a model can talk itself out of is a suggestion, not a gate. The skill teaches; the hook makes the one checkable habit non-optional.
Position: the execution-discipline layer of prd-os
This skill is not a sibling system to prd-os; it is prd-os's execution-discipline layer (merged 2026-07-04, see the plugin CHANGELOG). Two load paths, one skill:
- PRD work:
/issue-startloads this skill before the first edit of every DSSE issue. The issue's receipts (verified, reviewed, findings_triaged) are the task-level half of the same contract this skill states per-edit. - Non-PRD work: the quick-plan fast path and the fable-discipline auto-invoke rule load it for any coding task bigger than a one-line change.
The public standalone export (github.com/assafkip/fable-discipline) mirrors this
copy; scripts/export-fable-mirror.sh --check is the drift blocker.
When NOT to use this
One-line changes, typo fixes, a task with one obvious approach that fits in a single pass. Staging a trivial task buries the answer under ceremony. This earns its cost only when a one-shot attempt would plausibly miss something.
Layer 1: running the task
How to move through complex work without shipping a confident wrong answer.
-
Stage before you act. Write the stage plan first. Number the stages, name the one checkable artifact each produces. If a stage produces nothing checkable, merge it into the next. The map is living, not a contract; update it when what you learn invalidates it.
-
Verify each stage with a check that can fail. A test that runs, a file that provably exists in the right shape, a source actually read, an output diffed against the spec. "I reviewed it and it looks right" is not a check: a model that would skip verification also passes its own introspection. If a stage has no failable check, say so and mark its output unverified so the gap is visible.
What ships with it
4 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.
- 18d ago Changed · +35 lines 18118fa00437
- 1mo ago Changed · +7 lines cc8abb67ebdd
- 1mo ago First seen · 159 lines · 137 tokens per session scan A 3ca41af99ea9
fable-discipline is a skill published in the GitHub repository assafkip/kipi-system (121 stars, last pushed 2d ago), licensed MIT. It adds 137 tokens to every session and 2,742 once invoked, about $0.0005 per session on Opus 5.5. 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
format
MIF (Modeled Information Format) 1.4.2 Level 3 memory templates and formatting guidelines.
core
This skill should be used when the user says "capture memory", "save to memory", "remember this", or trigger phrases like: "I've decided", "let's use", "we're going with", "I learned", "turns out", "TIL", "discovered", "I'm stuck", "blocked by", "always use", "never do", "convention is". Also triggers on recall…
custodian
Memory system custodian for health checks, validation, and maintenance. Trigger phrases: "check memory health", "validate memories", "fix broken links", "update decay", "relocate memories", "audit memories", "memory maintenance", "custodian", "memory health".
blackboard
Cross-session handoff, persistent context via blackboard, and agent coordination patterns.
ontology
Ontology-based entity discovery and validation for mnemonic memories. Define custom namespaces, entity types, traits, and relationships. Triggers: "entity discovery", "validate ontology", "define namespaces", "resolve entity references", "list ontologies", "show namespaces", "entity types", "entity relationships"…
search
This skill should be used when the user says "search memories", "find in memories", "grep mnemonic", "look for memory", "deep search", "synthesize knowledge", or asks questions like "what do we know about X". Provides progressive disclosure and enhanced iterative search with synthesis.