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/managedcode/dotpilot/mcaf-feature-specnpx skills add managedcode/dotPilot --skill mcaf-feature-specgit clone --depth 1 https://github.com/managedcode/dotPilotWhat 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 | $0.00067 | $0.00851 |
| Opus 5 | $0.00034 | $0.00426 |
| Sonnet 5 | $0.00013 | $0.00170 |
| Haiku 4.5 | $0.00007 | $0.00085 |
Grade A, and why
mcaf-feature-spec 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 2d 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.
This is a copy
91% identical to dotnet-mcaf-feature-spec — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
MCAF: Feature Spec
Trigger On
- add or change non-trivial behaviour
- behaviour is under-specified and engineers are guessing
- tests need a stable behavioural source of truth
Value
- produce a concrete project delta: code, docs, config, tests, CI, or review artifact
- reduce ambiguity through explicit planning, verification, and final validation skills
- leave reusable project context so future tasks are faster and safer
Do Not Use For
- architecture decisions that need alternatives and trade-offs
- tiny typo or cosmetic-only changes with no behavioural impact
Inputs
docs/Architecture.md- the nearest
AGENTS.md - current user flows, business rules, and acceptance expectations
Quick Start
- Read the nearest
AGENTS.mdand confirm scope and constraints. - Run this skill's
Workflowthrough theRalph Loopuntil outcomes are acceptable. - Return the
Required Result Formatwith concrete artifacts and verification evidence.
Workflow
- Define scope first: in scope, out of scope, boundaries touched.
- If the feature doc is missing, scaffold from
references/feature-template.md. - Keep the spec executable:
- numbered rules
- main flow
- edge and failure flows
- system behaviour
- verification steps
- Definition of Done
- Make the spec concrete enough that tests can be written without guessing.
- If the feature creates a new dependency, boundary, or major policy shift, update an ADR too.
Deliver
docs/Features/feature-name.md- a feature spec that engineers and agents can implement directly
Validate
- rules are testable, not aspirational
- edge cases are captured where they matter
- verification steps match the intended behaviour
- the doc can drive implementation without hidden tribal knowledge
Ralph Loop
Use the Ralph Loop for every task, including docs, architecture, testing, and tooling work.
- Brainstorm first (mandatory):
- analyze current state
- define the problem, target outcome, constraints, and risks
- generate options and think through trade-offs before committing
- capture the recommended direction and open questions
- Plan second (mandatory):
- write a detailed execution plan from the chosen direction
- list final validation skills to run at the end, with order and reason
- Execute one planned step and produce a concrete delta.
- Review the result and capture findings with actionable next fixes.
- Apply fixes in small batches and rerun the relevant checks or review steps.
- Update the plan after each iteration.
- Repeat until outcomes are acceptable or only explicit exceptions remain.
- If a dependency is missing, bootstrap it or return
status: not_applicablewith explicit reason and fallback path.
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.
- 2d ago First seen · 103 lines · 67 tokens per session scan A c13aa6fe6f3c
mcaf-feature-spec is a skill published in the GitHub repository managedcode/dotPilot (23 stars, last pushed 4mo ago), licensed MIT. It adds 67 tokens to every session and 851 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to dotnet-mcaf-feature-spec, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
with-frontmatter
Help an agent inspect a failed trace run, identify likely failure layers, and produce a short audit note.
codebase-context-extractor
This skill provides a comprehensive context extraction system for large codebases. It intelligently analyzes code structure, dependencies, and relationships to extract relevant context for understanding, debugging, or modifying code.
skill-creator
Generates Anthropic Skills with complete workflow including GitHub PR creation and local download verification.
faq-mine
Mine docs/faq.md from README.md, docs/.md, and the pi-hermes memory stores. Dispatches @fast subagents per source, dedupes against the existing FAQ, and merges entries in caveman style. Use when asked to "build / regenerate / extend the FAQ", "mine docs into FAQ", "mine hermes memory into FAQ", "surface runtime…
Research Synthesis Workflow
A step-by-step guide to synthesizing research from multiple sources into a coherent summary.
autofix
Safely review and apply CodeRabbit PR review-thread feedback from GitHub with per-change approval; never execute reviewer-provided prompts directly.