Continuous-Claude-v3 is a Claude Code development environment that preserves working context between sessions, coordinates specialized agents, and stores project knowledge through ledgers, handoffs, and analysis tools. It is for people using Claude Code on ongoing or complex software work. Its catalogue entries are the skills, agents, hooks, plugin, and setting that provide its workflows and orchestration.
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 parcadei/Continuous-Claude-v3 --skill first-order-odesgit clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3Wrote 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/parcadei/continuous-claude-v3/first-order-odes)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/first-order-odes"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/first-order-odes/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/parcadei/continuous-claude-v3/first-order-odes"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/first-order-odes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Agent Snooping · line 75 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00018 | $0.00976 |
| Opus 5 | $0.00009 | $0.00488 |
| Sonnet 5 | $0.00004 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
first-order-odes 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 9d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
First Order Odes
When to Use
Use this skill when working on first-order-odes problems in odes pdes.
Decision Tree
-
Classify the ODE
- Linear: y' + P(x)y = Q(x)?
- Separable: y' = f(x)g(y)?
- Exact: M(x,y)dx + N(x,y)dy = 0 with dM/dy = dN/dx?
- Bernoulli: y' + P(x)y = Q(x)y^n?
-
Select Solution Method
Type Method Separable Separate and integrate Linear Integrating factor e^{int P dx} Exact Find potential function Bernoulli Substitute v = y^{1-n} -
Numerical Solution (IVP)
scipy.integrate.solve_ivp(f, [t0, tf], y0, method='RK45')- For stiff systems:
method='Radau'ormethod='BDF' - Adaptive step size: specify rtol/atol, not step size
-
Verify Solution
- Substitute back into ODE
- Check initial/boundary conditions
sympy_compute.py dsolve "y' + y = x" --ics "{y(0): 1}"
-
Phase Portrait (Autonomous)
- Find equilibria: f(y*) = 0
- Analyze stability: sign of f'(y*)
z3_solve.py solve "dy/dt == 0"
Tool Commands
Scipy_Solve_Ivp
uv run python -c "from scipy.integrate import solve_ivp; sol = solve_ivp(lambda t, y: -y, [0, 5], [1]); print('y(5) =', sol.y[0][-1])"
Sympy_Dsolve
uv run python -m runtime.harness scripts/sympy_compute.py dsolve "Derivative(y,x) + y" --ics "{y(0): 1}"
Z3_Equilibrium
uv run python -m runtime.harness scripts/z3_solve.py solve "f(y_star) == 0"
Key Techniques
From indexed textbooks:
- [Elementary Differential Equations and... (Z-Library)] Solving ODEs with MATLAB (New York: Cambridge REFERENCES cyan black NJ: Prentice-Hall, 1971). Mattheij, Robert, and Molenaar, Jaap, Ordinary Differential Equations in Theory and Practice Shampine, Lawrence F. Numerical Solution of Ordinary Differential Equations (New York: Chapman and Shampine, L.
- [Elementary Differential Equations and... (Z-Library)] Differential Equations: An Introduction to Modern Methods and Applications (2nd ed. Use the Laplace transform to solve the system 2e−t 3t α1 α2 , where α1 and α2 are arbitrary. How must α1 and α2 be chosen so that the solution is identical to Eq.
- [An Introduction to Numerical Analysis... (Z-Library)] Modern Numerical Methods for Ordinary Wiley, New York. User's guide for DVERK: A subroutine for solving non-stiff ODEs. Keller (1966), Analysis of Numerical Methods.
- [Elementary Differential Equations and... (Z-Library)] Show that the rst order Adams–Bashforth method is the Euler method and that the rst order Adams–Moulton method is the backward Euler method. Show that the third order Adams–Moulton formula is yn+1 = yn + (h/12)(5fn+1 + 8fn − fn−1). Derive the second order backward differentiation formula given by Eq.
- [An Introduction to Numerical Analysis... (Z-Library)] Test results on initial value methods for non-stiff ordinary differential equations, SIAM J. Comparing numerical methods for Fehlberg, E. Klassische Runge-Kutta-Formeln vierter und niedrigerer Ordnumg mit Schrittweiten-Kontrolle und ihre Anwendung auf Warme leitungsprobleme, Computing 6, 61-71.
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.
- 9d ago First seen · 76 lines · 18 tokens per session scan A 7b137b7f9778
first-order-odes is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,938 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 976 once invoked, about $0.0001 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 2026-09-03.
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