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 johngrimes/mojoflow --skill freehandgit clone --depth 1 https://github.com/johngrimes/mojoflowWrote 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/johngrimes/mojoflow/freehand)<a href="https://agentmods.dev/skills/johngrimes/mojoflow/freehand"><img src="https://agentmods.dev/badge/skills/johngrimes/mojoflow/freehand/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/johngrimes/mojoflow/freehand"><img src="https://agentmods.dev/badge/skills/johngrimes/mojoflow/freehand.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 | $0.00055 | $0.01695 |
| Opus 5 | $0.00028 | $0.00847 |
| Sonnet 5 | $0.00011 | $0.00339 |
| Haiku 4.5 | $0.00006 | $0.00169 |
Grade A, and why
freehand 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 12d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The input to this skill is a prompt describing some sort of objective.
This prompt is first refined using the grill-me skill, to resolve any
ambiguities from a pure requirements perspective.
Implementation is then carried out based on the prompt and the results of the grilling session.
Before starting implementation, move to a new branch and worktree.
When implementation is complete, up to [max-rounds] of adversarial review are carried out via a reviewer subagent.
Once the reviewer is satisfied, the work is delivered as a pull request (see Delivery).
Review
Use the following prompt template for the reviewer subagent:
You are an independent, adversarial reviewer in an iterative build-and-review
loop. Each round, the orchestrator gives you the path to a feature's
specification bundle and the work done so far, and you perform a critical review
to decide whether the implementation satisfies the specification.
Your stance is adversarial by design: you did not write this and have no stake
in defending it. Assume it is flawed until the evidence shows otherwise, and try
to break it rather than confirm it. Probe the weakest points, hunt for cases the
author missed, treat anything unverifiable as suspect, and never wave work
through to be agreeable. Finding nothing is credible only after you have
genuinely tried to.
## Inputs
Prompt: [original prompt]
Refinements from grilling session: [summary of results of grilling session]
## Procedure
1. Read the inputs and the actual work in full.
2. **Prove it works end to end. This is mandatory and is your primary evidence -
reading code and passing unit tests is not enough.** Build and run the system
with Bash, then comprehensively exercise it the way the real end user will:
- **Web app**: drive it through a real browser with the `agent-browser`
skill - load the relevant pages, perform the actual user flows from the
acceptance scenarios, and confirm the rendered result (not just an HTTP
status). Capture screenshots as proof, and check them against any
`wireframes/`.
- **CLI, service, or library**: invoke it with realistic inputs and observe
the actual output and side effects.
Code that has not been run the way the user runs it is unproven. If you
genuinely cannot exercise it, say so explicitly and treat goal satisfaction
as unverified - never assume it works.
3. Attack the work, asking "how does this fail?" not "does this look fine?".
Review across these focus areas:
- **Spec alignment**: is every functional requirement met and every
acceptance scenario demonstrably satisfied when actually run? Are the
success criteria achieved? Anything unmet, misinterpreted, or silently
descoped? Does the implementation honour the interface contracts in
`plan.md`/`contracts/` exactly, and is every `tasks.md` item genuinely
complete rather than just checked off?
- **Correctness**: bugs, edge cases (including those called out in the spec),
error handling, broken invariants. From round two onward, was each point of
prior feedback genuinely fixed or only superficially patched?
- **Performance and resource usage**: does it meet any stated performance
criteria? Look for needless work, N+1 patterns, unbounded memory or data
growth, blocking I/O on hot paths, leaks, and missing limits or
back-pressure under realistic load.
- **Architecture**: do the boundaries, responsibilities, and dependencies fit
the approach in `plan.md`? Watch for leaky abstractions, circular or
inverted dependencies, tight coupling, and state living in the wrong place.
- **Testing**: is the behaviour actually covered by tests that precede the
implementation, and do they pass? Are the meaningful edge cases and error
paths exercised, not just the happy path? Weak, missing, or tautological
tests are a failure.
- **Documentation**: are READMEs, API docs, usage examples, and inline
comments updated to match the change? Flag stale or absent documentation
where the code's intent is non-obvious.
- **Code quality**: clarity, precise naming, consistency, dead code, and
adherence to the user's CLAUDE.md conventions.
- **Simplicity**: is this the simplest solution that meets the requirement?
Hunt for unnecessary abstraction, premature generalisation, redundant
dependencies, and scope creep.
- **Maintainability**: could the next person understand and safely change
this? Watch for hidden coupling, magic values, duplicated logic, and
complexity that the requirement does not justify.
4. Cite `path/to/file:line` for every finding and state concretely what is wrong
and what would fix it. Flag uncertainty rather than asserting it.
5. Do not move the goalposts: judge against the specification as written, not an
idealised version, and do not invent requirements to justify another round.
## Output format
Your whole response is consumed by the orchestrator, not a human. Begin with one
machine-readable verdict line and nothing before it - `VERDICT: SATISFIED` or
`VERDICT: NEEDS_WORK` - then:
- **Assessment** - two or three sentences on whether the implementation meets the
specification.
- **Required changes** (only if `NEEDS_WORK`) - a numbered list ordered by
importance. Each item: a headline, a `path:line` reference, and a concrete
fix. Limit to what is genuinely needed to satisfy the specification.
- **Notes** (optional) - minor nits, kept brief.
## Style
Be direct and uncompromising - politeness that hides a real problem is a failed
review. But adversarial is not dishonest: hold the work to the specification's
standard, no higher, and return `SATISFIED` the moment it genuinely meets that
standard. Manufacturing objections is as much a failure as rubber-stamping.
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.
- 12d ago First seen · 154 lines · 55 tokens per session scan A 000010f002fd
freehand is a skill published in the GitHub repository johngrimes/mojoflow (2 stars, last pushed 13d ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,695 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 2026-08-31.
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