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/melodic-software/claude-code-plugins/shapenpx skills add melodic-software/claude-code-plugins --skill shapegit clone --depth 1 https://github.com/melodic-software/claude-code-pluginsWhat 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 | $0.00209 | $0.02302 |
| Opus 5 | $0.00105 | $0.01151 |
| Sonnet 5 | $0.00042 | $0.00460 |
| Haiku 4.5 | $0.00021 | $0.00230 |
Grade E, and why
shape scanned grade E with 2 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
- Weak: "Hope that helps. Let me know if you want to dig deeper." <!-- ai-slop-ignore: quotes the tell it documents --> Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
2. **A destructive action is ahead** (`rm -rf`, force push, schema migration, How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shape output for an ADHD reader
Once invoked, this is a standing instruction: shape every response for the rest of this session in the form below, not just the next one. The reader has ADHD. The output is not merely short. It is arranged so an ADHD brain can act on it.
Before applying: conflicting-shaper check
Scan the session context for another active output-shaping discipline: hook-injected instructions from a terse-for-tokens shaper (e.g. caveman's SessionStart/UserPromptSubmit context), or any standing instruction that strips words to save tokens. If one is active, surface the conflict before applying: name the conflicting source, say the two disciplines pull in opposite directions on the same axis (structure-for-the-reader vs strip-for-the-budget), and ask the user to pick one for this session. Do not silently apply both. The mix is contradictory and unpredictable. This is an advisory check: skills cannot detect or disable hooks mechanically, so name what the context shows and let the user decide.
Turning it off
The posture ends when the user says so. "stop shaping" or "normal output"
reverts to unshaped responses for the rest of the session (re-invoke
/adhd:shape to turn it back on). Treat close variants ("drop the ADHD
format") the same way.
Five facts that drive every rule
- Working memory is small. Anything off-screen is gone. Restate any earlier state the reader needs, in the current response.
- Knowing is not doing. The gap between understanding an answer and executing it is where the work stalls. Close it.
- Starting is the hardest step. The first action must be obvious, small, and doable right now.
- Time reads as uniform. "A bit of work" and "a few hours" land the same. Vague estimates fail.
- Dopamine is scarce. Visible progress registers; progress buried in prose does not.
The rules
1. Lead with the next action
The first line is a thing the reader can do. Not context, not a plan, the action. If the answer is a command, a path, or a snippet, it goes first; prose follows only if it earns its place.
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 · 203 lines · 209 tokens per session scan E f40e266b9ba4
shape is a skill published in the GitHub repository melodic-software/claude-code-plugins (12 stars, last pushed 2d ago), licensed MIT. It adds 209 tokens to every session and 2,302 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it E with 2 findings (hidden instructions, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
deep-research
Conducts iterative deep research on any topic using web search, progressive exploration, and structured synthesis. Use when asked for comprehensive research, deep investigation, thorough analysis, or multi-source exploration of any topic. Triggers: research, investigate, deep dive, comprehensive analysis, explore…
error-ux
Principles and patterns for writing error messages that help users recover. Use when auditing, writing, or improving error messages in code. Triggers: error messages, user experience, error handling, exception messages, validation errors.
adversarial-patterns
Library of realistic adversarial attack vectors and anti-patterns to avoid. Contains examples of valid attacks and subtle gaming patterns to reject.
documentation-testing
Provides heuristics for identifying incomplete or broken documentation. Use when validating README setup instructions, testing onboarding flows, or auditing documentation quality. Triggers: docs, readme, onboarding, setup validation, documentation audit.
adversarial-analysis
Analyze code to identify explicit contracts, implicit usage patterns, and realistic boundary conditions. Contains concrete formulas for calculating input realism limits. Use before generating adversarial tests.
propagate-then-search
For constraint problems: eliminate impossibilities before guessing, reduce search space through inference, fail fast on contradictions.